Index

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

abortExperiment() - Method in class weka.experiment.RemoteExperiment: Set the abort flag
ABS - Static variable in interface weka.core.mathematicalexpression.sym
ABS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
AbstractAssociator - Class in weka.associations: Abstract scheme for learning associations.
AbstractAssociator() - Constructor for class weka.associations.AbstractAssociator
AbstractClusterer - Class in weka.clusterers: Abstract clusterer.
AbstractClusterer() - Constructor for class weka.clusterers.AbstractClusterer
AbstractDataSink - Class in weka.gui.beans: Abstract class for objects that store instances to some destination.
AbstractDataSink() - Constructor for class weka.gui.beans.AbstractDataSink
AbstractDataSinkBeanInfo - Class in weka.gui.beans: Bean info class for the AbstractDataSink
AbstractDataSinkBeanInfo() - Constructor for class weka.gui.beans.AbstractDataSinkBeanInfo
AbstractDataSource - Class in weka.gui.beans: Abstract class for objects that can provide instances from some source
AbstractDataSource() - Constructor for class weka.gui.beans.AbstractDataSource: Creates a new AbstractDataSource instance.
AbstractDataSourceBeanInfo - Class in weka.gui.beans: Bean info class for AbstractDataSource.
AbstractDataSourceBeanInfo() - Constructor for class weka.gui.beans.AbstractDataSourceBeanInfo
AbstractDensityBasedClusterer - Class in weka.clusterers: Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
AbstractDensityBasedClusterer() - Constructor for class weka.clusterers.AbstractDensityBasedClusterer
AbstractEvaluator - Class in weka.gui.beans: Abstract class for objects that can provide some kind of evaluation for classifier, clusterers etc.
AbstractEvaluator() - Constructor for class weka.gui.beans.AbstractEvaluator: Constructor
AbstractFileLoader - Class in weka.core.converters: Abstract superclass for all file loaders.
AbstractFileLoader() - Constructor for class weka.core.converters.AbstractFileLoader
AbstractFileSaver - Class in weka.core.converters: Abstract class for Savers that save to a file Valid options are: -i input arff file
The input filw in arff format.
AbstractFileSaver() - Constructor for class weka.core.converters.AbstractFileSaver
AbstractLoader - Class in weka.core.converters: Abstract class gives default implementation of setSource methods.
AbstractLoader() - Constructor for class weka.core.converters.AbstractLoader
AbstractSaver - Class in weka.core.converters: Abstract class for Saver
AbstractSaver() - Constructor for class weka.core.converters.AbstractSaver
AbstractStringDistanceFunction - Class in weka.core: Represents the abstract ancestor for string-based distance functions, like EditDistance.
AbstractStringDistanceFunction() - Constructor for class weka.core.AbstractStringDistanceFunction: Constructor that doesn't set the data
AbstractStringDistanceFunction(Instances) - Constructor for class weka.core.AbstractStringDistanceFunction: Constructor that sets the data
AbstractTestSetProducer - Class in weka.gui.beans: Abstract class for TestSetProducers that contains default implementations of add/remove listener methods and defualt visual representation.
AbstractTestSetProducer() - Constructor for class weka.gui.beans.AbstractTestSetProducer: Creates a new AbstractTestSetProducer instance.
AbstractTestSetProducerBeanInfo - Class in weka.gui.beans: BeanInfo class for AbstractTestSetProducer
AbstractTestSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTestSetProducerBeanInfo
AbstractTimeSeries - Class in weka.filters.unsupervised.attribute: An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance.
AbstractTimeSeries() - Constructor for class weka.filters.unsupervised.attribute.AbstractTimeSeries
AbstractTrainAndTestSetProducer - Class in weka.gui.beans: Abstract base class for TrainAndTestSetProducers that contains default implementations of add/remove listener methods and defualt visual representation.
AbstractTrainAndTestSetProducer() - Constructor for class weka.gui.beans.AbstractTrainAndTestSetProducer: Creates a new AbstractTrainAndTestSetProducer instance.
AbstractTrainAndTestSetProducerBeanInfo - Class in weka.gui.beans: Bean info class for AbstractTrainAndTestSetProducers
AbstractTrainAndTestSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
AbstractTrainingSetProducer - Class in weka.gui.beans: Abstract class for TrainingSetProducers that contains default implementations of add/remove listener methods and default visual representation
AbstractTrainingSetProducer() - Constructor for class weka.gui.beans.AbstractTrainingSetProducer: Creates a new AbstractTrainingSetProducer instance.
AbstractTrainingSetProducerBeanInfo - Class in weka.gui.beans: BeanInfo class for AbstractTrainingSetProducer
AbstractTrainingSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTrainingSetProducerBeanInfo
accept(File) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter: Whether the given file is accepted by this filter.
accept(File) - Method in class weka.gui.ExtensionFileFilter: Returns true if the supplied file should be accepted (i.e.: if it has the required extension or is a directory).
accept(File, String) - Method in class weka.gui.ExtensionFileFilter: Returns true if the file in the given directory with the given name should be accepted.
ACCEPT - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent: States that the user has accepted the tree.
acceptClassifier(BatchClassifierEvent) - Method in interface weka.gui.beans.BatchClassifierListener: Accept a BatchClassifierEvent
acceptClassifier(BatchClassifierEvent) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Accept a classifier to be evaluated
acceptClassifier(IncrementalClassifierEvent) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Accepts and processes a classifier encapsulated in an incremental classifier event
acceptClassifier(IncrementalClassifierEvent) - Method in interface weka.gui.beans.IncrementalClassifierListener: Accept the event
acceptClassifier(IncrementalClassifierEvent) - Method in class weka.gui.beans.PredictionAppender: Accept and process an incremental classifier event
acceptClassifier(BatchClassifierEvent) - Method in class weka.gui.beans.PredictionAppender: Accept and process a batch classifier event
acceptClassifier(IncrementalClassifierEvent) - Method in class weka.gui.beans.SerializedModelSaver: Accept and save an incrementally trained classifier.
acceptClassifier(BatchClassifierEvent) - Method in class weka.gui.beans.SerializedModelSaver: Accept and save a batch trained classifier.
acceptClusterer(BatchClustererEvent) - Method in interface weka.gui.beans.BatchClustererListener: Accept a BatchClustererEvent
acceptClusterer(BatchClustererEvent) - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Accept a clusterer to be evaluated
acceptClusterer(BatchClustererEvent) - Method in class weka.gui.beans.PredictionAppender: Accept and process a batch clusterer event
acceptClusterer(BatchClustererEvent) - Method in class weka.gui.beans.SerializedModelSaver: Accept and save a batch trained clusterer.
acceptDataPoint(ChartEvent) - Method in interface weka.gui.beans.ChartListener
acceptDataPoint(ChartEvent) - Method in class weka.gui.beans.StripChart: Accept a data point (encapsulated in a chart event) to plot
acceptDataPoint(double[]) - Method in class weka.gui.beans.StripChart: Accept a data point to plot
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.AbstractDataSink: Accept a data set
acceptDataSet(ThresholdDataEvent) - Method in class weka.gui.beans.AbstractDataSink: Accept a threshold data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Subclass must implement
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.Associator
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.ClassAssigner
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.ClassValuePicker
acceptDataSet(ThresholdDataEvent) - Method in class weka.gui.beans.CostBenefitAnalysis: Accept a threshold data event and set up the visualization.
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker: Accept a data set
acceptDataSet(DataSetEvent) - Method in interface weka.gui.beans.DataSourceListener
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.DataVisualizer: Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.Filter: Accept a data set
acceptDataSet(ThresholdDataEvent) - Method in class weka.gui.beans.ModelPerformanceChart: Display a threshold curve.
acceptDataSet(VisualizableErrorEvent) - Method in class weka.gui.beans.ModelPerformanceChart: Display a scheme error plot.
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.Saver: Method reacts to a dataset event and starts the writing process in batch mode
acceptDataSet(ThresholdDataEvent) - Method in class weka.gui.beans.Saver: Method reacts to a threshold data event ans starts the writing process in batch mode.
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TestSetMaker: Accepts and processes a data set event
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TextViewer: Accept a data set for displaying as text
acceptDataSet(ThresholdDataEvent) - Method in interface weka.gui.beans.ThresholdDataListener
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TrainingSetMaker: Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker: Accept a data set
acceptDataSet(VisualizableErrorEvent) - Method in interface weka.gui.beans.VisualizableErrorListener
acceptGraph(GraphEvent) - Method in interface weka.gui.beans.GraphListener: Describe acceptGraph method here.
acceptGraph(GraphEvent) - Method in class weka.gui.beans.GraphViewer: Accept a graph
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.AbstractDataSink: Accept an instance
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.ClassAssigner
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.Classifier: Accepts an instance for incremental processing.
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.Filter: Accept an instance for processing by StreamableFilters only
acceptInstance(InstanceEvent) - Method in interface weka.gui.beans.InstanceListener: Accept and process an instance event
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Accept an instance to add to the batch.
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.Saver: Methods reacts to instance events and saves instances incrementally.
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.StripChart
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.AveragingResultProducer: Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.CSVResultListener: Just prints out each result as it is received.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseResultListener: Submit the result to the appropriate table of the database
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseResultProducer: Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.InstancesResultListener: Collects each instance and adjusts the header information.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.LearningRateResultProducer: Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in interface weka.experiment.ResultListener: Accepts results from a ResultProducer.
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.AbstractDataSink: Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.ClassAssigner
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Classifier: Accepts a test set for a batch trained classifier
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Clusterer: Accepts a test set for a batch trained clusterer
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker: Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.DataVisualizer: Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Filter: Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Saver: Method reacts to a test set event and starts the writing process in batch mode
acceptTestSet(TestSetEvent) - Method in interface weka.gui.beans.TestSetListener: Accept and process a test set event
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.TextViewer: Accept a test set for displaying as text
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.TrainingSetMaker
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker: Accept a test set
acceptText(TextEvent) - Method in interface weka.gui.beans.TextListener: Accept and process a text event
acceptText(TextEvent) - Method in class weka.gui.beans.TextViewer: Accept some text
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.AbstractDataSink: Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Associator: Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.ClassAssigner
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Classifier: Accepts a training set and builds batch classifier
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Clusterer: Accepts a training set and builds batch clusterer
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker: Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.DataVisualizer: Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Filter: Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Saver: Method reacts to a training set event and starts the writing process in batch mode
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.TestSetMaker
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.TextViewer: Accept a training set for displaying as text
acceptTrainingSet(TrainingSetEvent) - Method in interface weka.gui.beans.TrainingSetListener: Accept and process a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker: Accept a training set
accuracy() - Method in class weka.associations.RuleItem: Gets the expected predictive accuracy of a rule
ACCURACY - Static variable in class weka.classifiers.meta.ThresholdSelector: accuracy
actEntropy - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the actual entropy
action_table() - Method in class weka.core.mathematicalexpression.Parser: Access to parse-action table.
action_table() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Access to parse-action table.
actionPerformed(ActionEvent) - Method in class weka.gui.arffviewer.ArffPanel: invoked when an action occurs
actionPerformed(ActionEvent) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: invoked when an action occurs
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.AlgorithmListPanel: Handle actions when buttons get pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.DatasetListPanel: Handle actions when buttons get pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Handles the various button clicking type activities.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.HostListPanel: Handle actions when text is entered into the host field or the delete button is pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.RunPanel: Controls starting and stopping the experiment.
actionPerformed(ActionEvent) - Method in class weka.gui.SimpleCLIPanel: Only gets called when return is pressed in the input area, which starts the command running.
actionPerformed(ActionEvent) - Method in class weka.gui.streams.InstanceLoader
actionPerformed(ActionEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Performs the action associated with the ActionEvent.
actual() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the actual class value.
actual() - Method in class weka.classifiers.evaluation.NumericPrediction: Gets the actual class value.
actual() - Method in interface weka.classifiers.evaluation.Prediction: Gets the actual class value.
actualNumBags() - Method in class weka.classifiers.trees.j48.Distribution: Returns number of non-empty bags of distribution.
actualNumClasses() - Method in class weka.classifiers.trees.j48.Distribution: Returns number of classes actually occuring in distribution.
actualNumClasses(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns number of classes actually occuring in given bag.
acuityTipText() - Method in class weka.clusterers.Cobweb: Returns the tip text for this property
AdaBoostM1 - Class in weka.classifiers.meta: Class for boosting a nominal class classifier using the Adaboost M1 method.
AdaBoostM1() - Constructor for class weka.classifiers.meta.AdaBoostM1: Constructor.
add(Object) - Method in class weka.associations.tertius.SimpleLinkedList
add(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution: Adds given instance to given bag.
add(int, double[]) - Method in class weka.classifiers.trees.j48.Distribution: Adds counts to given bag.
add(double, Object) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue: Adds a new Object to the queue
add(double, Object, String) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue: Adds a new Object to the queue
add(AlgVector) - Method in class weka.core.AlgVector: Returns the sum of this vector with another.
add(Instance) - Method in class weka.core.Instances: Adds one instance to the end of the set.
add(Matrix) - Method in class weka.core.Matrix: Deprecated.

Returns the sum of this matrix with another.
add(String) - Method in class weka.core.Stopwords: adds the given word to the stopword list (is automatically converted to lower case and trimmed)
add(TechnicalInformation) - Method in class weka.core.TechnicalInformation: adds the given information to the list of additional technical informations
add(TechnicalInformation.Type) - Method in class weka.core.TechnicalInformation: Adds an empty technical information with the given type to the list of additional informations and returns the instance.
add(PrintStream) - Method in class weka.core.Tee: adds the given PrintStream to the list of streams, with NO timestamp and NO prefix.
add(PrintStream, boolean) - Method in class weka.core.Tee: adds the given PrintStream to the list of streams, with NO prefix.
add(PrintStream, boolean, String) - Method in class weka.core.Tee: adds the given PrintStream to the list of streams.
add(String) - Method in class weka.core.Trie: Ensures that this collection contains the specified element.
add(String) - Method in class weka.core.Trie.TrieNode: adds the given string to its children (creates children if necessary)
add(String, Method) - Method in class weka.core.xml.MethodHandler: adds the specified method for the property with the given displayname to its internal list.
add(Class, Method) - Method in class weka.core.xml.MethodHandler: adds the specified method for the given class to its internal list.
add(double, double) - Method in class weka.experiment.PairedStats: Add an observed pair of values.
add(double[], double[]) - Method in class weka.experiment.PairedStats: Adds an array of observed pair of values.
add(double) - Method in class weka.experiment.Stats: Adds a value to the observed values
add(double, double) - Method in class weka.experiment.Stats: Adds a value that has been seen n times to the observed values
Add - Class in weka.filters.unsupervised.attribute: An instance filter that adds a new attribute to the dataset.
Add() - Constructor for class weka.filters.unsupervised.attribute.Add
add(int, Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Inserts the specified element at the specified position in this list.
add(String) - Method in class weka.gui.HierarchyPropertyParser: Add the given item of property to the tree
ADD_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
addActionListener(ActionListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Register a listener to be notified when plotting completes
addActionListener(ActionListener) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Add a listener interested in kowing about editor status changes
addActionListener(ActionListener) - Method in class weka.gui.visualize.ClassPanel: Add an action listener that will be notified if the user changes the colour of a label
addActionListener(ActionListener) - Method in class weka.gui.visualize.VisualizePanel: Add a listener for this visualize panel
addAll(SimpleLinkedList) - Method in class weka.associations.tertius.SimpleLinkedList
addAll(Collection) - Method in class weka.core.neighboursearch.covertrees.Stack: Adds all the given elements in the stack.
addAll(Collection<? extends String>) - Method in class weka.core.Trie: Adds all of the elements in the specified collection to this collection
addAllBeansToContainer(JComponent) - Static method in class weka.gui.beans.BeanInstance: Adds all beans to the supplied component
addAllowed(Class, String) - Method in class weka.core.xml.PropertyHandler: adds the given property (display name) to the list of allowed properties for the specified class.
addAndUpdate(Rule) - Method in class weka.classifiers.rules.RuleStats: Add a rule to the ruleset and update the stats
addArc(String, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Add arc between two nodes Distributions are updated by duplication for every value of the parent node.
addArc(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Add arc between two nodes Distributions are updated by duplication for every value of the parent node.
addArc(String, FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Add arc between parent node and each of the nodes in a given list.
addAttributePanelListener(AttributePanelListener) - Method in class weka.gui.visualize.AttributePanel: Add a listener to the list of things listening to this panel
addBatchClassifierListener(BatchClassifierListener) - Method in class weka.gui.beans.Classifier: Add a batch classifier listener
addBatchClustererListener(BatchClustererListener) - Method in class weka.gui.beans.Clusterer: Add a batch clusterer listener
addBean(JComponent) - Method in class weka.gui.beans.BeanInstance: Adds this bean to the global list of beans and to the supplied container.
addBefore(Object) - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
addCancelListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to hook an action listener to the cancel button.
addCapabilities(String, Capabilities) - Static method in class weka.gui.PropertySheetPanel: generates a string from the capapbilities, suitable to add to the help text.
addCapabilitiesFilterListener(Explorer.CapabilitiesFilterChangeListener) - Method in class weka.gui.explorer.Explorer: adds the listener to the list of objects that listen for changes of the CapabilitiesFilter
addChangeListener(ChangeListener) - Method in class weka.gui.arffviewer.ArffPanel: Adds a ChangeListener to the panel
addChangeListener(ChangeListener) - Method in class weka.gui.arffviewer.ArffTable: Adds a ChangeListener to the panel
addChartListener(ChartListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Add a chart listener
addCheckBoxActionListener(ActionListener) - Method in class weka.gui.experiment.DistributeExperimentPanel: Enable objects to listen for changes to the check box
addChild(Splitter, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode: Adds a child to this node.
addChild(Edge) - Method in class weka.gui.treevisualizer.Node: Set the value of children.
addChildClique(MarginCalculator.JunctionTreeNode) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
addChildFrame(Container) - Method in class weka.gui.GUIChooser: adds the given child frame to the list of frames.
addChildFrame(Container) - Method in class weka.gui.Main: adds the given child frame to the list of frames.
AddClassification - Class in weka.filters.supervised.attribute: A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
AddClassification() - Constructor for class weka.filters.supervised.attribute.AddClassification
AddCluster - Class in weka.filters.unsupervised.attribute: A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm.
AddCluster() - Constructor for class weka.filters.unsupervised.attribute.AddCluster
addConnectionListener(ConnectionListener) - Method in class weka.gui.sql.ConnectionPanel: adds the given listener to the list of listeners.
addConnectionListener(ConnectionListener) - Method in class weka.gui.sql.SqlViewer: adds the given listener to the list of listeners.
addCons(int[]) - Method in class weka.associations.PriorEstimation: generates a class association rule out of a given premise.
addCVParameter(String) - Method in class weka.classifiers.meta.CVParameterSelection: Adds a scheme parameter to the list of parameters to be set by cross-validation
addDataFormatListener(DataFormatListener) - Method in class weka.gui.beans.ClassAssigner
addDataFormatListener(DataFormatListener) - Method in class weka.gui.beans.ClassValuePicker
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.AbstractDataSource: Add a listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassAssigner
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassValuePicker
addDataSourceListener(DataSourceListener) - Method in interface weka.gui.beans.DataSource: Add a data source listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.DataVisualizer: Add a listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Filter: Add a data source listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Loader: Add a listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.PredictionAppender: Add a datasource listener
addElement(Literal) - Method in class weka.associations.tertius.LiteralSet: Add a Literal to this set.
addElement(Object) - Method in class weka.core.FastVector: Adds an element to this vector.
addElement(int, int, double) - Method in class weka.core.Matrix: Deprecated.

Add a value to an element.
addElement(double) - Method in class weka.core.matrix.DoubleVector: Adds an element into the vector
addElement(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Adds the specified component to the end of this list.
addErrs(double, double, float) - Static method in class weka.classifiers.trees.j48.Stats: Computes estimated extra error for given total number of instances and error using normal approximation to binomial distribution (and continuity correction).
AddExpression - Class in weka.filters.unsupervised.attribute: An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
AddExpression() - Constructor for class weka.filters.unsupervised.attribute.AddExpression
addFile(String) - Static method in class weka.core.ClassloaderUtil: Add file to CLASSPATH
addFile(File) - Static method in class weka.core.ClassloaderUtil: Add file to CLASSPATH
addFirst(Object) - Method in class weka.associations.tertius.SimpleLinkedList
addGraphListener(GraphListener) - Method in class weka.gui.beans.Associator: Add a graph listener
addGraphListener(GraphListener) - Method in class weka.gui.beans.Classifier: Add a graph listener
addGraphListener(GraphListener) - Method in class weka.gui.beans.Clusterer: Add a graph listener
addHeader(String, String) - Method in class weka.experiment.ResultMatrix: adds the key-value pair to the header
addHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.ConnectionPanel: adds the given listener to the list of listeners.
addHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.QueryPanel: adds the given listener to the list of listeners.
addHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.SqlViewer: adds the given listener to the list of listeners.
AddID - Class in weka.filters.unsupervised.attribute: An instance filter that adds an ID attribute to the dataset.
AddID() - Constructor for class weka.filters.unsupervised.attribute.AddID
addIgnored(String) - Method in class weka.core.xml.PropertyHandler: adds the given display name of a property to the ignore list.
addIgnored(Class, String) - Method in class weka.core.xml.PropertyHandler: adds the given class with the display name of a property to the ignore list.
addIncrementalClassifierListener(IncrementalClassifierListener) - Method in class weka.gui.beans.Classifier: Add an incremental classifier listener
addInstance(Instance) - Method in class weka.clusterers.Cobweb: Deprecated.
updateClusterer(Instance) should be used instead
addInstance(BallNode, Instance) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Adds an instance to the ball tree.
addInstance(BallNode, Instance) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor: Adds an instance to the ball tree.
addInstance(BallNode, Instance) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Adds an instance to the tree.
addInstance(BallNode, Instance) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Adds an instance to the ball tree.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.BallTree: Adds the given instance's info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.CoverTree: Adds the given instance info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.KDTree: Adds one instance to KDTree loosly.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.LinearNNSearch: Adds the given instance info.
addInstanceInfo(Instance) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Adds information from the given instance without modifying the datastructure a lot.
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.AbstractDataSource: Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.ClassAssigner
addInstanceListener(InstanceListener) - Method in interface weka.gui.beans.DataSource: Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.Filter: Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.Loader: Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.PredictionAppender: Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceJoiner
addInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceLoader
addInstanceListener(InstanceListener) - Method in interface weka.gui.streams.InstanceProducer
addInstanceNumberAttribute() - Method in class weka.gui.visualize.PlotData2D: Adds an instance number attribute to the plottable instances,
AddInstanceToBestCluster(Instance) - Method in class weka.clusterers.CLOPE: Add instance to best cluster
addInstWithUnknown(Instances, int) - Method in class weka.classifiers.trees.j48.Distribution: Adds all instances with unknown values for given attribute, weighted according to frequency of instances in each bag.
additional() - Method in class weka.core.TechnicalInformation: returns an enumeration of all the additional technical informations (if there are any)
AdditionalMeasureProducer - Interface in weka.core: Interface to something that can produce measures other than those calculated by evaluation modules.
AdditiveRegression - Class in weka.classifiers.meta: Meta classifier that enhances the performance of a regression base classifier.
AdditiveRegression() - Constructor for class weka.classifiers.meta.AdditiveRegression: Default constructor specifying DecisionStump as the classifier
AdditiveRegression(Classifier) - Constructor for class weka.classifiers.meta.AdditiveRegression: Constructor which takes base classifier as argument.
addLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Method to add a LayoutCompleteEventListener
addLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method adds a LayoutCompleteEventListener to the LayoutEngine.
addLiteral(Literal) - Method in class weka.associations.tertius.Predicate
addMouseListener(MouseListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Adds a mouse listener.
addMouseListenerToHeader(JTable) - Method in class weka.gui.SortedTableModel: Adds a mouselistener to the header: left-click on the header sorts in ascending manner, using shift-left-click in descending manner.
addNode(String, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Add new node to the network, initializing instances, parentsets, distributions.
addNode(String, int, int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Add node to network at a given position, initializing instances, parentsets, distributions.
addNodeValue(int, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Add node value to a node.
AddNoise - Class in weka.filters.unsupervised.attribute: An instance filter that changes a percentage of a given attributes values.
AddNoise() - Constructor for class weka.filters.unsupervised.attribute.AddNoise
addNoise(Instances, int, int, int, boolean) - Method in class weka.filters.unsupervised.attribute.AddNoise: add noise to the dataset a given percentage of the instances are changed in the way, that a set of instances are randomly selected using seed.
addObject(String, Object) - Method in class weka.gui.ResultHistoryPanel: Adds an object to the results list
addOkListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to hook an action listener to the ok button.
addParent(int, Instances) - Method in class weka.classifiers.bayes.net.ParentSet: Add parent to parent set and update internals (specifically the cardinality of the parent set)
addParent(int, int, Instances) - Method in class weka.classifiers.bayes.net.ParentSet: Add parent to parent set at specific location and update internals (specifically the cardinality of the parent set)
addPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D: Add a plot to the list of plots to display
addPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel: Set a new plot to the visualize panel
addPrediction(NominalPrediction) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Includes a prediction in the confusion matrix.
addPredictions(FastVector) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Includes a whole bunch of predictions in the confusion matrix.
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.AbstractDataSource: Add a property change listener to this bean
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.AssociatorCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.BeanVisual: Add a listener for property change events
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassAssignerCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassifierCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassValuePickerCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClustererCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer: Add a property change listener
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.DataVisualizer: Add a property change listener to this bean
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.FilterCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.LoaderCustomizer: Add a property change listener
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.ModelPerformanceChart: Add a property change listener to this bean
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.PredictionAppenderCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.SaverCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.SerializedModelSaverCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.StripChartCustomizer: Add a property change listener
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.TextViewer: Add a property change listener to this bean
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.CostMatrixEditor: Adds an object to the list of those that wish to be informed when the cost matrix changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SetupModePanel: Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SetupPanel: Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SimpleSetupPanel: Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel: Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericArrayEditor: Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericObjectEditor: Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.PropertySheetPanel: Adds a PropertyChangeListener.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SetInstancesPanel: Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SimpleDateFormatEditor: Adds an object to the list of those that wish to be informed when the date format changes.
addPropertyChangeListenersSubFlow(PropertyChangeListener) - Method in class weka.gui.beans.MetaBean
addPSFontReplacement(String, String) - Static method in class weka.gui.visualize.PostscriptGraphics: adds the PS font name to replace and its replacement in the replacement hashtable
addQueryExecuteListener(QueryExecuteListener) - Method in class weka.gui.sql.QueryPanel: adds the given listener to the list of listeners.
addQueryExecuteListener(QueryExecuteListener) - Method in class weka.gui.sql.SqlViewer: adds the given listener to the list of listeners.
addRange(int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution: Adds all instances in given range to given bag.
addReference(Instance) - Method in class weka.classifiers.trees.adtree.ReferenceInstances: Adds one instance reference to the end of the set.
addRelation(Instances) - Method in class weka.core.Attribute: Adds a relation to a relation-valued attribute.
addRemoteExperimentListener(RemoteExperimentListener) - Method in class weka.experiment.RemoteExperiment: Add an object to the list of those interested in recieving update information from the RemoteExperiment
addRemoteExperimentListener(RemoteExperimentListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Add an object to the list of those interested in recieving update information from the RemoteExperiment
addRemoteHost(String) - Method in class weka.experiment.RemoteExperiment: Add a host name to the list of remote hosts
addRenderingHints(Map) - Method in class weka.gui.visualize.PostscriptGraphics
addRepaintNotify(Component) - Method in class weka.gui.visualize.ClassPanel: Adds a component that will need to be repainted if the user changes the colour of a label.
addRepaintNotify(Component) - Method in class weka.gui.visualize.LegendPanel: Adds a component that will need to be repainted if the user changes the colour of a label.
addResult(String, StringBuffer) - Method in class weka.gui.ResultHistoryPanel: Adds a new result to the result list.
addResultChangedListener(ResultChangedListener) - Method in class weka.gui.sql.ResultPanel: adds the given listener to the list of listeners
addResultChangedListener(ResultChangedListener) - Method in class weka.gui.sql.SqlViewer: adds the given listener to the list of listeners.
addStartupListener(StartUpListener) - Static method in class weka.gui.beans.KnowledgeFlowApp: Add a listener to be notified when startup is complete
addStartupListener(StartUpListener) - Static method in class weka.gui.Main: Add a listener to be notified when startup is complete.
addStringValue(String) - Method in class weka.core.Attribute: Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(Attribute, int) - Method in class weka.core.Attribute: Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addTableModelListener(TableModelListener) - Method in class weka.gui.arffviewer.ArffSortedTableModel: adds a listener to the list that is notified each time a change to data model occurs
addTableModelListener(TableModelListener) - Method in class weka.gui.arffviewer.ArffTableModel: adds a listener to the list that is notified each time a change to data model occurs
addTableModelListener(TableModelListener) - Method in class weka.gui.sql.ResultSetTableModel: adds a listener to the list that is notified each time a change to data model occurs.
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTestSetProducer: Add a listener for test sets
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Add a test set listener
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.ClassAssigner
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.Filter: Add a test set listener
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.PredictionAppender: Add a test set listener
addTestSetListener(TestSetListener) - Method in interface weka.gui.beans.TestSetProducer: Add a listener for test set events
addTextListener(TextListener) - Method in class weka.gui.beans.Associator: Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.Classifier: Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.Clusterer: Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.TextViewer: Add a text listener
addThresholdDataListener(ThresholdDataListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Add a threshold data listener
addToList(Object[], double) - Method in class weka.attributeSelection.BestFirst.LinkedList2: adds an element (Link) to the list.
addToList(Object[], double) - Method in class weka.attributeSelection.LFSMethods.LinkedList2: adds an element (Link) to the list.
addTrainingInstance(Instance) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Adds a training instance to the visualization dataset.
addTrainingInstanceFromMouseLocation(int, int, int, double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Adds a training instance to our dataset, based on the coordinates of the mouse on the panel.
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.ClassAssigner
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.Filter: Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.PredictionAppender: Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in interface weka.gui.beans.TrainingSetProducer: Add a training set listener
addUndoPoint() - Method in interface weka.core.Undoable: adds an undo point to the undo history
addUndoPoint() - Method in class weka.gui.arffviewer.ArffPanel: adds the current state of the instances to the undolist
addUndoPoint() - Method in class weka.gui.arffviewer.ArffSortedTableModel: adds an undo point to the undo history
addUndoPoint() - Method in class weka.gui.arffviewer.ArffTableModel: adds an undo point to the undo history, if the undo support is enabled
addUndoPoint() - Method in class weka.gui.explorer.PreprocessPanel: Backs up the current state of the dataset, so the changes can be undone.
addURL(URL) - Static method in class weka.core.ClassloaderUtil: Add URL to CLASSPATH
addValue(double, double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Add a new data value to the current estimator.
addValue(double, double, double) - Method in interface weka.estimators.ConditionalEstimator: Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DDConditionalEstimator: Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.DiscreteEstimator: Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DKConditionalEstimator: Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DNConditionalEstimator: Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.Estimator: Add a new data value to the current estimator.
addValue(double, double) - Method in interface weka.estimators.IncrementalEstimator: Add one value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.KDConditionalEstimator: Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.KernelEstimator: Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.KKConditionalEstimator: Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.MahalanobisEstimator: Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.NDConditionalEstimator: Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.NNConditionalEstimator: Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.NormalEstimator: Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.PoissonEstimator: Add a new data value to the current estimator.
addValues(Instances, int) - Method in class weka.estimators.Estimator: Initialize the estimator with a new dataset.
addValues(Instances, int, double, double, double) - Method in class weka.estimators.Estimator: Initialize the estimator with all values of one attribute of a dataset.
addValues(Instances, int, int, int) - Method in class weka.estimators.Estimator: Initialize the estimator using only the instance of one class.
addValues(Instances, int, int, int, double, double) - Method in class weka.estimators.Estimator: Initialize the estimator using only the instance of one class.
AddValues - Class in weka.filters.unsupervised.attribute: Adds the labels from the given list to an attribute if they are missing.
AddValues() - Constructor for class weka.filters.unsupervised.attribute.AddValues
addVariable(String, String) - Method in class weka.core.Environment: Add a variable to the internal map.
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.AbstractDataSource: Add a vetoable change listener to this bean
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.CostBenefitAnalysis
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.DataVisualizer: Add a vetoable change listener to this bean
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.GraphViewer: Add a vetoable change listener to this bean
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.ModelPerformanceChart: Add a vetoable change listener to this bean
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.TextViewer: Add a vetoable change listener to this bean
addVisualizableErrorListener(VisualizableErrorListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Add a visualizable error listener
addWeights(Instance, double[]) - Method in class weka.classifiers.trees.j48.Distribution: Adds given instance to all bags weighting it according to given weights.
adjustCenter(double) - Method in class weka.gui.treevisualizer.Node: Will increase or decrease the postion of center.
adjustSize(SERObject) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Adjusts the size of this panel in respect of the shown content
adjustWeightsTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns the tip text for this property
ADNode - Class in weka.classifiers.bayes.net: The ADNode class implements the ADTree datastructure which increases the speed with which sub-contingency tables can be constructed from a data set in an Instances object.
ADNode() - Constructor for class weka.classifiers.bayes.net.ADNode: Creates new ADNode
ADTree - Class in weka.classifiers.trees: Class for generating an alternating decision tree.
ADTree() - Constructor for class weka.classifiers.trees.ADTree
advanceCounters() - Method in class weka.experiment.Experiment: Increments iteration counters appropriately.
advanceCounters() - Method in class weka.experiment.RemoteExperiment: overides the one in Experiment
Agrawal - Class in weka.datagenerators.classifiers.classification: Generates a people database and is based on the paper by Agrawal et al.:
R.
Agrawal() - Constructor for class weka.datagenerators.classifiers.classification.Agrawal: initializes the generator with default values
AIC - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
ALGORITHM_HAAR - Static variable in class weka.filters.unsupervised.attribute.Wavelet: the type of algorithm: Haar wavelet
ALGORITHM_PLS1 - Static variable in class weka.filters.supervised.attribute.PLSFilter: the type of algorithm: PLS1
ALGORITHM_SIMPLS - Static variable in class weka.filters.supervised.attribute.PLSFilter: the type of algorithm: SIMPLS
AlgorithmListPanel - Class in weka.gui.experiment: This panel controls setting a list of algorithms for an experiment to iterate over.
AlgorithmListPanel(Experiment) - Constructor for class weka.gui.experiment.AlgorithmListPanel: Creates the algorithm list panel with the given experiment.
AlgorithmListPanel() - Constructor for class weka.gui.experiment.AlgorithmListPanel: Create the algorithm list panel initially disabled.
AlgorithmListPanel.ObjectCellRenderer - Class in weka.gui.experiment: Class required to show the Classifiers nicely in the list
algorithmTipText() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns the tip text for this property
algorithmTipText() - Method in class weka.filters.unsupervised.attribute.Wavelet: Returns the tip text for this property
ALGORITHMTYPE_ARITHMETIC - Static variable in class weka.classifiers.mi.MILR: collective MI assumption, arithmetic mean for posteriors
ALGORITHMTYPE_DEFAULT - Static variable in class weka.classifiers.mi.MILR: standard MI assumption
ALGORITHMTYPE_GEOMETRIC - Static variable in class weka.classifiers.mi.MILR: collective MI assumption, geometric mean for posteriors
algorithmTypeTipText() - Method in class weka.classifiers.mi.MILR: Returns the tip text for this property
AlgVector - Class in weka.core: Class for performing operations on an algebraic vector of floating-point values.
AlgVector(int) - Constructor for class weka.core.AlgVector: Constructs a vector and initializes it with default values.
AlgVector(double[]) - Constructor for class weka.core.AlgVector: Constructs a vector using a given array.
AlgVector(Instances, Random) - Constructor for class weka.core.AlgVector: Constructs a vector using a given data format.
AlgVector(Instance) - Constructor for class weka.core.AlgVector: Constructs a vector using an instance.
alignBottom(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: align set of nodes with the bottom most node in the list
alignLeft(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: align set of nodes with the left most node in the list
alignRight(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: align set of nodes with the right most node in the list
alignTop(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: align set of nodes with the top most node in the list
ALL - Static variable in class weka.core.Debug: the log level All
AllFilter - Class in weka.filters: A simple instance filter that passes all instances directly through.
AllFilter() - Constructor for class weka.filters.AllFilter
AllJavadoc - Class in weka.core: Applies all known Javadoc-derived classes to a source file.
AllJavadoc() - Constructor for class weka.core.AllJavadoc
allowed() - Method in class weka.core.xml.PropertyHandler: returns an enumeration of the classnames for which only certain properties (display names) are allowed
allowUnclassifiedInstancesTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property
AlphabeticTokenizer - Class in weka.core.tokenizers: Alphabetic string tokenizer, tokens are to be formed only from contiguous alphabetic sequences.
AlphabeticTokenizer() - Constructor for class weka.core.tokenizers.AlphabeticTokenizer
alphaTipText() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator
alphaTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
amplitudeTipText() - Method in class weka.datagenerators.classifiers.regression.Expression: Returns the tip text for this property
amplitudeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Returns the tip text for this property
and(Capabilities) - Method in class weka.core.Capabilities: performs an AND conjunction with the capabilities of the given Capabilities object and updates itself
AND - Static variable in interface weka.core.mathematicalexpression.sym
AND - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
Antd(Attribute) - Constructor for class weka.classifiers.rules.JRip.Antd: Constructor
AODE - Class in weka.classifiers.bayes: AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
AODE() - Constructor for class weka.classifiers.bayes.AODE
AODEsr - Class in weka.classifiers.bayes: AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see:
Fei Zheng, Geoffrey I.
AODEsr() - Constructor for class weka.classifiers.bayes.AODEsr
append(String, String) - Method in class weka.gui.sql.InfoPanel: adds the given message to the end of the list (with the associated icon at the beginning)
append(Object) - Method in class weka.gui.sql.InfoPanel: adds the given message to the end of the list
appendElements(FastVector) - Method in class weka.core.FastVector: Appends all elements of the supplied vector to this vector.
appendPredictedProbabilitiesTipText() - Method in class weka.gui.beans.PredictionAppender: Return a tip text suitable for displaying in a GUI
applyClassifier(PMMLModel, Instances) - Static method in class weka.core.pmml.PMMLFactory
applyCostMatrix(Instances, Random) - Method in class weka.classifiers.CostMatrix: Applies the cost matrix to a set of instances.
applyMinMaxRescaleCast(double) - Method in class weka.core.pmml.TargetMetaInfo: Apply min and max, rescaleFactor, rescaleConstant and castInteger - in that order (where defined).
applyMissingAndOutlierTreatments(double[]) - Method in class weka.core.pmml.MiningSchema: Apply both missing and outlier treatments to an incoming instance.
applyMissingValuesTreatment(double[]) - Method in class weka.core.pmml.MiningSchema: Apply the missing value treatments (if any) to an incoming instance.
applyMissingValueTreatment(double) - Method in class weka.core.pmml.MiningFieldMetaInfo: Apply the missing value treatment method for this field.
applyOutlierTreatment(double) - Method in class weka.core.pmml.MiningFieldMetaInfo: Apply the outlier treatment method for this field.
applyOutlierTreatment(double[]) - Method in class weka.core.pmml.MiningSchema: Apply the outlier treatment methods (if any) to an incoming instance.
APPROVE_OPTION - Static variable in class weka.gui.experiment.OutputFormatDialog: Signifies an OK property selection.
APPROVE_OPTION - Static variable in class weka.gui.ListSelectorDialog: Signifies an OK property selection
APPROVE_OPTION - Static variable in class weka.gui.PropertySelectorDialog: Signifies an OK property selection
APPROVE_OPTION - Static variable in class weka.gui.ViewerDialog: Signifies an OK property selection
Apriori - Class in weka.associations: Class implementing an Apriori-type algorithm.
Apriori() - Constructor for class weka.associations.Apriori: Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.
aprioriGen(FastVector) - Static method in class weka.associations.gsp.Sequence: Generates all possible candidate k-Sequences and prunes the ones that contain an infrequent (k-1)-Sequence.
AprioriItemSet - Class in weka.associations: Class for storing a set of items.
AprioriItemSet(int) - Constructor for class weka.associations.AprioriItemSet: Constructor
areaUnderROC(int) - Method in class weka.classifiers.Evaluation: Returns the area under ROC for those predictions that have been collected in the evaluateClassifier(Classifier, Instances) method.
ARFF_ATTRIBUTE - Static variable in class weka.core.Attribute: The keyword used to denote the start of an arff attribute declaration
ARFF_ATTRIBUTE_DATE - Static variable in class weka.core.Attribute: The keyword used to denote a date attribute
ARFF_ATTRIBUTE_INTEGER - Static variable in class weka.core.Attribute: A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_NUMERIC - Static variable in class weka.core.Attribute: A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_REAL - Static variable in class weka.core.Attribute: A keyword used to denote a numeric attribute
ARFF_ATTRIBUTE_RELATIONAL - Static variable in class weka.core.Attribute: The keyword used to denote a relation-valued attribute
ARFF_ATTRIBUTE_STRING - Static variable in class weka.core.Attribute: The keyword used to denote a string attribute
ARFF_DATA - Static variable in class weka.core.Instances: The keyword used to denote the start of the arff data section
ARFF_END_SUBRELATION - Static variable in class weka.core.Attribute: The keyword used to denote the end of the declaration of a subrelation
ARFF_RELATION - Static variable in class weka.core.Instances: The keyword used to denote the start of an arff header
ArffLoader - Class in weka.core.converters: Reads a source that is in arff (attribute relation file format) format.
ArffLoader() - Constructor for class weka.core.converters.ArffLoader
ArffLoader.ArffReader - Class in weka.core.converters: Reads data from an ARFF file, either in incremental or batch mode.
ArffPanel - Class in weka.gui.arffviewer: A Panel representing an ARFF-Table and the associated filename.
ArffPanel() - Constructor for class weka.gui.arffviewer.ArffPanel: initializes the panel with no data
ArffPanel(String) - Constructor for class weka.gui.arffviewer.ArffPanel: initializes the panel and loads the specified file
ArffPanel(Instances) - Constructor for class weka.gui.arffviewer.ArffPanel: initializes the panel with the given data
ArffReader(Reader) - Constructor for class weka.core.converters.ArffLoader.ArffReader: Reads the data completely from the reader.
ArffReader(Reader, int) - Constructor for class weka.core.converters.ArffLoader.ArffReader: Reads only the header and reserves the specified space for instances.
ArffReader(Reader, Instances, int) - Constructor for class weka.core.converters.ArffLoader.ArffReader: Reads the data without header according to the specified template.
ArffReader(Reader, Instances, int, int) - Constructor for class weka.core.converters.ArffLoader.ArffReader: Initializes the reader without reading the header according to the specified template.
ArffSaver - Class in weka.core.converters: Writes to a destination in arff text format.
ArffSaver() - Constructor for class weka.core.converters.ArffSaver: Constructor
ArffSortedTableModel - Class in weka.gui.arffviewer: A sorter for the ARFF-Viewer - necessary because of the custom CellRenderer.
ArffSortedTableModel(String) - Constructor for class weka.gui.arffviewer.ArffSortedTableModel: initializes the sorter w/o a model, but loads the given file and creates from that a model
ArffSortedTableModel(Instances) - Constructor for class weka.gui.arffviewer.ArffSortedTableModel: initializes the sorter w/o a model, but uses the given data to create a model from that
ArffSortedTableModel(TableModel) - Constructor for class weka.gui.arffviewer.ArffSortedTableModel: initializes the sorter with the given model
ArffTable - Class in weka.gui.arffviewer: A specialized JTable for the Arff-Viewer.
ArffTable() - Constructor for class weka.gui.arffviewer.ArffTable: initializes with no model
ArffTable(TableModel) - Constructor for class weka.gui.arffviewer.ArffTable: initializes with the given model
ArffTableCellRenderer - Class in weka.gui.arffviewer: Handles the background colors for missing values differently than the DefaultTableCellRenderer.
ArffTableCellRenderer() - Constructor for class weka.gui.arffviewer.ArffTableCellRenderer: initializes the Renderer with a standard color
ArffTableCellRenderer(Color, Color) - Constructor for class weka.gui.arffviewer.ArffTableCellRenderer: initializes the Renderer with the given colors
ArffTableCellRenderer(Color, Color, Color, Color) - Constructor for class weka.gui.arffviewer.ArffTableCellRenderer: initializes the Renderer with the given colors
ArffTableModel - Class in weka.gui.arffviewer: The model for the Arff-Viewer.
ArffTableModel(String) - Constructor for class weka.gui.arffviewer.ArffTableModel: initializes the object and loads the given file
ArffTableModel(Instances) - Constructor for class weka.gui.arffviewer.ArffTableModel: initializes the model with the given data
ArffViewer - Class in weka.gui.arffviewer: A little tool for viewing ARFF files.
ArffViewer() - Constructor for class weka.gui.arffviewer.ArffViewer: initializes the object
ArffViewerMainPanel - Class in weka.gui.arffviewer: The main panel of the ArffViewer.
ArffViewerMainPanel(Container) - Constructor for class weka.gui.arffviewer.ArffViewerMainPanel: initializes the object
arrayLeftDivide(Matrix) - Method in class weka.core.matrix.Matrix: Element-by-element left division, C = A.\B
arrayLeftDivideEquals(Matrix) - Method in class weka.core.matrix.Matrix: Element-by-element left division in place, A = A.\B
arrayRightDivide(Matrix) - Method in class weka.core.matrix.Matrix: Element-by-element right division, C = A./B
arrayRightDivideEquals(Matrix) - Method in class weka.core.matrix.Matrix: Element-by-element right division in place, A = A./B
arrayTimes(Matrix) - Method in class weka.core.matrix.Matrix: Element-by-element multiplication, C = A.*B
arrayTimesEquals(Matrix) - Method in class weka.core.matrix.Matrix: Element-by-element multiplication in place, A = A.*B
arrayToString(Object) - Static method in class weka.core.Utils: Returns the given Array in a string representation.
arrayToString(Object[]) - Static method in class weka.experiment.DatabaseUtils: Converts an array of objects to a string by inserting a space between each element.
artificialSizeTipText() - Method in class weka.classifiers.meta.Decorate: Returns the tip text for this property
ASEvaluation - Class in weka.attributeSelection: Abstract attribute selection evaluation class
ASEvaluation() - Constructor for class weka.attributeSelection.ASEvaluation
ASSearch - Class in weka.attributeSelection: Abstract attribute selection search class.
ASSearch() - Constructor for class weka.attributeSelection.ASSearch
assign(Capabilities) - Method in class weka.core.Capabilities: retrieves the data from the given Capabilities object
assign(TestInstances) - Method in class weka.core.TestInstances: updates itself with all the settings from the given TestInstances object
assign(Tester) - Method in class weka.experiment.PairedTTester: retrieves all the settings from the given Tester
assign(ResultMatrix) - Method in class weka.experiment.ResultMatrix: acquires the data from the given matrix
assign(Tester) - Method in interface weka.experiment.Tester: retrieves all the settings from the given Tester
assignIDs(int) - Method in class weka.classifiers.trees.ft.FTtree: Assigns unique IDs to all nodes in the tree
assignIDs(int) - Method in class weka.classifiers.trees.j48.ClassifierTree: Assigns a uniqe id to every node in the tree.
assignIDs(int) - Method in class weka.classifiers.trees.lmt.LMTNode: Assigns unique IDs to all nodes in the tree
assignLeafModelNumbers(int) - Method in class weka.classifiers.trees.ft.FTtree: Assigns numbers to the logistic regression models at the leaves of the tree
assignLeafModelNumbers(int) - Method in class weka.classifiers.trees.lmt.LMTNode: Assigns numbers to the logistic regression models at the leaves of the tree
assignSubToCenters(KDTreeNode, Instances, int[], int[]) - Method in class weka.core.neighboursearch.KDTree: Assigns instances of this node to center.
associatedConnections(Vector) - Static method in class weka.gui.beans.BeanConnection: Returns a vector of BeanConnections associated with the supplied vector of BeanInstances, i.e.
AssociationRule(Collection<FPGrowth.BinaryItem>, Collection<FPGrowth.BinaryItem>, FPGrowth.AssociationRule.METRIC_TYPE, int, int, int, int) - Constructor for class weka.associations.FPGrowth.AssociationRule: Construct a new association rule.
AssociationsPanel - Class in weka.gui.explorer: This panel allows the user to select, configure, and run a scheme that learns associations.
AssociationsPanel() - Constructor for class weka.gui.explorer.AssociationsPanel: Creates the associator panel
Associator - Interface in weka.associations
Associator - Class in weka.gui.beans: Bean that wraps around weka.associations
Associator() - Constructor for class weka.gui.beans.Associator: Creates a new Associator instance.
AssociatorBeanInfo - Class in weka.gui.beans: BeanInfo class for the Associator wrapper bean
AssociatorBeanInfo() - Constructor for class weka.gui.beans.AssociatorBeanInfo
AssociatorCustomizer - Class in weka.gui.beans: GUI customizer for the associator wrapper bean
AssociatorCustomizer() - Constructor for class weka.gui.beans.AssociatorCustomizer
AssociatorEvaluation - Class in weka.associations: Class for evaluating Associaters.
AssociatorEvaluation() - Constructor for class weka.associations.AssociatorEvaluation: default constructor
associatorTipText() - Method in class weka.associations.SingleAssociatorEnhancer: Returns the tip text for this property
aSubsumesB(RuleItem, RuleItem) - Static method in class weka.associations.CaRuleGeneration: Methods that decides whether or not rule a subsumes rule b.
aSubsumesB(RuleItem, RuleItem) - Static method in class weka.associations.RuleGeneration: Methods that decides whether or not rule a subsumes rule b.
ATT_ARRAY - Static variable in class weka.core.xml.XMLSerialization: the tag whether array or not (yes/no)
ATT_ARRAY_DEFAULT - Static variable in class weka.core.xml.XMLSerialization: default value for attribute ATT_ARRAY
ATT_CLASS - Static variable in class weka.core.xml.XMLInstances: the class attribute
ATT_CLASS - Static variable in class weka.core.xml.XMLSerialization: the tag for the class
ATT_FORMAT - Static variable in class weka.core.xml.XMLInstances: the format attribute (for date attributes)
ATT_INDEX - Static variable in class weka.core.xml.XMLInstances: the index attribute
ATT_MISSING - Static variable in class weka.core.xml.XMLInstances: the missing attribute
ATT_NAME - Static variable in class weka.core.xml.XMLDocument: the "name" attribute.
ATT_NAME - Static variable in class weka.core.xml.XMLOptions: the name attribute.
ATT_NAME - Static variable in class weka.core.xml.XMLSerialization: the tag for the name
ATT_NULL - Static variable in class weka.core.xml.XMLSerialization: the tag whether null or not (yes/no)
ATT_NULL_DEFAULT - Static variable in class weka.core.xml.XMLSerialization: default value for attribute ATT_NULL
ATT_PRIMITIVE - Static variable in class weka.core.xml.XMLSerialization: the tag whether primitive or not (yes/no)
ATT_PRIMITIVE_DEFAULT - Static variable in class weka.core.xml.XMLSerialization: default value for attribute ATT_PRIMITIVE
ATT_TYPE - Static variable in class weka.core.xml.XMLInstances: the type attribute
ATT_TYPE - Static variable in class weka.core.xml.XMLOptions: the type attribute.
ATT_VALUE - Static variable in class weka.core.xml.XMLOptions: the value attribute.
ATT_VERSION - Static variable in class weka.core.xml.XMLDocument: the "version" attribute.
ATT_VERSION - Static variable in class weka.core.xml.XMLInstances: the version attribute
ATT_VERSION - Static variable in class weka.core.xml.XMLSerialization: the version attribute
ATT_WEIGHT - Static variable in class weka.core.xml.XMLInstances: the weight attribute
attIndex() - Method in class weka.classifiers.trees.j48.BinC45Split: Returns index of attribute for which split was generated.
attIndex() - Method in class weka.classifiers.trees.j48.C45Split: Returns index of attribute for which split was generated.
attIndex() - Method in class weka.classifiers.trees.j48.NBTreeSplit: Returns index of attribute for which split was generated.
attList_IrrTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the tip text for this property
attribute() - Method in class weka.classifiers.trees.j48.GraftSplit
Attribute - Class in weka.core: Class for handling an attribute.
Attribute(String) - Constructor for class weka.core.Attribute: Constructor for a numeric attribute.
Attribute(String, ProtectedProperties) - Constructor for class weka.core.Attribute: Constructor for a numeric attribute, where metadata is supplied.
Attribute(String, String) - Constructor for class weka.core.Attribute: Constructor for a date attribute.
Attribute(String, String, ProtectedProperties) - Constructor for class weka.core.Attribute: Constructor for a date attribute, where metadata is supplied.
Attribute(String, FastVector) - Constructor for class weka.core.Attribute: Constructor for nominal attributes and string attributes.
Attribute(String, FastVector, ProtectedProperties) - Constructor for class weka.core.Attribute: Constructor for nominal attributes and string attributes, where metadata is supplied.
Attribute(String, Instances) - Constructor for class weka.core.Attribute: Constructor for relation-valued attributes.
Attribute(String, Instances, ProtectedProperties) - Constructor for class weka.core.Attribute: Constructor for relation-valued attributes.
Attribute(String, int) - Constructor for class weka.core.Attribute: Constructor for a numeric attribute with a particular index.
Attribute(String, String, int) - Constructor for class weka.core.Attribute: Constructor for date attributes with a particular index.
Attribute(String, FastVector, int) - Constructor for class weka.core.Attribute: Constructor for nominal attributes and string attributes with a particular index.
Attribute(String, Instances, int) - Constructor for class weka.core.Attribute: Constructor for a relation-valued attribute with a particular index.
attribute(int) - Method in class weka.core.Instance: Returns the attribute with the given index.
attribute(int) - Method in class weka.core.Instances: Returns an attribute.
attribute(String) - Method in class weka.core.Instances: Returns an attribute given its name.
ATTRIBUTE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
attributeAsClass() - Method in class weka.gui.arffviewer.ArffPanel: sets the current attribute as class attribute, i.e.
attributeAsClass() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: sets the current selected Attribute as class attribute, i.e.
attributeAsClassAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: sets the attribute at the given col index as the new class attribute
attributeAsClassAt(int) - Method in class weka.gui.arffviewer.ArffTableModel: sets the attribute at the given col index as the new class attribute, i.e.
AttributeEvaluator - Interface in weka.attributeSelection: Interface for classes that evaluate attributes individually.
attributeEvaluatorTipText() - Method in class weka.attributeSelection.FilteredAttributeEval: Returns the tip text for this property
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RankSearch: Returns the tip text for this property
AttributeExpression - Class in weka.core: A general purpose class for parsing mathematical expressions involving attribute values.
AttributeExpression() - Constructor for class weka.core.AttributeExpression
attributeIndexesTipText() - Method in class weka.filters.unsupervised.attribute.NominalToString: Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.Add: Returns the tip text for this property.
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.AddValues: Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
attributeIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.core.NormalizableDistance: Returns the tip text for this property.
attributeIndicesTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Copy: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Remove: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Reorder: Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
attributeList(BitSet) - Method in class weka.attributeSelection.ScatterSearchV1: converts a BitSet into a list of attribute indexes
AttributeListPanel - Class in weka.gui: Creates a panel that displays the attributes contained in a set of instances, letting the user select a single attribute for inspection.
AttributeListPanel() - Constructor for class weka.gui.AttributeListPanel: Creates the attribute selection panel with no initial instances.
AttributeLocator - Class in weka.core: This class locates and records the indices of a certain type of attributes, recursively in case of Relational attributes.
AttributeLocator(Instances, int) - Constructor for class weka.core.AttributeLocator: Initializes the AttributeLocator with the given data for the specified type of attribute.
AttributeLocator(Instances, int, int, int) - Constructor for class weka.core.AttributeLocator: Initializes the AttributeLocator with the given data for the specified type of attribute.
AttributeLocator(Instances, int, int[]) - Constructor for class weka.core.AttributeLocator: initializes the AttributeLocator with the given data for the specified type of attribute.
attributeNamePrefixTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
attributeNames() - Method in class weka.classifiers.functions.SMO: Returns the attribute names.
attributeNames() - Method in class weka.classifiers.mi.MISMO: Returns the attribute names.
attributeNameTipText() - Method in class weka.filters.unsupervised.attribute.Add: Returns the tip text for this property.
attributeNameTipText() - Method in class weka.filters.unsupervised.attribute.AddID: Returns the tip text for this property
AttributePanel - Class in weka.gui.visualize: This panel displays one dimensional views of the attributes in a dataset.
AttributePanel() - Constructor for class weka.gui.visualize.AttributePanel
AttributePanel(Color) - Constructor for class weka.gui.visualize.AttributePanel: This constructs an attributePanel.
AttributePanelEvent - Class in weka.gui.visualize: Class encapsulating a change in the AttributePanel's selected x and y attributes.
AttributePanelEvent(boolean, boolean, int) - Constructor for class weka.gui.visualize.AttributePanelEvent: Constructor
AttributePanelListener - Interface in weka.gui.visualize: Interface for classes that want to listen for Attribute selection changes in the attribute panel
attributeRangeTipText() - Method in class weka.filters.unsupervised.attribute.StringToNominal
AttributeSelectedClassifier - Class in weka.classifiers.meta: Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
AttributeSelectedClassifier() - Constructor for class weka.classifiers.meta.AttributeSelectedClassifier: Default constructor.
AttributeSelection - Class in weka.attributeSelection: Attribute selection class.
AttributeSelection() - Constructor for class weka.attributeSelection.AttributeSelection: constructor.
AttributeSelection - Class in weka.filters.supervised.attribute: A supervised attribute filter that can be used to select attributes.
AttributeSelection() - Constructor for class weka.filters.supervised.attribute.AttributeSelection: Constructor
attributeSelectionChange(AttributePanelEvent) - Method in interface weka.gui.visualize.AttributePanelListener: Called when the user clicks on an attribute bar
attributeSelectionMethodTipText() - Method in class weka.classifiers.functions.LinearRegression: Returns the tip text for this property
AttributeSelectionPanel - Class in weka.gui: Creates a panel that displays the attributes contained in a set of instances, letting the user toggle whether each attribute is selected or not (eg: so that unselected attributes can be removed before classification).
AttributeSelectionPanel() - Constructor for class weka.gui.AttributeSelectionPanel: Creates the attribute selection panel with no initial instances.
AttributeSelectionPanel(boolean, boolean, boolean, boolean) - Constructor for class weka.gui.AttributeSelectionPanel: Creates the attribute selection panel with no initial instances.
AttributeSelectionPanel - Class in weka.gui.explorer: This panel allows the user to select and configure an attribute evaluator and a search method, set the attribute of the current dataset to be used as the class, and perform attribute selection using one of two selection modes (select using all the training data or perform a n-fold cross validation---on each trial selecting features using n-1 folds of the data).
AttributeSelectionPanel() - Constructor for class weka.gui.explorer.AttributeSelectionPanel: Creates the classifier panel
AttributeSetEvaluator - Class in weka.attributeSelection: Abstract attribute set evaluator.
AttributeSetEvaluator() - Constructor for class weka.attributeSelection.AttributeSetEvaluator
attributeSparse(int) - Method in class weka.core.Instance: Returns the attribute with the given index.
attributeSparse(int) - Method in class weka.core.SparseInstance: Returns the attribute associated with the internal index.
AttributeStats - Class in weka.core: A Utility class that contains summary information on an the values that appear in a dataset for a particular attribute.
AttributeStats() - Constructor for class weka.core.AttributeStats
attributeStats(int) - Method in class weka.core.Instances: Calculates summary statistics on the values that appear in this set of instances for a specified attribute.
attributesToString() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Make a string from the attribues list.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.Splitter: Gets the string describing the attributes the split depends on.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the string describing the attributes the split depends on.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the string describing the attributes the split depends on.
AttributeSummarizer - Class in weka.gui.beans: Bean that encapsulates displays bar graph summaries for attributes in a data set.
AttributeSummarizer() - Constructor for class weka.gui.beans.AttributeSummarizer: Creates a new AttributeSummarizer instance.
AttributeSummarizerBeanInfo - Class in weka.gui.beans: Bean info class for the attribute summarizer bean
AttributeSummarizerBeanInfo() - Constructor for class weka.gui.beans.AttributeSummarizerBeanInfo
AttributeSummaryPanel - Class in weka.gui: This panel displays summary statistics about an attribute: name, type number/% of missing/unique values, number of distinct values.
AttributeSummaryPanel() - Constructor for class weka.gui.AttributeSummaryPanel: Creates the instances panel with no initial instances.
attributeToDoubleArray(int) - Method in class weka.core.Instances: Gets the value of all instances in this dataset for a particular attribute.
AttributeTransformer - Interface in weka.attributeSelection: Abstract attribute transformer.
attributeTypeTipText() - Method in class weka.filters.unsupervised.attribute.Add: Returns the tip text for this property
attributeTypeTipText() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns the tip text for this property
attributeTypeToString(int) - Static method in class weka.core.CheckScheme: returns a string representation of the attribute type
AttributeValueLiteral - Class in weka.associations.tertius
AttributeValueLiteral(Predicate, String, int, int, int) - Constructor for class weka.associations.tertius.AttributeValueLiteral
AttributeVisualizationPanel - Class in weka.gui: Creates a panel that shows a visualization of an attribute in a dataset.
AttributeVisualizationPanel() - Constructor for class weka.gui.AttributeVisualizationPanel: Constructor - If used then the class will not show the class selection combo box.
AttributeVisualizationPanel(boolean) - Constructor for class weka.gui.AttributeVisualizationPanel: Constructor.
attrIndexRangeTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns the tip text for this property
attrSplit(int, Instances) - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Finds the best splitting point for an attribute in the instances
attrSplit(int, Instances) - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Finds the best splitting point for an attribute in the instances
attrSplit(int, Instances) - Method in class weka.classifiers.trees.m5.YongSplitInfo: Finds the best splitting point for an attribute in the instances
attsToEliminatePerIterationTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
autoBuildTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
autoKeyGenerationTipText() - Method in class weka.core.converters.DatabaseSaver: Returns the tip text for this property.
AVERAGE_RULE - Static variable in class weka.classifiers.meta.Vote: combination rule: Average of Probabilities
AveragingResultProducer - Class in weka.experiment: Takes the results from a ResultProducer and submits the average to the result listener.
AveragingResultProducer() - Constructor for class weka.experiment.AveragingResultProducer
avgCost() - Method in class weka.classifiers.Evaluation: Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
avgProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the average transformation probability

B

B_ENTROPY - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
B_SPHERE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants: Blend setting modes
BackgroundDesktopPane(String) - Constructor for class weka.gui.Main.BackgroundDesktopPane: intializes the desktop pane.
backQuoteChars(String) - Static method in class weka.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
backward(PaceMatrix, IntVector, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Backward ordering of columns in terms of response explanation.
Bagging - Class in weka.classifiers.meta: Class for bagging a classifier to reduce variance.
Bagging() - Constructor for class weka.classifiers.meta.Bagging: Constructor.
bagSizePercentTipText() - Method in class weka.classifiers.meta.Bagging: Returns the tip text for this property
bagSizePercentTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
balanceClassTipText() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Returns the tip text for this property
balancedTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
BallNode - Class in weka.core.neighboursearch.balltrees: Class representing a node of a BallTree.
BallNode(int) - Constructor for class weka.core.neighboursearch.balltrees.BallNode: Constructor.
BallNode(int, int, int) - Constructor for class weka.core.neighboursearch.balltrees.BallNode: Creates a new instance of BallNode.
BallNode(int, int, int, Instance, double) - Constructor for class weka.core.neighboursearch.balltrees.BallNode: Creates a new instance of BallNode.
BallSplitter - Class in weka.core.neighboursearch.balltrees: Abstract class for splitting a ball tree's BallNode.
BallSplitter() - Constructor for class weka.core.neighboursearch.balltrees.BallSplitter: default constructor.
BallSplitter(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.BallSplitter: Creates a new instance of BallSplitter.
ballSplitterTipText() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Returns the tip text for this property.
BallTree - Class in weka.core.neighboursearch: Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search.
The connection to dataset is only a reference.
BallTree() - Constructor for class weka.core.neighboursearch.BallTree: Creates a new instance of BallTree.
BallTree(Instances) - Constructor for class weka.core.neighboursearch.BallTree: Creates a new instance of BallTree.
BallTreeConstructor - Class in weka.core.neighboursearch.balltrees: Abstract class for constructing a BallTree .
BallTreeConstructor() - Constructor for class weka.core.neighboursearch.balltrees.BallTreeConstructor: Creates a new instance of BallTreeConstructor.
ballTreeConstructorTipText() - Method in class weka.core.neighboursearch.BallTree: Returns the tip text for this property.
baseTipText() - Method in class weka.core.neighboursearch.CoverTree: Returns the tip text for this property.
BATCH - Static variable in interface weka.core.converters.Loader
BATCH - Static variable in interface weka.core.converters.Saver
BATCH_FINISHED - Static variable in class weka.gui.beans.IncrementalClassifierEvent
BATCH_FINISHED - Static variable in class weka.gui.beans.InstanceEvent
BATCH_FINISHED - Static variable in class weka.gui.streams.InstanceEvent: Specifies that the batch of instances is finished
BatchClassifierEvent - Class in weka.gui.beans: Class encapsulating a built classifier and a batch of instances to test on.
BatchClassifierEvent(Object, Classifier, DataSetEvent, DataSetEvent, int, int) - Constructor for class weka.gui.beans.BatchClassifierEvent: Creates a new BatchClassifierEvent instance.
BatchClassifierEvent(Object, Classifier, DataSetEvent, DataSetEvent, int, int, int, int) - Constructor for class weka.gui.beans.BatchClassifierEvent: Creates a new BatchClassifierEvent instance.
BatchClassifierListener - Interface in weka.gui.beans: Interface to something that can process a BatchClassifierEvent
BatchClustererEvent - Class in weka.gui.beans: Class encapsulating a built clusterer and a batch of instances to test on.
BatchClustererEvent(Object, Clusterer, DataSetEvent, int, int, int) - Constructor for class weka.gui.beans.BatchClustererEvent: Creates a new BatchClustererEvent instance.
BatchClustererListener - Interface in weka.gui.beans: Interface to something that can process a BatchClustererEvent
BatchConverter - Interface in weka.core.converters: Marker interface for a loader/saver that can retrieve instances in batch mode
batchFilterFile(Filter, String[]) - Static method in class weka.filters.Filter: Method for testing filters ability to process multiple batches.
batchFinished() - Method in class weka.filters.Filter: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.MultiFilter: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.SimpleBatchFilter: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.SimpleStreamFilter: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.AttributeSelection: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.ClassOrder: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.Discretize: Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.NominalToBinary: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.Resample: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.SMOTE: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.SpreadSubsample: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AddCluster: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AddID: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AddNoise: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Center: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Discretize: Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.MathExpression: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.NominalToString: Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Normalize: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.RemoveType: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Standardize: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.Randomize: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveRange: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.Resample: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.gui.streams.InstanceJoiner: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.gui.streams.InstanceSavePanel
batchFinished() - Method in class weka.gui.streams.InstanceTable
batchFinished() - Method in class weka.gui.streams.InstanceViewer
BAYES - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable: score types
BayesianLogisticRegression - Class in weka.classifiers.bayes: Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.

For more information, see

Alexander Genkin, David D.
BayesianLogisticRegression() - Constructor for class weka.classifiers.bayes.BayesianLogisticRegression
BayesNet - Class in weka.classifiers.bayes: Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
BayesNet() - Constructor for class weka.classifiers.bayes.BayesNet
BayesNet - Static variable in interface weka.core.Drawable
BayesNet - Class in weka.datagenerators.classifiers.classification: Generates random instances based on a Bayes network.
BayesNet() - Constructor for class weka.datagenerators.classifiers.classification.BayesNet: initializes the generator
BayesNetEstimator - Class in weka.classifiers.bayes.net.estimate: BayesNetEstimator is the base class for estimating the conditional probability tables of a Bayes network once the structure has been learned.
BayesNetEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.BayesNetEstimator
BayesNetGenerator - Class in weka.classifiers.bayes.net: Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
BayesNetGenerator() - Constructor for class weka.classifiers.bayes.net.BayesNetGenerator: Constructor for BayesNetGenerator.
BDeu - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
BEAN_EXECUTING - Static variable in class weka.gui.beans.BeanInstance
BeanCommon - Interface in weka.gui.beans: Interface specifying routines that all weka beans should implement in order to allow the bean environment to exercise some control over the bean and also to allow the bean to exercise some control over connections.
BeanConnection - Class in weka.gui.beans: Class for encapsulating a connection between two beans.
BeanConnection(BeanInstance, BeanInstance, EventSetDescriptor) - Constructor for class weka.gui.beans.BeanConnection: Creates a new BeanConnection instance.
BeanInstance - Class in weka.gui.beans: Class that manages a set of beans.
BeanInstance(JComponent, Object, int, int) - Constructor for class weka.gui.beans.BeanInstance: Creates a new BeanInstance instance.
BeanInstance(JComponent, String, int, int) - Constructor for class weka.gui.beans.BeanInstance: Creates a new BeanInstance instance given the fully qualified name of the bean
BeanVisual - Class in weka.gui.beans: BeanVisual encapsulates icons and label for a given bean.
BeanVisual(String, String, String) - Constructor for class weka.gui.beans.BeanVisual: Constructor
BestFirst - Class in weka.attributeSelection: BestFirst:

Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
BestFirst() - Constructor for class weka.attributeSelection.BestFirst: Constructor
BestFirst.Link2 - Class in weka.attributeSelection: Class for a node in a linked list.
BestFirst.LinkedList2 - Class in weka.attributeSelection: Class for handling a linked list.
betaTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
BetaVector - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Array for storing coefficients of Bayesian regression model.
BFTree - Class in weka.classifiers.trees: Class for building a best-first decision tree classifier.
BFTree() - Constructor for class weka.classifiers.trees.BFTree
bias() - Method in class weka.classifiers.functions.SMO: Returns the bias of each binary SMO.
bias() - Method in class weka.classifiers.mi.MISMO: Returns the bias of each binary SMO.
biasTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
biasTipText() - Method in class weka.classifiers.misc.VFI: Returns the tip text for this property
biasToUniformClassTipText() - Method in class weka.filters.supervised.instance.Resample: Returns the tip text for this property.
BIBTEX_ENDTAG - Static variable in class weka.core.TechnicalInformationHandlerJavadoc: the end comment tag for inserting the generated BibTex
BIBTEX_STARTTAG - Static variable in class weka.core.TechnicalInformationHandlerJavadoc: the start comment tag for inserting the generated BibTex
BIFFileTipText() - Method in class weka.classifiers.bayes.BayesNet
BIFFormatException - Exception in weka.gui.graphvisualizer: This is the Exception thrown by BIFParser, if there was an error in parsing the XMLBIF string or input stream.
BIFFormatException(String) - Constructor for exception weka.gui.graphvisualizer.BIFFormatException
BIFParser - Class in weka.gui.graphvisualizer: This class parses an inputstream or a string in XMLBIF ver.
BIFParser(String, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.BIFParser: Constructor (if our input is a String)
BIFParser(InputStream, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.BIFParser: Constructor (if our input is an InputStream)
BIFReader - Class in weka.classifiers.bayes.net: Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format.

For more details on XML BIF see:

Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).
BIFReader() - Constructor for class weka.classifiers.bayes.net.BIFReader: the default constructor
bigF(double, double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression: This is a convient function that defines and upper bound (Delta>0) for values of r(i) reachable by updates in the trust region.
binarizeNumericAttributesTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns the tip text for this property
binarizeNumericAttributesTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns the tip text for this property
BINARY - Static variable in class weka.gui.beans.SerializedModelSaver
binaryAttributesNominalTipText() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns the tip text for this property
binaryAttributesNominalTipText() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns the tip text for this property
BinaryItem(Attribute, int) - Constructor for class weka.associations.FPGrowth.BinaryItem
BinarySMO() - Constructor for class weka.classifiers.functions.SMO.BinarySMO
BinarySparseInstance - Class in weka.core: Class for storing a binary-data-only instance as a sparse vector.
BinarySparseInstance(Instance) - Constructor for class weka.core.BinarySparseInstance: Constructor that generates a sparse instance from the given instance.
BinarySparseInstance(SparseInstance) - Constructor for class weka.core.BinarySparseInstance: Constructor that copies the info from the given instance.
BinarySparseInstance(double, double[]) - Constructor for class weka.core.BinarySparseInstance: Constructor that generates a sparse instance from the given parameters.
BinarySparseInstance(double, int[], int) - Constructor for class weka.core.BinarySparseInstance: Constructor that inititalizes instance variable with given values.
BinarySparseInstance(int) - Constructor for class weka.core.BinarySparseInstance: Constructor of an instance that sets weight to one, all values to 1, and the reference to the dataset to null.
binarySplitsTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
binarySplitsTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
binarySplitsTipText() - Method in class weka.classifiers.trees.J48graft: Returns the tip text for this property
binaryToKOML(String, String) - Static method in class weka.core.xml.SerialUIDChanger: converts a binary file into a KOML XML file
BinC45ModelSelection - Class in weka.classifiers.trees.j48: Class for selecting a C4.5-like binary (!) split for a given dataset.
BinC45ModelSelection(int, Instances) - Constructor for class weka.classifiers.trees.j48.BinC45ModelSelection: Initializes the split selection method with the given parameters.
BinC45Split - Class in weka.classifiers.trees.j48: Class implementing a binary C4.5-like split on an attribute.
BinC45Split(int, int, double) - Constructor for class weka.classifiers.trees.j48.BinC45Split: Initializes the split model.
binomialDistribution(double, double, double) - Static method in class weka.associations.RuleGeneration: calculates the probability using a binomial distribution.
binomialStandardError(double, int) - Static method in class weka.core.Statistics: Computes standard error for observed values of a binomial random variable.
binomP(double, double, double) - Method in class weka.classifiers.lazy.LBR: Significance test binomp:
binSplitTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
binsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
binsTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns the tip text for this property
binValueTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
biprob(double, double, double) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Significance test
BIRCHCluster - Class in weka.datagenerators.clusterers: Cluster data generator designed for the BIRCH System

Dataset is generated with instances in K clusters.
Instances are 2-d data points.
Each cluster is characterized by the number of data points in itits radius and its center.
BIRCHCluster() - Constructor for class weka.datagenerators.clusterers.BIRCHCluster: initializes the generator with default values
blocker(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron: A function used to stop the code that called buildclassifier from continuing on before the user has finished the decision tree.
BMAEstimator - Class in weka.classifiers.bayes.net.estimate: BMAEstimator estimates conditional probability tables of a Bayes network using Bayes Model Averaging (BMA).
BMAEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.BMAEstimator
BMPWriter - Class in weka.gui.visualize: This class takes any JComponent and outputs it to a BMP-file.
BMPWriter() - Constructor for class weka.gui.visualize.BMPWriter: initializes the object
BMPWriter(JComponent) - Constructor for class weka.gui.visualize.BMPWriter: initializes the object with the given Component
BMPWriter(JComponent, File) - Constructor for class weka.gui.visualize.BMPWriter: initializes the object with the given Component and filename
Body - Class in weka.associations.tertius: Class representing the body of a rule.
Body() - Constructor for class weka.associations.tertius.Body: Constructor without storing the counter-instances.
Body(Instances) - Constructor for class weka.associations.tertius.Body: Constructor storing the counter-instances.
bodyContains(Literal) - Method in class weka.associations.tertius.Rule: Test if the body of the rule contains a literal.
BOOL - Static variable in class weka.experiment.DatabaseUtils: Type mapping for BOOL used for reading experiment results.
BOOLEAN - Static variable in interface weka.core.mathematicalexpression.sym
BOOLEAN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
booleanColsTipText() - Method in class weka.datagenerators.ClusterGenerator: Returns the tip text for this property
boost() - Method in class weka.classifiers.trees.ADTree: Performs a single boosting iteration, using two-class optimized method.
BottomUpConstructor - Class in weka.core.neighboursearch.balltrees: The class that constructs a ball tree bottom up.
BottomUpConstructor() - Constructor for class weka.core.neighboursearch.balltrees.BottomUpConstructor: Creates a new instance of BottomUpConstructor.
BoundaryPanel - Class in weka.gui.boundaryvisualizer: BoundaryPanel.
BoundaryPanel(int, int) - Constructor for class weka.gui.boundaryvisualizer.BoundaryPanel: Creates a new BoundaryPanel instance.
BoundaryPanelDistributed - Class in weka.gui.boundaryvisualizer: This class extends BoundaryPanel with code for distributing the processing necessary to create a visualization among a list of remote machines.
BoundaryPanelDistributed(int, int) - Constructor for class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Creates a new BoundaryPanelDistributed instance.
BoundaryVisualizer - Class in weka.gui.boundaryvisualizer: BoundaryVisualizer.
BoundaryVisualizer() - Constructor for class weka.gui.boundaryvisualizer.BoundaryVisualizer: Creates a new BoundaryVisualizer instance.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.Splitter: Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the index of the branch that an instance applies to.
BrowserHelper - Class in weka.gui: A little helper class for browser related stuff.
BrowserHelper() - Constructor for class weka.gui.BrowserHelper
bubbleSubsetSort(List<ScatterSearchV1.Subset>) - Method in class weka.attributeSelection.ScatterSearchV1: Sort a List of subsets according to their merits
build(String, String) - Method in class weka.gui.HierarchyPropertyParser: Build a tree from the given property with the given delimitor
buildAssociations(Instances) - Method in class weka.associations.Apriori: Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence.
buildAssociations(Instances) - Method in interface weka.associations.Associator: Generates an associator.
buildAssociations(Instances) - Method in class weka.associations.FilteredAssociator: Build the associator on the filtered data.
buildAssociations(Instances) - Method in class weka.associations.FPGrowth: Method that generates all large item sets with a minimum support, and from these all association rules with a minimum metric (i.e.
buildAssociations(Instances) - Method in class weka.associations.GeneralizedSequentialPatterns: Extracts all sequential patterns out of a given sequential data set and prints out the results.
buildAssociations(Instances) - Method in class weka.associations.PredictiveApriori: Method that generates all large itemsets with a minimum support, and from these all association rules.
buildAssociations(Instances) - Method in class weka.associations.Tertius: Method that launches the search to find the rules with the highest confirmation.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.AODE: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.AODEsr: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: (1) Set the data to the class attribute m_Instances. (2)Call the method initialize() to initialize the values.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.BayesNet: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.DMNBtext: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.HNB: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesSimple: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.WAODE: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.Classifier: Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.GaussianProcesses: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.IsotonicRegression: Builds an isotonic regression model given the supplied training data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.LeastMedSq: Build lms regression
buildClassifier(Instances) - Method in class weka.classifiers.functions.LibLINEAR: builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.LibSVM: builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.LinearRegression: Builds a regression model for the given data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Logistic: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.MultilayerPerceptron: Call this function to build and train a neural network for the training data provided.
buildClassifier(Instances) - Method in class weka.classifiers.functions.PaceRegression: Builds a pace regression model for the given data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.PLSClassifier: builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.RBFNetwork: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.SimpleLinearRegression: Builds a simple linear regression model given the supplied training data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SimpleLogistic: Builds the logistic regression using LogitBoost.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SMO: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SMOreg: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SPegasos: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.supportVector.RegOptimizer: learn SVM parameters from data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.supportVector.RegSMO: learn SVM parameters from data using Smola's SMO algorithm.
buildClassifier(Instances) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: learn SVM parameters from data using Keerthi's SMO algorithm.
buildClassifier(Instances) - Method in class weka.classifiers.functions.VotedPerceptron: Builds the ensemble of perceptrons.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Winnow: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Stump method for building the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.IB1: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.IBk: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.KStar: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.LBR: For lazy learning, building classifier is only to prepare their inputs until classification time.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.LWL: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.AdaBoostM1: Boosting method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.AdditiveRegression: Build the classifier on the supplied data
buildClassifier(Instances) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Build the classifier on the dimensionally reduced data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Bagging: Bagging method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.ClassificationViaClustering: builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.ClassificationViaRegression: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.meta.CVParameterSelection: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Dagging: Bagging method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Decorate: Build Decorate classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.END: Builds the committee of randomizable classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.FilteredClassifier: Build the classifier on the filtered data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.GridSearch: builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.LogitBoost: Builds the boosted classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.MetaCost: Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiBoostAB: Method for building this classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiClassClassifier: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiScheme: Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Builds tree recursively.
buildClassifier(Instances) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Builds tree recursively.
buildClassifier(Instances) - Method in class weka.classifiers.meta.nestedDichotomies.ND: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.OrdinalClassClassifier: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RandomCommittee: Builds the committee of randomizable classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RandomSubSpace: builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RegressionByDiscretization: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RotationForest: builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Stacking: Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.ThresholdSelector: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Vote: Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances) - Method in class weka.classifiers.mi.CitationKNN: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MDD: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIBoost: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIDD: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIEMDD: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MILR: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MINND: As normal Nearest Neighbour algorithm does, it's lazy and simply records the exemplar information (i.e.
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIOptimalBall: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MISMO: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.mi.MISVM: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.MIWrapper: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.mi.SimpleMI: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.misc.HyperPipes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.misc.SerializedClassifier: loads only the serialized classifier
buildClassifier(Instances) - Method in class weka.classifiers.misc.VFI: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Throw an exception - PMML models are pre-built.
buildClassifier(Instances) - Method in class weka.classifiers.rules.ConjunctiveRule: Builds a single rule learner with REP dealing with nominal classes or numeric classes.
buildClassifier(Instances) - Method in class weka.classifiers.rules.DecisionTable: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.DTNB: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.JRip: Builds Ripper in the order of class frequencies.
buildClassifier(Instances) - Method in class weka.classifiers.rules.NNge: Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.OneR: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.PART: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.part.MakeDecList: Builds dec list.
buildClassifier(Instances) - Method in class weka.classifiers.rules.Prism: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.Ridor: Builds a ripple-down manner rule learner.
buildClassifier(Instances) - Method in class weka.classifiers.rules.ZeroR: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.ADTree: Builds a classifier for a set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.BFTree: Method for building a BestFirst decision tree classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.DecisionStump: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.FT: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTInnerNode: Method for building a Functional Inner tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTLeavesNode: Method for building a Functional Leaves tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTNode: Method for building a Functional tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTtree: Method for building a Functional Tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.Id3: Builds Id3 decision tree classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Creates a C4.5-type split on the given data.
buildClassifier(Instances) - Method in class weka.classifiers.trees.J48: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree: Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.C45Split: Creates a C4.5-type split on the given data.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Builds the classifier split model for the given set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree: Method for building a classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.GraftSplit: builds m_graftdistro using the passed data
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree: Method for building a naive bayes classifier tree
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Build the no-split node
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit: Creates a NBTree-type split on the given data.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NoSplit: Creates a "no-split"-split for a given set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.PruneableClassifierTree: Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.J48graft: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.LADTree: Builds a classifier for a set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.LMT: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.lmt.LMTNode: Method for building a logistic model tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.lmt.LogisticBase: Builds the logistic regression model usiing LogitBoost.
buildClassifier(Instances, double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Builds the split.
buildClassifier(Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Method not in use
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.M5Base: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.Rule: Generates a single rule or m5 model tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.RuleNode: Build this node (find an attribute and split point)
buildClassifier(Instances) - Method in class weka.classifiers.trees.NBTree: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomForest: Builds a classifier for a set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomTree: Builds classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.REPTree: Builds classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.SimpleCart: Build the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.UserClassifier: Call this function to build a decision tree for the training data provided.
buildClassifierForNode(ND.NDTree, Instances) - Method in class weka.classifiers.meta.nestedDichotomies.ND: Builds the classifier for one node.
buildClusterer(Instances) - Method in class weka.clusterers.AbstractClusterer: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.CLOPE: Generate Clustering via CLOPE
buildClusterer(Instances) - Method in interface weka.clusterers.Clusterer: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.Cobweb: Builds the clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.DBSCAN: Generate Clustering via DBSCAN
buildClusterer(Instances) - Method in class weka.clusterers.EM: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.FarthestFirst: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.FilteredClusterer: Build the clusterer on the filtered data.
buildClusterer(Instances) - Method in class weka.clusterers.HierarchicalClusterer
buildClusterer(Instances) - Method in class weka.clusterers.MakeDensityBasedClusterer: Builds a clusterer for a set of instances.
buildClusterer(Instances) - Method in class weka.clusterers.OPTICS: Generate Clustering via OPTICS
buildClusterer(Instances) - Method in class weka.clusterers.sIB: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.SimpleKMeans: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.XMeans: Generates the X-Means clusterer.
buildCNN() - Method in class weka.classifiers.mi.CitationKNN: generates all the variables associated to the citation classifier
buildDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.C45PruneableDecList: Builds the partial tree without hold out set.
buildDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.ClassifierDecList: Builds the partial tree without hold out set.
buildDecList(Instances, Instances, boolean) - Method in class weka.classifiers.rules.part.PruneableDecList: Builds the partial tree with hold out set
buildDistribution(double, double) - Method in class weka.associations.PriorEstimation: updates the distribution of the confidence values.
buildEstimator(Estimator, String[], boolean) - Static method in class weka.estimators.Estimator: Build an estimator using the options.
buildEstimator(Estimator, Instances, int, int, int, boolean) - Static method in class weka.estimators.Estimator
buildEvaluator(Instances) - Method in class weka.attributeSelection.ASEvaluation: Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.CfsSubsetEval: Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Initializes a chi-squared attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval: Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ConsistencySubsetEval: Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.FilteredAttributeEval: Initializes a filtered attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.FilteredSubsetEval: Initializes a filtered attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.GainRatioAttributeEval: Initializes a gain ratio attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.InfoGainAttributeEval: Initializes an information gain attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.LatentSemanticAnalysis: Initializes the singular values/vectors and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.OneRAttributeEval: Initializes a OneRAttribute attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.PrincipalComponents: Initializes principal components and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.ReliefFAttributeEval: Initializes a ReliefF attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.SVMAttributeEval: Initializes the evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Initializes a symmetrical uncertainty attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.WrapperSubsetEval: Generates a attribute evaluator.
buildGenerator(Instances) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Build the data generator
buildGenerator(Instances) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Initialize the generator using the supplied instances
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.CachedKernel: builds the kernel with the given data.
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.Kernel: builds the kernel with the given data
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.Puk: builds the kernel with the given data.
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.RBFKernel: builds the kernel with the given data.
buildKernel(Instances) - Method in class weka.classifiers.functions.supportVector.StringKernel: builds the kernel with the given data.
buildKernel(Instances) - Method in class weka.classifiers.mi.supportVector.MIRBFKernel: builds the kernel with the given data.
buildLogisticModelsTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
buildLogisticModelsTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
buildRegressionTreeTipText() - Method in class weka.classifiers.trees.m5.M5Base: Returns the tip text for this property
buildRule(Instances) - Method in class weka.classifiers.rules.part.ClassifierDecList: Method for building a pruned partial tree.
buildRule(Instances, Instances) - Method in class weka.classifiers.rules.part.PruneableDecList: Method for building a pruned partial tree.
buildStructure() - Method in class weka.classifiers.bayes.BayesNet: buildStructure determines the network structure/graph of the network.
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.fixed.FromFile
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.fixed.NaiveBayes
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.TAN: buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: buildStructure determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.TAN: buildStructure determines the network structure/graph of the network using the maximimum weight spanning tree algorithm of Chow and Liu
buildStructure(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm: buildStructure determines the network structure/graph of the network.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTInnerNode: Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTLeavesNode: Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTNode: Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTtree: Abstract method for building the tree structure.
buildTree(Instances, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree: Builds the tree structure.
buildTree(Instances, Instances, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree: Builds the tree structure with hold out set
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.lmt.LMTNode: Method for building the tree structure.
buildTree() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Builds the ball tree.
buildTree() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor: Builds the ball tree bottom up.
buildTree() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Builds a ball tree middle out.
buildTree() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Builds the ball tree top down.
BuiltInArithmetic - Class in weka.core.pmml: Built-in function for +, -, *, /.
BuiltInArithmetic(BuiltInArithmetic.Operator) - Constructor for class weka.core.pmml.BuiltInArithmetic: Construct a new Arithmetic built-in pmml function.
BuiltInMath - Class in weka.core.pmml: Built-in function for min, max, sum, avg, log10, ln, sqrt, abs, exp, pow, threshold, floor, ceil and round.
BuiltInMath(BuiltInMath.MathFunc) - Constructor for class weka.core.pmml.BuiltInMath: Construct a new built-in pmml Math function.
BuiltInString - Class in weka.core.pmml: Built-in function for uppercase, substring and trimblanks.
BVDecompose - Class in weka.classifiers: Class for performing a Bias-Variance decomposition on any classifier using the method specified in:

Ron Kohavi, David H.
BVDecompose() - Constructor for class weka.classifiers.BVDecompose
BVDecomposeSegCVSub - Class in weka.classifiers: This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
The Kohavi and Wolpert definition of bias and variance is specified in (2).
The Webb definition of bias and variance is specified in (3).

Geoffrey I.
BVDecomposeSegCVSub() - Constructor for class weka.classifiers.BVDecomposeSegCVSub
BYTE - Static variable in class weka.experiment.DatabaseUtils: Type mapping for BYTE used for reading experiment results.

C

C45Loader - Class in weka.core.converters: Reads a file that is C45 format.
C45Loader() - Constructor for class weka.core.converters.C45Loader
C45ModelSelection - Class in weka.classifiers.trees.j48: Class for selecting a C4.5-type split for a given dataset.
C45ModelSelection(int, Instances) - Constructor for class weka.classifiers.trees.j48.C45ModelSelection: Initializes the split selection method with the given parameters.
C45PruneableClassifierTree - Class in weka.classifiers.trees.j48: Class for handling a tree structure that can be pruned using C4.5 procedures.
C45PruneableClassifierTree(ModelSelection, boolean, float, boolean, boolean) - Constructor for class weka.classifiers.trees.j48.C45PruneableClassifierTree: Constructor for pruneable tree structure.
C45PruneableClassifierTreeG - Class in weka.classifiers.trees.j48: Class for handling a tree structure that can be pruned using C4.5 procedures and have nodes grafted on.
C45PruneableClassifierTreeG(ModelSelection, boolean, float, boolean, boolean, boolean) - Constructor for class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Constructor for pruneable tree structure.
C45PruneableClassifierTreeG(ModelSelection, Instances, ClassifierSplitModel, boolean, float, boolean, boolean, boolean, boolean) - Constructor for class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Constructor for pruneable tree structure.
C45PruneableDecList - Class in weka.classifiers.rules.part: Class for handling a partial tree structure pruned using C4.5's pruning heuristic.
C45PruneableDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.C45PruneableDecList: Constructor for pruneable tree structure.
C45Saver - Class in weka.core.converters: Writes to a destination that is in the format used by the C4.5 algorithm.
Therefore it outputs a names and a data file.
C45Saver() - Constructor for class weka.core.converters.C45Saver: Constructor
C45Split - Class in weka.classifiers.trees.j48: Class implementing a C4.5-type split on an attribute.
C45Split(int, int, double) - Constructor for class weka.classifiers.trees.j48.C45Split: Initializes the split model.
CachedKernel - Class in weka.classifiers.functions.supportVector: Base class for RBFKernel and PolyKernel that implements a simple LRU.
CachedKernel() - Constructor for class weka.classifiers.functions.supportVector.CachedKernel: default constructor - does nothing.
cacheKeyNameTipText() - Method in class weka.experiment.DatabaseResultListener: Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.supportVector.CachedKernel: Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the tip text for this property
CacheTable(int, float) - Constructor for class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Constructs a new hashtable with a default capacity and load factor.
CacheTable() - Constructor for class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Constructs a new hashtable with a default capacity and load factor.
calcCentroidPivot(int[], Instances) - Static method in class weka.core.neighboursearch.balltrees.BallNode: Calculates the centroid pivot of a node.
calcCentroidPivot(int, int, int[], Instances) - Static method in class weka.core.neighboursearch.balltrees.BallNode: Calculates the centroid pivot of a node.
calcColumnWidth(int) - Method in class weka.gui.JTableHelper: calcs the optimal column width of the given column
calcColumnWidth(JTable, int) - Static method in class weka.gui.JTableHelper: Calculates the optimal width for the column of the given table.
calcFullMargins(BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator
calcGraph(int, int) - Method in class weka.gui.AttributeVisualizationPanel: Recalculates the barplot or histogram to display, required usually when the attribute is changed or the component is resized.
calcHeaderWidth(int) - Method in class weka.gui.JTableHelper: calcs the optimal header width of the given column
calcHeaderWidth(JTable, int) - Static method in class weka.gui.JTableHelper: Calculates the optimal width for the header of the given table.
calcMargins(BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator: Calc marginal distributions of nodes in Bayesian network Note that a connected network is assumed.
calcNodeScore(int) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: Calc Node Score for given parent set
calcOutOfBagTipText() - Method in class weka.classifiers.meta.Bagging: Returns the tip text for this property
calcPivot(BallNode, BallNode, Instances) - Static method in class weka.core.neighboursearch.balltrees.BallNode: Calculates the centroid pivot of a node based on its two child nodes (if merging two nodes).
calcPivot(BottomUpConstructor.TempNode, BottomUpConstructor.TempNode, Instances) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor: Calculates the centroid pivot of a node based on its two child nodes.
calcPivot(MiddleOutConstructor.TempNode, MiddleOutConstructor.TempNode, Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: /** Calculates the centroid pivot of a node based on its two child nodes (if merging two nodes).
calcPivot(MiddleOutConstructor.MyIdxList, MiddleOutConstructor.MyIdxList, Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Calculates the centroid pivot of a node based on the list of points that it contains (tbe two lists of its children are provided).
calcRadius(int[], Instances, Instance, DistanceFunction) - Static method in class weka.core.neighboursearch.balltrees.BallNode: Calculates the radius of node.
calcRadius(int, int, int[], Instances, Instance, DistanceFunction) - Static method in class weka.core.neighboursearch.balltrees.BallNode: Calculates the radius of a node.
calcRadius(BallNode, BallNode, Instance, DistanceFunction) - Static method in class weka.core.neighboursearch.balltrees.BallNode: Calculates the radius of a node based on its two child nodes (if merging two nodes).
calcRadius(BottomUpConstructor.TempNode, BottomUpConstructor.TempNode) - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor: Calculates the radius of a node based on its two child nodes.
calcRadius(MiddleOutConstructor.TempNode, MiddleOutConstructor.TempNode) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Calculates the radius of a node based on its two child nodes (if merging two nodes).
calcRadius(MiddleOutConstructor.MyIdxList, MiddleOutConstructor.MyIdxList, Instance, Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Calculates the radius of a node based on the list of points that it contains (the two lists of its children are provided).
calcScore(BayesNet) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: performCV returns the accuracy calculated using cross validation.
calcScoreWithExtraParent(int, int) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: Calc Node Score With Added Parent
calcScoreWithExtraParent(int, int) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: Calc Node Score With AddedParent
calcScoreWithMissingParent(int, int) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: Calc Node Score With Parent Deleted
calcScoreWithMissingParent(int, int) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: Calc Node Score With Parent Deleted
calcScoreWithReversedParent(int, int) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: Calc Node Score With Arrow reversed
calculateAlphas() - Method in class weka.classifiers.trees.lmt.LMTNode: Updates the alpha field for all nodes.
calculateAlphas() - Method in class weka.classifiers.trees.SimpleCart: Updates the alpha field for all nodes.
calculateConfirmation() - Method in class weka.associations.tertius.Rule: Calculate the confirmation of this rule.
calculateDerived() - Method in class weka.experiment.PairedStats: Calculates the derived statistics (significance etc).
calculateDerived() - Method in class weka.experiment.PairedStatsCorrected: Calculates the derived statistics (significance etc).
calculateDerived() - Method in class weka.experiment.Stats: Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateDistance(Instances) - Method in class weka.classifiers.mi.MIOptimalBall: calculate the distances from each instance in a positive bag to each bag.
calculateOptimistic() - Method in class weka.associations.tertius.Rule: Calculate the optimistic estimate of this rule.
calculatePriorSum(boolean, double) - Method in class weka.associations.PriorEstimation: calculates the numerator and the denominator of the prior equation
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedCorrectedTTester: Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedTTester: Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStatistics(Instance, int, int, int) - Method in interface weka.experiment.Tester: Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStdDevsTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
calculateTreshhold() - Method in class weka.attributeSelection.ScatterSearchV1: Calculate the treshold of a dataSet given an evaluator
canAcceptConnection(Class) - Method in class weka.gui.beans.MetaBean: Checks to see if any of the inputs to this group implements the supplied listener class
cancel() - Method in class weka.core.converters.AbstractFileSaver: Cancels the incremental saving process.
cancel() - Method in class weka.core.converters.AbstractSaver: Cancels the incremental saving process if the write mode is CANCEL.
cancel() - Method in class weka.core.converters.DatabaseSaver: Cancels the incremental saving process and tries to drop the table if the write mode is CANCEL.
CANCEL_OPTION - Static variable in class weka.gui.experiment.OutputFormatDialog: Signifies a cancelled property selection.
CANCEL_OPTION - Static variable in class weka.gui.ListSelectorDialog: Signifies a cancelled property selection
CANCEL_OPTION - Static variable in class weka.gui.PropertySelectorDialog: Signifies a cancelled property selection
CANCEL_OPTION - Static variable in class weka.gui.ViewerDialog: Signifies a cancelled property selection
canKeep(Instance, Literal) - Method in class weka.associations.tertius.Body: Test if an instance can be kept as a counter-instance, if a new literal is added to this body.
canKeep(Instance, Literal) - Method in class weka.associations.tertius.Head: Test if an instance can be kept as a counter-instance, if a new literal is added to this head.
canKeep(Instance, Literal) - Method in class weka.associations.tertius.LiteralSet: Test if an instance can be kept as a counter-instance, given a new literal.
canMoveDown(JList) - Static method in class weka.gui.JListHelper: checks whether the selected items can be moved down
canMoveUp(JList) - Static method in class weka.gui.JListHelper: checks whether the selected items can be moved up
canRedo() - Method in class weka.classifiers.bayes.net.EditableBayesNet: return whether there is something on the undo stack that can be performed
canUndo() - Method in class weka.classifiers.bayes.net.EditableBayesNet: return whether there is something on the undo stack that can be performed
canUndo() - Method in interface weka.core.Undoable: returns whether an undo is possible, i.e.
canUndo() - Method in class weka.gui.arffviewer.ArffPanel: returns whether an undo is possible
canUndo() - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns whether an undo is possible, i.e.
canUndo() - Method in class weka.gui.arffviewer.ArffTableModel: returns whether an undo is possible, i.e.
Capabilities - Class in weka.core: A class that describes the capabilites (e.g., handling certain types of attributes, missing values, types of classes, etc.) of a specific classifier.
Capabilities(CapabilitiesHandler) - Constructor for class weka.core.Capabilities: initializes the capabilities for the given owner
capabilities() - Method in class weka.core.Capabilities: Returns an Iterator over the stored capabilities
Capabilities.Capability - Enum in weka.core: enumeration of all capabilities
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.AssociationsPanel: method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.AttributeSelectionPanel: method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.ClassifierPanel: method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.ClustererPanel: method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in interface weka.gui.explorer.Explorer.CapabilitiesFilterChangeListener: method gets called in case of a change event
capabilitiesFilterChanged(Explorer.CapabilitiesFilterChangeEvent) - Method in class weka.gui.explorer.PreprocessPanel: method gets called in case of a change event
CapabilitiesFilterChangeEvent(Object, Capabilities) - Constructor for class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent: Constructs a GOECapabilitiesFilterChangeEvent object.
CapabilitiesFilterDialog() - Constructor for class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog: creates a dialog to choose Capabilities from.
CapabilitiesHandler - Interface in weka.core: Classes implementing this interface return their capabilities in regards to datasets.
capacity() - Method in class weka.core.FastVector: Returns the capacity of the vector.
capacity() - Method in class weka.core.matrix.DoubleVector: Gets the capacity of the vector.
capacity() - Method in class weka.core.matrix.IntVector: Returns the capacity of the vector
cardinalityTipText() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Returns the tip text for this property
caretUpdate(CaretEvent) - Method in class weka.gui.LogWindow: Called when the caret position is updated.
caretUpdate(CaretEvent) - Method in class weka.gui.sql.ConnectionPanel: Called when the caret position is updated.
caretUpdate(CaretEvent) - Method in class weka.gui.sql.QueryPanel: Called when the caret position is updated.
carTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
carTipText() - Method in class weka.associations.PredictiveApriori: Returns the tip text for this property
CaRuleGeneration - Class in weka.associations: Class implementing the rule generation procedure of the predictive apriori algorithm for class association rules.
CaRuleGeneration(ItemSet) - Constructor for class weka.associations.CaRuleGeneration: Constructor
CARuleMiner - Interface in weka.associations: Interface for learning class association rules.
cat(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Combine two vectors together
cbind(PaceMatrix) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns a new matrix which binds two matrices with columns.
CEIL - Static variable in interface weka.core.mathematicalexpression.sym
CEIL - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
Center - Class in weka.filters.unsupervised.attribute: Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
Center() - Constructor for class weka.filters.unsupervised.attribute.Center
centerDataTipText() - Method in class weka.attributeSelection.PrincipalComponents: Returns the tip text for this property
centerDataTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Returns the tip text for this property
centerHorizontal(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: center set of nodes half way between left and right most node in the list
centerInstances(Instances, int[], double) - Method in class weka.core.neighboursearch.KDTree: Assigns instances to centers using KDTree.
centerVertical(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: center set of nodes half way between top and bottom most node in the list
CfsSubsetEval - Class in weka.attributeSelection: CfsSubsetEval :

Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.

Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.

For more information see:

M.
CfsSubsetEval() - Constructor for class weka.attributeSelection.CfsSubsetEval: Constructor
change() - Method in class weka.associations.RuleGeneration: Gets if the list fo the best rules has been changed
Change - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: This variable is used to keep track of change in the value of delta summation of r(i).
ChangeDateFormat - Class in weka.filters.unsupervised.attribute: Changes the date format used by a date attribute.
ChangeDateFormat() - Constructor for class weka.filters.unsupervised.attribute.ChangeDateFormat
changeLength(double) - Method in class weka.core.AlgVector: Changes the length of a vector.
changeUID(long, long, String, String) - Static method in class weka.core.xml.SerialUIDChanger: changes the oldUID into newUID from the given file (binary/KOML) into the other one (binary/KOML).
CharacterDelimitedTokenizer - Class in weka.core.tokenizers: Abstract superclass for tokenizers that take characters as delimiters.
CharacterDelimitedTokenizer() - Constructor for class weka.core.tokenizers.CharacterDelimitedTokenizer
charSetTipText() - Method in class weka.core.converters.TextDirectoryLoader: the tip text for this property
ChartEvent - Class in weka.gui.beans: Event encapsulating info for plotting a data point on the StripChart
ChartEvent(Object, Vector, double, double, double[], boolean) - Constructor for class weka.gui.beans.ChartEvent: Creates a new ChartEvent instance.
ChartEvent(Object) - Constructor for class weka.gui.beans.ChartEvent: Creates a new ChartEvent instance.
ChartListener - Interface in weka.gui.beans: Interface to something that can process a ChartEvent
ChebyshevDistance - Class in weka.core: Implements the Chebyshev distance.
ChebyshevDistance() - Constructor for class weka.core.ChebyshevDistance: Constructs an Chebyshev Distance object, Instances must be still set.
ChebyshevDistance(Instances) - Constructor for class weka.core.ChebyshevDistance: Constructs an Chebyshev Distance object and automatically initializes the ranges.
check(double) - Method in class weka.classifiers.trees.j48.Distribution: Checks if at least two bags contain a minimum number of instances.
Check - Class in weka.core: Abstract general class for testing in Weka.
Check() - Constructor for class weka.core.Check
CheckAssociator - Class in weka.associations: Class for examining the capabilities and finding problems with associators.
CheckAssociator() - Constructor for class weka.associations.CheckAssociator
CheckAttributeSelection - Class in weka.attributeSelection: Class for examining the capabilities and finding problems with attribute selection schemes.
CheckAttributeSelection() - Constructor for class weka.attributeSelection.CheckAttributeSelection
CheckBoxList - Class in weka.gui: An extended JList that contains CheckBoxes.
CheckBoxList() - Constructor for class weka.gui.CheckBoxList: initializes the list with an empty CheckBoxListModel
CheckBoxList(CheckBoxList.CheckBoxListModel) - Constructor for class weka.gui.CheckBoxList: initializes the list with the given CheckBoxListModel
CheckBoxList.CheckBoxListModel - Class in weka.gui: A specialized model.
CheckBoxList.CheckBoxListRenderer - Class in weka.gui: A specialized CellRenderer for the CheckBoxList
CheckBoxListModel() - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel: initializes the model with no data.
CheckBoxListModel(Object[]) - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel: Constructs a CheckBoxListModel from an array of objects and then applies setModel to it.
CheckBoxListModel(Vector) - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel: Constructs a CheckBoxListModel from a Vector and then applies setModel to it.
CheckBoxListRenderer() - Constructor for class weka.gui.CheckBoxList.CheckBoxListRenderer
checkCanonicalUserOptions() - Method in class weka.core.CheckOptionHandler: checks whether the user-supplied options stay the same after settting, getting and re-setting again
CheckClassifier - Class in weka.classifiers: Class for examining the capabilities and finding problems with classifiers.
CheckClassifier() - Constructor for class weka.classifiers.CheckClassifier
CheckClusterer - Class in weka.clusterers: Class for examining the capabilities and finding problems with clusterers.
CheckClusterer() - Constructor for class weka.clusterers.CheckClusterer: default constructor
checkDefaultOptions() - Method in class weka.core.CheckOptionHandler: checks whether the default options can be processed completely or some invalid options are returned by the getOptions() method.
checkErrorRateTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
CheckEstimator - Class in weka.estimators: Class for examining the capabilities and finding problems with estimators.
CheckEstimator() - Constructor for class weka.estimators.CheckEstimator
CheckEstimator.AttrTypes - Class in weka.estimators: class that contains info about the attribute types the estimator can estimate estimator work on one attribute only
CheckEstimator.EstTypes - Class in weka.estimators: public class that contains info about the chosen attribute type estimator work on one attribute only
CheckEstimator.PostProcessor - Class in weka.estimators: a class for postprocessing the test-data
checkForAttributeType(int) - Method in class weka.core.Instances: Checks for attributes of the given type in the dataset
checkForMissing(Instance, Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if an instance has a missing value.
checkForNominalAttributes(Instances) - Method in class weka.clusterers.XMeans: Checks for nominal attributes in the dataset.
checkForRemainingOptions(String[]) - Static method in class weka.core.Utils: Checks if the given array contains any non-empty options.
checkForStringAttributes() - Method in class weka.core.Instances: Checks for string attributes in the dataset
checkGlobalInfo() - Method in class weka.core.CheckGOE: checks whether the object declares a globalInfo method.
CheckGOE - Class in weka.core: Simple command line checking of classes that are editable in the GOE.
Usage:
CheckGOE -W classname -- test options
Valid options are:
CheckGOE() - Constructor for class weka.core.CheckGOE: default constructor
checkIndicesList(MiddleOutConstructor.MyIdxList, int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Checks whether if the points in an index list are in some specified of the master index array.
checkInstance(Instance) - Method in class weka.core.Instances: Checks if the given instance is compatible with this dataset.
CheckKernel - Class in weka.classifiers.functions.supportVector: Class for examining the capabilities and finding problems with kernels.
CheckKernel() - Constructor for class weka.classifiers.functions.supportVector.CheckKernel
checkListOptions() - Method in class weka.core.CheckOptionHandler: checks whether the listOptions method works
checkModel() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Checks if generated model is valid.
checkModel(int) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Checks if there are at least 2 subsets that contain >= minNumInstances.
CheckOptionHandler - Class in weka.core: Simple command line checking of classes that implement OptionHandler.
Usage:
CheckOptionHandler -W optionHandlerClassName -- test options
Valid options are:
CheckOptionHandler() - Constructor for class weka.core.CheckOptionHandler
checkRemainingOptions() - Method in class weka.core.CheckOptionHandler: checks whether the user-supplied options can be processed completely or some "left-over" options remain
checkResettingOptions() - Method in class weka.core.CheckOptionHandler: checks whether the optionhandler can be re-setted again to default options after the user-supplied options have been set.
CheckScheme - Class in weka.core: Abstract general class for testing schemes in Weka.
CheckScheme() - Constructor for class weka.core.CheckScheme
CheckScheme.PostProcessor - Class in weka.core: a class for postprocessing the test-data
checkSetOptions() - Method in class weka.core.CheckOptionHandler: checks whether the user-supplied options can be processed at all
CheckSource - Class in weka.classifiers: A simple class for checking the source generated from Classifiers implementing the weka.classifiers.Sourcable interface.
CheckSource() - Constructor for class weka.classifiers.CheckSource
CheckSource - Class in weka.filters: A simple class for checking the source generated from Filters implementing the weka.filters.Sourcable interface.
CheckSource() - Constructor for class weka.filters.CheckSource
checkStatus(Object) - Method in interface weka.experiment.Compute: Check on the status of a Task
checkStatus(Object) - Method in class weka.experiment.RemoteEngine: Returns status information on a particular task
checksTurnedOffTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
checksTurnedOffTipText() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns the tip text for this property
checksTurnedOffTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
checksTurnedOffTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns the tip text for this property
checkToolTips() - Method in class weka.core.CheckGOE: checks whether the object declares tip text method for all its properties.
ChildFrameMDI(Main, String) - Constructor for class weka.gui.Main.ChildFrameMDI: constructs a new internal frame that knows about its parent.
ChildFrameSDI(GUIChooser, String) - Constructor for class weka.gui.GUIChooser.ChildFrameSDI: constructs a new internal frame that knows about its parent.
ChildFrameSDI(Main, String) - Constructor for class weka.gui.Main.ChildFrameSDI: constructs a new internal frame that knows about its parent.
children() - Method in class weka.classifiers.trees.adtree.PredictionNode: Enumerates the children of this node.
childrenValues() - Method in class weka.gui.HierarchyPropertyParser: The value in the children nodes.
chisqDistribution - Static variable in class weka.core.matrix.Maths: Distribution type: chi-squared
ChisqMixture - Class in weka.classifiers.functions.pace: Class for manipulating chi-square mixture distributions.
ChisqMixture() - Constructor for class weka.classifiers.functions.pace.ChisqMixture: Contructs an empty ChisqMixture
chiSquared(double[][], boolean) - Static method in class weka.core.ContingencyTables: Returns chi-squared probability for a given matrix.
ChiSquaredAttributeEval - Class in weka.attributeSelection: ChiSquaredAttributeEval :

Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.

Valid options are:
ChiSquaredAttributeEval() - Constructor for class weka.attributeSelection.ChiSquaredAttributeEval: Constructor
chiSquaredProbability(double, double) - Static method in class weka.core.Statistics: Returns chi-squared probability for given value and degrees of freedom.
chiVal(double[][], boolean) - Static method in class weka.core.ContingencyTables: Computes chi-squared statistic for a contingency table.
chol() - Method in class weka.core.matrix.Matrix: Cholesky Decomposition
CholeskyDecomposition - Class in weka.core.matrix: Cholesky Decomposition.
CholeskyDecomposition(Matrix) - Constructor for class weka.core.matrix.CholeskyDecomposition: Cholesky algorithm for symmetric and positive definite matrix.
chooseIndex() - Method in class weka.classifiers.rules.part.ClassifierDecList: Method for choosing a subset to expand.
chooseLastIndex() - Method in class weka.classifiers.rules.part.ClassifierDecList: Choose last index (ie.
CISearchAlgorithm - Class in weka.classifiers.bayes.net.search.ci: The CISearchAlgorithm class supports Bayes net structure search algorithms that are based on conditional independence test (as opposed to for example score based of cross validation based search algorithms).
CISearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm
CitationKNN - Class in weka.classifiers.mi: Modified version of the Citation kNN multi instance classifier.

For more information see:

Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.
CitationKNN() - Constructor for class weka.classifiers.mi.CitationKNN
CLASS_IS_LAST - Static variable in class weka.core.TestInstances: can be used for settting the class attribute index to last
CLASS_PYTHONINERPRETER - Static variable in class weka.core.Jython: the classname of the Python interpreter
CLASS_PYTHONOBJECTINPUTSTREAM - Static variable in class weka.core.Jython: the classname of the Python ObjectInputStream
ClassAssigner - Class in weka.filters.unsupervised.attribute: Filter that can set and unset the class index.
ClassAssigner() - Constructor for class weka.filters.unsupervised.attribute.ClassAssigner
ClassAssigner - Class in weka.gui.beans: Bean that assigns a class attribute to a data set.
ClassAssigner() - Constructor for class weka.gui.beans.ClassAssigner
ClassAssignerBeanInfo - Class in weka.gui.beans: BeanInfo class for the class assigner bean
ClassAssignerBeanInfo() - Constructor for class weka.gui.beans.ClassAssignerBeanInfo
ClassAssignerCustomizer - Class in weka.gui.beans: GUI customizer for the class assigner bean
ClassAssignerCustomizer() - Constructor for class weka.gui.beans.ClassAssignerCustomizer
classAttribute() - Method in class weka.core.Instance: Returns class attribute.
classAttribute() - Method in class weka.core.Instances: Returns the class attribute.
classAttributeNames() - Method in class weka.classifiers.functions.SMO
classAttributeNames() - Method in class weka.classifiers.mi.MISMO: Returns the names of the class attributes.
ClassBalancedND - Class in weka.classifiers.meta.nestedDichotomies: A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure.

For more info, check

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
ClassBalancedND() - Constructor for class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Constructor.
classColumnTipText() - Method in class weka.gui.beans.ClassAssigner: Tool tip text for this property
ClassDiscovery - Class in weka.core: This class is used for discovering classes that implement a certain interface or a derived from a certain class.
ClassDiscovery() - Constructor for class weka.core.ClassDiscovery
ClassDiscovery.StringCompare - Class in weka.core: compares two strings.
classFirst(boolean) - Method in class weka.experiment.Experiment: Sets whether the first attribute is treated as the class for all datasets involved in the experiment.
classFlagTipText() - Method in class weka.datagenerators.ClusterGenerator: Returns the tip text for this property
ClassificationGenerator - Class in weka.datagenerators: Abstract class for data generators for classifiers.
ClassificationGenerator() - Constructor for class weka.datagenerators.ClassificationGenerator: initializes with default values
classificationTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
ClassificationViaClustering - Class in weka.classifiers.meta: A simple meta-classifier that uses a clusterer for classification.
ClassificationViaClustering() - Constructor for class weka.classifiers.meta.ClassificationViaClustering: default constructor
ClassificationViaRegression - Class in weka.classifiers.meta: Class for doing classification using regression methods.
ClassificationViaRegression() - Constructor for class weka.classifiers.meta.ClassificationViaRegression: Default constructor.
Classifier - Class in weka.classifiers: Abstract classifier.
Classifier() - Constructor for class weka.classifiers.Classifier
Classifier - Class in weka.gui.beans: Bean that wraps around weka.classifiers
Classifier() - Constructor for class weka.gui.beans.Classifier: Creates a new Classifier instance.
ClassifierBeanInfo - Class in weka.gui.beans: BeanInfo class for the Classifier wrapper bean
ClassifierBeanInfo() - Constructor for class weka.gui.beans.ClassifierBeanInfo
ClassifierCustomizer - Class in weka.gui.beans: GUI customizer for the classifier wrapper bean
ClassifierCustomizer() - Constructor for class weka.gui.beans.ClassifierCustomizer
ClassifierDecList - Class in weka.classifiers.rules.part: Class for handling a rule (partial tree) for a decision list.
ClassifierDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.ClassifierDecList: Constructor - just calls constructor of class DecList.
ClassifierPanel - Class in weka.gui.explorer: 0* This panel allows the user to select and configure a classifier, set the attribute of the current dataset to be used as the class, and evaluate the classifier using a number of testing modes (test on the training data, train/test on a percentage split, n-fold cross-validation, test on a separate split).
ClassifierPanel() - Constructor for class weka.gui.explorer.ClassifierPanel: Creates the classifier panel
ClassifierPerformanceEvaluator - Class in weka.gui.beans: A bean that evaluates the performance of batch trained classifiers
ClassifierPerformanceEvaluator() - Constructor for class weka.gui.beans.ClassifierPerformanceEvaluator
ClassifierPerformanceEvaluatorBeanInfo - Class in weka.gui.beans: Bean info class for the classifier performance evaluator
ClassifierPerformanceEvaluatorBeanInfo() - Constructor for class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
classifiers() - Method in class weka.classifiers.meta.LogitBoost: Returns the array of classifiers that have been built.
ClassifierSplitEvaluator - Class in weka.experiment: A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
ClassifierSplitEvaluator() - Constructor for class weka.experiment.ClassifierSplitEvaluator: No args constructor.
ClassifierSplitModel - Class in weka.classifiers.trees.j48: Abstract class for classification models that can be used recursively to split the data.
ClassifierSplitModel() - Constructor for class weka.classifiers.trees.j48.ClassifierSplitModel
classifiersTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
classifiersTipText() - Method in class weka.classifiers.MultipleClassifiersCombiner: Returns the tip text for this property
ClassifierSubsetEval - Class in weka.attributeSelection: Classifier subset evaluator:

Evaluates attribute subsets on training data or a seperate hold out testing set.
ClassifierSubsetEval() - Constructor for class weka.attributeSelection.ClassifierSubsetEval
classifierTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the tip text for this property
classifierTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.SingleClassifierEnhancer: Returns the tip text for this property
classifierTipText() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns the tip text for this property
classifierTipText() - Method in class weka.experiment.RegressionSplitEvaluator: Returns the tip text for this property
classifierTipText() - Method in class weka.filters.supervised.attribute.AddClassification: Returns the tip text for this property.
classifierTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
ClassifierTree - Class in weka.classifiers.trees.j48: Class for handling a tree structure used for classification.
ClassifierTree(ModelSelection) - Constructor for class weka.classifiers.trees.j48.ClassifierTree: Constructor.
CLASSIFY_CHILD - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent: Asks for another learning scheme to classify this node.
classifyInstance(Instance) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Classifies the given instance using the Bayesian Logistic Regression function.
classifyInstance(Instance) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.Classifier: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.GaussianProcesses: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.IsotonicRegression: Generate a prediction for the supplied instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LeastMedSq: Classify a given instance using the best generated LinearRegression Classifier.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LinearRegression: Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.PaceRegression: Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.PLSClassifier: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.SimpleLinearRegression: Generate a prediction for the supplied instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.SMOreg: Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.Winnow: Outputs the prediction for the given instance.
classifyInstance(Instance) - Method in class weka.classifiers.lazy.IB1: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.AdditiveRegression: Classify an instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.ClassificationViaClustering: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns a predicted class for the test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.Vote: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.mi.MINND: Use Kullback Leibler distance to find the nearest neighbours of the given exemplar.
classifyInstance(Instance) - Method in class weka.classifiers.rules.NNge: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.OneR: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.PART: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.MakeDecList: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.Prism: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.Ridor: Classify the test instance with the rule learner
classifyInstance(Instance) - Method in class weka.classifiers.rules.ZeroR: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.FT: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.Id3: Classifies a given test instance using the decision tree.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierTree: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.J48: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.J48graft: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.LMT: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.M5Base: Calculates a prediction for an instance using a set of rules or an M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Predicts the class of the supplied instance using the linear model.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.Rule: Calculates a prediction for an instance using this rule or M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.RuleNode: Classify an instance using this node.
classifyInstance(Instance) - Method in class weka.classifiers.trees.NBTree: Classifies an instance.
ClassIndex - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: The class index from the training data
classIndex() - Method in class weka.core.Instance: Returns the class attribute's index.
classIndex() - Method in class weka.core.Instances: Returns the class attribute's index.
classIndexTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
classIndexTipText() - Method in class weka.associations.FilteredAssociator: Returns the tip text for this property
classIndexTipText() - Method in class weka.associations.PredictiveApriori: Returns the tip text for this property
classIndexTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
classIndexTipText() - Method in class weka.core.converters.LibSVMSaver: Returns the tip text for this property
classIndexTipText() - Method in class weka.core.converters.SVMLightSaver: Returns the tip text for this property.
classIndexTipText() - Method in class weka.core.converters.XRFFSaver: Returns the tip text for this property
classIndexTipText() - Method in class weka.filters.unsupervised.attribute.ClassAssigner: Returns the tip text for this property.
classIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
classIsMissing() - Method in class weka.core.Instance: Tests if an instance's class is missing.
ClassloaderUtil - Class in weka.core: Utility class that can add jar files to the classpath dynamically.
ClassloaderUtil() - Constructor for class weka.core.ClassloaderUtil
className(int) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the name of one of the classes.
classNameTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the tip text for this property
ClassOrder - Class in weka.filters.supervised.attribute: Changes the order of the classes so that the class values are no longer of in the order specified in the header.
ClassOrder() - Constructor for class weka.filters.supervised.attribute.ClassOrder
classOrderTipText() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns the tip text for this property
ClassPanel - Class in weka.gui.visualize: This panel displays coloured labels for nominal attributes and a spectrum for numeric attributes.
ClassPanel() - Constructor for class weka.gui.visualize.ClassPanel
ClassPanel(Color) - Constructor for class weka.gui.visualize.ClassPanel
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.BinC45Split: Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.C45Split: Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.GraftSplit: returns the probability for instance for the specified class
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Return the probability for a class value
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.NBTreeSplit: Return the probability for a class value
classProbLaplace(int, Instance, int) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Gets class probability for instance.
classSgn(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression: This class is used to mask the internal class labels.
classValue() - Method in class weka.core.Instance: Returns an instance's class value in internal format.
ClassValuePicker - Class in weka.gui.beans
ClassValuePicker() - Constructor for class weka.gui.beans.ClassValuePicker
ClassValuePickerBeanInfo - Class in weka.gui.beans: BeanInfo class for the class value picker bean
ClassValuePickerBeanInfo() - Constructor for class weka.gui.beans.ClassValuePickerBeanInfo
ClassValuePickerCustomizer - Class in weka.gui.beans
ClassValuePickerCustomizer() - Constructor for class weka.gui.beans.ClassValuePickerCustomizer
classValueTipText() - Method in class weka.filters.supervised.instance.SMOTE: Returns the tip text for this property.
clean() - Method in class weka.attributeSelection.ASEvaluation: Tells the evaluator that the attribute selection process is complete.
clean() - Method in class weka.attributeSelection.CfsSubsetEval
clean() - Method in class weka.attributeSelection.ConsistencySubsetEval
clean() - Method in class weka.attributeSelection.WrapperSubsetEval
clean() - Method in class weka.classifiers.functions.supportVector.CachedKernel: Frees the cache used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.Kernel: Frees the memory used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Frees the cache used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Frees the memory used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.StringKernel: Frees the memory used by the kernel.
clean() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel: Frees the cache used by the kernel.
clean() - Method in interface weka.core.DistanceFunction: Free any references to training instances
clean() - Method in class weka.core.NormalizableDistance: Free any references to training instances
cleanse(Instance) - Method in class weka.classifiers.mi.MINND: Cleanse the given exemplar according to the valid and noise data statistics
cleanup(Instances) - Method in class weka.classifiers.rules.part.ClassifierDecList: Cleanup in order to save memory.
cleanUp() - Method in class weka.classifiers.rules.RuleStats: Frees up memory after classifier has been built.
cleanup() - Method in class weka.classifiers.trees.ft.FTtree: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.j48.BinC45ModelSelection: Sets reference to training data to null.
cleanup() - Method in class weka.classifiers.trees.j48.C45ModelSelection: Sets reference to training data to null.
cleanup(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.j48.NBTreeModelSelection: Sets reference to training data to null.
cleanup() - Method in class weka.classifiers.trees.lmt.LMTNode: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.lmt.LogisticBase: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Method not in use
clear() - Method in class weka.associations.tertius.SimpleLinkedList
clear() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Clears this hashtable so that it contains no keys.
clear() - Method in class weka.classifiers.xml.XMLClassifier: generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.core.neighboursearch.covertrees.Stack: Removes all the elements from the stack.
clear() - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
clear() - Method in class weka.core.Stopwords: removes all stopwords
clear() - Method in class weka.core.Tee: removes all streams and places the default printstream, if any, again in the list.
clear() - Method in class weka.core.Trie: Removes all of the elements from this collection
clear() - Method in class weka.core.xml.MethodHandler: removes all mappings
clear() - Method in class weka.core.xml.XMLBasicSerialization: generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.core.xml.XMLDocument: sets up an empty DOM document, with the current DOCTYPE and root node.
clear() - Method in class weka.core.xml.XMLSerialization: generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.core.xml.XMLSerializationMethodHandler: removes all current methods and adds the methods according to the
clear() - Method in class weka.experiment.ResultMatrix: removes the stored data and the ordering, but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixCSV: removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixGnuPlot: removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixHTML: removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixLatex: removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixPlainText: removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.ResultMatrixSignificance: removes the stored data but retains the dimensions of the matrix
clear() - Method in class weka.experiment.xml.XMLExperiment: generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.gui.beans.xml.XMLBeans: generates internally a new XML document and clears also the IgnoreList and the mappings for the Read/Write-Methods
clear() - Method in class weka.gui.LogWindow: clears the output
clear() - Method in class weka.gui.sql.ConnectionPanel: sets the parameters back to standard.
clear() - Method in class weka.gui.sql.InfoPanel: clears the content of the panel
clear() - Method in class weka.gui.sql.QueryPanel: clears the textarea.
clear() - Method in class weka.gui.sql.ResultPanel: sets the parameters back to standard
clear() - Method in class weka.gui.sql.SqlViewer: calls the clear method of all sub-panels to set back to default values and free up memory.
clearCache() - Static method in class weka.core.ClassDiscovery: clears the cache for class/classnames relation.
clearHeader() - Method in class weka.experiment.ResultMatrix: removes all the header information
clearLayout() - Method in class weka.gui.beans.KnowledgeFlowApp
clearRanking() - Method in class weka.experiment.ResultMatrix: clears the currently stored ranking data
clearRect(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Draw a filled rectangle with the background color.
clearResults() - Method in class weka.gui.ResultHistoryPanel: Removes all of the result buffers from the history.
clearSearch() - Method in class weka.gui.arffviewer.ArffPanel: clears the search, i.e.
clearSearch() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: clears the search, i.e.
clearStatus() - Method in class weka.gui.beans.LogPanel: Clear the status area.
clearSummary() - Method in class weka.experiment.ResultMatrix: clears the current summary data
clearUndo() - Method in interface weka.core.Undoable: removes the undo history
clearUndo() - Method in class weka.gui.arffviewer.ArffPanel: removes the undo history
clearUndo() - Method in class weka.gui.arffviewer.ArffSortedTableModel: removes the undo history
clearUndo() - Method in class weka.gui.arffviewer.ArffTableModel: removes the undo history
clearUndoStack() - Method in class weka.classifiers.bayes.net.EditableBayesNet: remove all actions from the undo stack
clip(Shape) - Method in class weka.gui.visualize.PostscriptGraphics
clipRect(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
Clock() - Constructor for class weka.core.Debug.Clock: automatically starts the clock with FORMAT_SECONDS format and CPU time if available
Clock(int) - Constructor for class weka.core.Debug.Clock: automatically starts the clock with the given output format and CPU time if available
Clock(boolean) - Constructor for class weka.core.Debug.Clock: starts the clock depending on start immediately with the FORMAT_SECONDS output format and CPU time if available
Clock(boolean, int) - Constructor for class weka.core.Debug.Clock: starts the clock depending on start immediately, using CPU time if available
clone() - Method in class weka.associations.gsp.Element: Returns a deep clone of an Element.
clone() - Method in class weka.associations.gsp.Sequence: Returns a deep clone of a Sequence.
clone() - Method in class weka.associations.tertius.LiteralSet: Returns a shallow copy of this set.
clone() - Method in class weka.associations.tertius.Rule: Returns a shallow copy of this rule.
clone() - Method in class weka.attributeSelection.ScatterSearchV1.Subset
clone() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Creates and returns a clone of this object.
clone() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Clones the discrete function
clone() - Method in class weka.classifiers.functions.pace.PaceMatrix: Clone the PaceMatrix object.
clone() - Method in interface weka.classifiers.IterativeClassifier: Performs a deep copy of the classifier, and a reference copy of the training instances (or a deep copy if required).
clone() - Method in class weka.classifiers.trees.ADTree: Creates a clone that is identical to the current tree, but is independent.
clone() - Method in class weka.classifiers.trees.adtree.PredictionNode: Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.Splitter: Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Clones this node.
clone() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Allows to clone a model (shallow copy).
clone() - Method in class weka.classifiers.trees.j48.Distribution: Clones distribution (Deep copy of distribution).
clone() - Method in class weka.core.AlgVector: Creates and returns a clone of this object.
clone() - Method in class weka.core.Capabilities: Creates and returns a copy of this object.
clone() - Method in class weka.core.Matrix: Deprecated.

Creates and returns a clone of this object.
clone() - Method in class weka.core.matrix.DoubleVector: Clones the DoubleVector object.
clone() - Method in class weka.core.matrix.IntVector: Clones the IntVector object.
clone() - Method in class weka.core.matrix.Matrix: Clone the Matrix object.
clone() - Method in class weka.core.PropertyPath.PathElement: returns a clone of the current object
clone() - Method in class weka.core.TestInstances: creates a clone of the current object
clone() - Method in class weka.core.Trie: returns a deep copy of itself
clone() - Method in class weka.core.Trie.TrieNode: creates a deep copy of itself
clone(Estimator) - Static method in class weka.estimators.Estimator: Creates a deep copy of the given estimator using serialization.
CLOPE - Class in weka.clusterers: Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data.
CLOPE() - Constructor for class weka.clusterers.CLOPE: the default constructor
close(ResultSet) - Method in class weka.experiment.DatabaseUtils: closes the ResultSet and the statement that generated the ResultSet to avoid memory leaks in JDBC drivers - in contrast to the JDBC specs, a lot of JDBC drives don't clean up correctly.
close() - Method in class weka.experiment.DatabaseUtils: closes the m_PreparedStatement to avoid memory leaks.
close() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: closes the window, i.e., if the parent is not null and implements the WindowListener interface it calls the windowClosing method
close() - Method in class weka.gui.LogWindow: closes the frame
closeAllFiles() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: closes all open files
closeFile() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: closes the current tab
closeFile(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: closes the current tab
closeFrame() - Method in class weka.gui.SetInstancesPanel: closes the frame, i.e., the visibility is set to false
closestPoint(Instance, Instances, int[]) - Method in class weka.core.EuclideanDistance: Returns the index of the closest point to the current instance.
closeToDefaultTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
closeToTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
closeToToleranceTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
ClusterDefinition - Class in weka.datagenerators: Ancestor to all ClusterDefinitions, i.e., subclasses that handle their own parameters that the cluster generator only passes on.
ClusterDefinition() - Constructor for class weka.datagenerators.ClusterDefinition: initializes the cluster, without a parent cluster (necessary for GOE)
ClusterDefinition(ClusterGenerator) - Constructor for class weka.datagenerators.ClusterDefinition: initializes the cluster
clusterDefinitionsTipText() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Returns the tip text for this property
Clusterer - Interface in weka.clusterers: Interface for clusterers.
Clusterer - Class in weka.gui.beans: Bean that wraps around weka.clusterers
Clusterer() - Constructor for class weka.gui.beans.Clusterer: Creates a new Clusterer instance.
ClustererBeanInfo - Class in weka.gui.beans: BeanInfo class for the Clusterer wrapper bean
ClustererBeanInfo() - Constructor for class weka.gui.beans.ClustererBeanInfo
ClustererCustomizer - Class in weka.gui.beans: GUI customizer for the Clusterer wrapper bean
ClustererCustomizer() - Constructor for class weka.gui.beans.ClustererCustomizer
ClustererPanel - Class in weka.gui.explorer: This panel allows the user to select and configure a clusterer, and evaluate the clusterer using a number of testing modes (test on the training data, train/test on a percentage split, test on a separate split).
ClustererPanel() - Constructor for class weka.gui.explorer.ClustererPanel: Creates the clusterer panel
ClustererPerformanceEvaluator - Class in weka.gui.beans: A bean that evaluates the performance of batch trained clusterers
ClustererPerformanceEvaluator() - Constructor for class weka.gui.beans.ClustererPerformanceEvaluator
ClustererPerformanceEvaluatorBeanInfo - Class in weka.gui.beans: Bean info class for the clusterer performance evaluator
ClustererPerformanceEvaluatorBeanInfo() - Constructor for class weka.gui.beans.ClustererPerformanceEvaluatorBeanInfo
clustererTipText() - Method in class weka.classifiers.meta.ClassificationViaClustering: Returns the tip text for this property
clustererTipText() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns the tip text for this property
clustererTipText() - Method in class weka.clusterers.SingleClustererEnhancer: Returns the tip text for this property
clustererTipText() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Returns the tip text for this property
clustererTipText() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns the tip text for this property
ClusterEvaluation - Class in weka.clusterers: Class for evaluating clustering models.
Valid options are:
-t name of the training file
Specify the training file.
ClusterEvaluation() - Constructor for class weka.clusterers.ClusterEvaluation: Constructor.
ClusterGenerator - Class in weka.datagenerators: Abstract class for cluster data generators.
ClusterGenerator() - Constructor for class weka.datagenerators.ClusterGenerator: initializes the generator
clusteringSeedTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
clusterInstance(Instance) - Method in class weka.clusterers.AbstractClusterer: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.CLOPE: Classifies a given instance.
clusterInstance(Instance) - Method in interface weka.clusterers.Clusterer: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.Cobweb: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.DBSCAN: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.FarthestFirst: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.HierarchicalClusterer
clusterInstance(Instance) - Method in class weka.clusterers.OPTICS: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.sIB: Cluster a given instance, this is the method defined in Clusterer interface do nothing but just return the cluster assigned to it
clusterInstance(Instance) - Method in class weka.clusterers.SimpleKMeans: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.XMeans: Classifies a given instance.
ClusterMembership - Class in weka.filters.unsupervised.attribute: A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data).
ClusterMembership() - Constructor for class weka.filters.unsupervised.attribute.ClusterMembership
clusterPriors() - Method in class weka.clusterers.AbstractDensityBasedClusterer: Returns the prior probability of each cluster.
clusterPriors() - Method in interface weka.clusterers.DensityBasedClusterer: Returns the prior probability of each cluster.
clusterPriors() - Method in class weka.clusterers.EM: Returns the cluster priors.
clusterPriors() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns the cluster priors.
clusterResultsToString() - Method in class weka.clusterers.ClusterEvaluation: return the results of clustering.
clusters - Variable in class weka.clusterers.CLOPE: Array of clusters
clusterSubTypeTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns the tip text for this property
clusterTypeTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns the tip text for this property
Cobweb - Class in weka.clusterers: Class implementing the Cobweb and Classit clustering algorithms.

Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
Cobweb() - Constructor for class weka.clusterers.Cobweb: default constructor
cochransCriterion(double[][]) - Static method in class weka.core.ContingencyTables: Tests if Cochran's criterion is fullfilled for the given contingency table.
codingCost() - Method in class weka.classifiers.trees.j48.C45Split: Returns coding cost for split (used in rule learner).
codingCost() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns coding costs of model.
coef0TipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
coefficients() - Method in class weka.classifiers.functions.LinearRegression: Returns the coefficients for this linear model.
coefficients() - Method in class weka.classifiers.functions.Logistic: Returns the coefficients for this logistic model.
coefficients() - Method in class weka.classifiers.functions.PaceRegression: Returns the coefficients for this linear model.
coefficients() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Return the array of coefficients
collapse() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree: Collapses a tree to a node if training error doesn't increase.
collapse() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Collapses a tree to a node if training error doesn't increase.
COLOR_STDERR - Static variable in class weka.gui.LogWindow: the Color of the style for stderr
COLOR_STDOUT - Static variable in class weka.gui.LogWindow: the color of the style for stdout
Colors - Class in weka.gui.treevisualizer: This class maintains a list that contains all the colornames from the dotty standard and what color (in RGB) they represent
Colors() - Constructor for class weka.gui.treevisualizer.Colors
columnResponseExplanation(PaceMatrix, IntVector, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the squared ks-th response value if the j-th column becomes the ks-th after orthogonal transformation.
combinationRuleTipText() - Method in class weka.classifiers.meta.Vote: Returns the tip text for this property
combinations(int, int) - Static method in class weka.classifiers.functions.LeastMedSq: Produces the combination nCr
combinationTipText() - Method in class weka.attributeSelection.ScatterSearchV1: Returns the tip text for this property
combinedDL(double, double) - Method in class weka.classifiers.rules.RuleStats: Compute the combined DL of the ruleset in this class, i.e.
CombineParents() - Method in class weka.attributeSelection.ScatterSearchV1: Combine all the posible pair solutions existing in the Population
combSort11(double[], int[]) - Static method in class weka.core.neighboursearch.NearestNeighbourSearch: sorts the two given arrays.
COMMA - Static variable in interface weka.core.mathematicalexpression.sym
COMMA - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
CommandlineCompletion() - Constructor for class weka.gui.SimpleCLIPanel.CommandlineCompletion: default constructor.
compactify() - Method in class weka.core.Instances: Compactifies the set of instances.
compare(Object, Object) - Method in class weka.core.ClassDiscovery.StringCompare: Compares its two arguments for order.
compare(Object, Object) - Method in class weka.core.InstanceComparator: compares the two instances, returns -1 if o1 is smaller than o2, 0 if equal and +1 if greater.
compareTo(FPGrowth.AssociationRule) - Method in class weka.associations.FPGrowth.AssociationRule: Compare this rule to the supplied rule.
compareTo(FPGrowth.BinaryItem) - Method in class weka.associations.FPGrowth.BinaryItem: Ensures that items will be sorted in descending order of frequency.
compareTo(Object) - Method in class weka.associations.RuleItem: compares two RuleItems and allows an ordering concerning expected predictive accuracy and time of generation Note: this class has a natural ordering that is inconsistent with equals
compareTo(Object) - Method in class weka.classifiers.trees.j48.GraftSplit: method needed for sorting a collection of GraftSplits by laplace value
compareTo(AttributeLocator) - Method in class weka.core.AttributeLocator: Compares this object with the specified object for order.
compareTo(Object) - Method in class weka.core.Version: checks the version of this class against the given version-string
compareTo(SortedTableModel.SortContainer) - Method in class weka.gui.SortedTableModel.SortContainer: Compares this object with the specified object for order.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.Splitter: Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the string describing the comparision the split depends on for a particular branch.
ComplementNaiveBayes - Class in weka.classifiers.bayes: Class for building and using a Complement class Naive Bayes classifier.

For more information see,

Jason D.
ComplementNaiveBayes() - Constructor for class weka.classifiers.bayes.ComplementNaiveBayes
complexityParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
ComponentHelper - Class in weka.gui: A helper class for some common tasks with Dialogs, Icons, etc.
ComponentHelper() - Constructor for class weka.gui.ComponentHelper
componentHidden(ComponentEvent) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
componentMoved(ComponentEvent) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
componentResized(ComponentEvent) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
componentShown(ComponentEvent) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer
compressOutputTipText() - Method in class weka.core.converters.ArffSaver: Returns the tip text for this property
compressOutputTipText() - Method in class weka.core.converters.XRFFSaver: Returns the tip text for this property
Compute - Interface in weka.experiment: Interface to something that can accept remote connections and execute a task.
computeLoglikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl: This method calls the log-likelihood implemented in the Prior abstract class.
computeLogLikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl: Computes the log-likelihood values using the implementation in the Prior class.
computelogLikelihood(double[], Instances) - Method in class weka.classifiers.bayes.blr.Prior: Function computes the log-likelihood value: -sum{1 to n}{ln(1+exp(-Beta*x(i)*y(i))}
computeMinMaxAtts() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set up the bounds of our graphic based by finding the smallest reasonable area in the instance space to surround our data points.
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl: This function computes the penalty term specific to Gaussian distribution.
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl: This function computes the penalty term specific to Laplacian distribution.
computePenalty(double[], double[]) - Method in class weka.classifiers.bayes.blr.Prior: Skeleton function to compute penalty terms.
cond() - Method in class weka.core.matrix.Matrix: Matrix condition (2 norm)
cond() - Method in class weka.core.matrix.SingularValueDecomposition: Two norm condition number
ConditionalEstimator - Interface in weka.estimators: Interface for conditional probability estimators.
confidenceFactorTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
confidenceFactorTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
confidenceFactorTipText() - Method in class weka.classifiers.trees.J48graft: Returns the tip text for this property
confidenceForRule(AprioriItemSet, AprioriItemSet) - Static method in class weka.associations.AprioriItemSet: Outputs the confidence for a rule.
CONFIG - Static variable in class weka.core.Debug: the log level Vonfig
confirmationComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their confirmation value.
confirmationThenObservedComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their confirmation and then their observed number of counter-instances.
confirmationThresholdTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
confirmationValuesTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
ConfusionMatrix - Class in weka.classifiers.evaluation: Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.
confusionMatrix() - Method in class weka.classifiers.Evaluation: Returns a copy of the confusion matrix.
ConfusionMatrix(String[]) - Constructor for class weka.classifiers.evaluation.ConfusionMatrix: Creates the confusion matrix with the given class names.
ConjunctiveRule - Class in weka.classifiers.rules: This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.

A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression.
ConjunctiveRule() - Constructor for class weka.classifiers.rules.ConjunctiveRule
connect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection: Connects two units together.
CONNECT - Static variable in class weka.gui.sql.event.ConnectionEvent: it was a connect try
CONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This flag is set once the unit has a connection.
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractDataSink: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.AbstractDataSink: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(String) - Method in class weka.gui.beans.AbstractEvaluator: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractEvaluator: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTestSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractTestSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.Associator: Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Associator: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(EventSetDescriptor) - Method in interface weka.gui.beans.BeanCommon: Returns true if, at this time, the object will accept a connection via the supplied EventSetDescriptor
connectionAllowed(String) - Method in interface weka.gui.beans.BeanCommon: Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(String) - Method in class weka.gui.beans.ClassAssigner: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.ClassAssigner: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.Classifier: Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Classifier: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.ClassValuePicker: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.ClassValuePicker: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.Clusterer: Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Clusterer: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.CostBenefitAnalysis: Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.CostBenefitAnalysis: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.Filter: Returns true if, at this time, the object will accept a connection with respect to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Filter: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Returns true if, at this time, the object will accept a connection with respect to the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.Loader: Returns true if, at this time, the object will accept a connection via the supplied EventSetDescriptor.
connectionAllowed(String) - Method in class weka.gui.beans.Loader: Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.MetaBean: Returns true if, at this time, the object will accept a connection with respect to the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.MetaBean
connectionAllowed(String) - Method in class weka.gui.beans.PredictionAppender: Returns true if, at this time, the object will accept a connection according to the supplied event name
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.PredictionAppender: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.SerializedModelSaver: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor.
connectionAllowed(String) - Method in class weka.gui.beans.SerializedModelSaver: Returns true if, at this time, the object will accept a connection according to the supplied event name.
connectionAllowed(String) - Method in class weka.gui.beans.StripChart: Returns true if, at this time, the object will accept a connection via the named event
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.StripChart: Returns true if, at this time, the object will accept a connection according to the supplied EventSetDescriptor
connectionAllowed(EventSetDescriptor) - Method in class weka.gui.beans.TextViewer: Returns true if, at this time, the object will accept a connection via the supplied EventSetDescriptor
connectionAllowed(String) - Method in class weka.gui.beans.TextViewer: Returns true if, at this time, the object will accept a connection via the named event
connectionChange(ConnectionEvent) - Method in interface weka.gui.sql.event.ConnectionListener: This method gets called when the connection is either established or disconnected.
connectionChange(ConnectionEvent) - Method in class weka.gui.sql.QueryPanel: This method gets called when the connection is either established or disconnected.
connectionChange(ConnectionEvent) - Method in class weka.gui.sql.SqlViewer: This method gets called when the connection is either established or disconnected.
ConnectionEvent - Class in weka.gui.sql.event: An event that is generated when a connection is established or dropped.
ConnectionEvent(Object, int, DbUtils) - Constructor for class weka.gui.sql.event.ConnectionEvent: constructs the event
ConnectionEvent(Object, int, DbUtils, Exception) - Constructor for class weka.gui.sql.event.ConnectionEvent: constructs the event
ConnectionListener - Interface in weka.gui.sql.event: A listener for connect/disconnect events.
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractDataSink: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractEvaluator: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTestSetProducer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.Associator: Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in interface weka.gui.beans.BeanCommon: Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.ClassAssigner: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.Classifier: Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in class weka.gui.beans.ClassValuePicker: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.Clusterer: Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in interface weka.gui.beans.ConnectionNotificationConsumer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name.
connectionNotification(String, Object) - Method in class weka.gui.beans.CostBenefitAnalysis: Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.Filter: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in class weka.gui.beans.Loader: Notify this object that it has been registered as a listener with a source for receiving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.MetaBean: Notify this object that it has been registered as a listener with a source with respect to the named event.
connectionNotification(String, Object) - Method in class weka.gui.beans.PredictionAppender: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.SerializedModelSaver: Notify this object that it has been registered as a listener with a source with respect to the supplied event name.
connectionNotification(String, Object) - Method in class weka.gui.beans.StripChart: Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.TextViewer: Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
ConnectionNotificationConsumer - Interface in weka.gui.beans: Interface for Beans that can receive (dis-)connection events generated when (dis-)connecting data processing nodes in the Weka KnowledgeFlow.
ConnectionPanel - Class in weka.gui.sql: Enables the user to insert a database URL, plus user/password to connect to this database.
ConnectionPanel(JFrame) - Constructor for class weka.gui.sql.ConnectionPanel: initializes the panel.
CONNECTIONS - Static variable in class weka.gui.beans.BeanConnection: The list of connections
connectToDatabase() - Method in class weka.core.converters.DatabaseLoader: Opens a connection to the database
connectToDatabase() - Method in class weka.core.converters.DatabaseSaver: Opens a connection to the database.
connectToDatabase() - Method in class weka.experiment.DatabaseUtils: Opens a connection to the database.
consequence() - Method in class weka.associations.RuleItem: Gets the consequence of a rule
conservativeForwardSelectionTipText() - Method in class weka.attributeSelection.GreedyStepwise: Returns the tip text for this property
ConsistencySubsetEval - Class in weka.attributeSelection: ConsistencySubsetEval :

Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes.
ConsistencySubsetEval() - Constructor for class weka.attributeSelection.ConsistencySubsetEval: Constructor.
ConsistencySubsetEval.hashKey - Class in weka.attributeSelection: Class providing keys to the hash table.
ConsoleLogger - Class in weka.core.logging: A simple logger that outputs the logging information in the console.
ConsoleLogger() - Constructor for class weka.core.logging.ConsoleLogger
CONST_AUTOMATIC_SHAPE - Static variable in class weka.gui.visualize.Plot2D
Constant - Class in weka.core.pmml: Class encapsulating a Constant Expression.
Constant(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.Constant: Construct an new Constant Expression.
constructWithCopy(double[][]) - Static method in class weka.core.matrix.Matrix: Construct a matrix from a copy of a 2-D array.
containChildBallsTipText() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the tip text for this property.
containedBy(Instance) - Method in class weka.associations.ItemSet: Checks if an instance contains an item set.
contains(Literal) - Method in class weka.associations.tertius.LiteralSet: Test if this LiteralSet contains a given Literal.
contains(int) - Method in class weka.classifiers.bayes.net.ParentSet: test if node is contained in parent set
contains(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Checks whether an element is in the set.
contains(DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Tests if the database contains the dataObject_Query
contains(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Tests if the database contains the dataObject_Query
contains(Object) - Method in class weka.core.FastVector: added by akibriya
contains(PrintStream) - Method in class weka.core.Tee: checks whether the given PrintStream is already in the list.
contains(Object) - Method in class weka.core.Trie: Returns true if this collection contains the specified element.
contains(String) - Method in class weka.core.Trie.TrieNode: checks whether a suffix can be found in its children
contains(String) - Method in class weka.core.xml.MethodHandler: checks whether a method is stored for the given property
contains(Class) - Method in class weka.core.xml.MethodHandler: checks whether a method is stored for the given class
contains(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Tests whether the specified object is a component in this list.
contains(String) - Method in class weka.gui.HierarchyPropertyParser: Whether the HierarchyPropertyParser contains the given string
containsAll(Collection<?>) - Method in class weka.core.Trie: Returns true if this collection contains all of the elements in the specified collection.
containsEnvVariables(String) - Static method in class weka.core.Environment: Tests for the presence of environment variables.
containsItems(ArrayList<Attribute>, boolean) - Method in class weka.associations.FPGrowth.AssociationRule
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Tests if the specified double is a key in this hashtable.
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache: Checks if the specified key maps with an entry in the cache table
containsOverOneEvent() - Method in class weka.associations.gsp.Element: Checks if an Element contains over one event.
containsPrefix(String) - Method in class weka.core.Trie: checks whether the given prefix is stored in the trie
containsValue(double) - Method in class weka.core.pmml.FieldMetaInfo.Interval: Returns true if this interval contains the supplied value.
containsWindow(Class) - Method in class weka.gui.Main: checks, whether an instance of the given window class is already in the Window list.
containsWindow(String) - Method in class weka.gui.Main: checks, whether a window with the given title is already in the Window list.
context() - Method in class weka.gui.HierarchyPropertyParser: The context of the current node, i.e.
ContingencyTables - Class in weka.core: Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class weka.core.ContingencyTables
CONTINUOUS - Static variable in class weka.datagenerators.clusterers.SubspaceCluster: cluster subtype: continuous
ConverterFileChooser - Class in weka.gui: A specialized JFileChooser that lists all available file Loaders and Savers.
ConverterFileChooser() - Constructor for class weka.gui.ConverterFileChooser: onstructs a FileChooser pointing to the user's default directory.
ConverterFileChooser(File) - Constructor for class weka.gui.ConverterFileChooser: Constructs a FileChooser using the given File as the path.
ConverterFileChooser(String) - Constructor for class weka.gui.ConverterFileChooser: Constructs a FileChooser using the given path.
ConverterUtils - Class in weka.core.converters: Utility routines for the converter package.
ConverterUtils() - Constructor for class weka.core.converters.ConverterUtils
ConverterUtils.DataSink - Class in weka.core.converters: Helper class for saving data to files.
ConverterUtils.DataSource - Class in weka.core.converters: Helper class for loading data from files and URLs.
convertInfixToPostfix(String) - Method in class weka.core.AttributeExpression: Converts a string containing a mathematical expression in infix form to postfix form.
convertInstance(Instance) - Method in interface weka.attributeSelection.AttributeTransformer: Transforms an instance in the format of the original data to the transformed space
convertInstance(Instance) - Method in class weka.attributeSelection.LatentSemanticAnalysis: Transform an instance in original (unnormalized) format
convertInstance(Instance) - Method in class weka.attributeSelection.PrincipalComponents: Transform an instance in original (unormalized) format.
convertNewLines(String) - Static method in class weka.core.Utils: Converts carriage returns and new lines in a string into \r and \n.
convertNominalTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
convertNominalToBinaryTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
convertNumericAttToNominal(int, ArrayList<String>) - Method in class weka.core.pmml.MiningSchema: Convert a numeric attribute in the mining schema to nominal.
convertStringAttsToNominal() - Method in class weka.core.pmml.MiningSchema: Method to convert any string attributes in the mining schema Instances to nominal attributes.
convertToAttribX(double) - Method in class weka.gui.visualize.Plot2D: convert a Panel x coordinate to a raw x value.
convertToAttribY(double) - Method in class weka.gui.visualize.Plot2D: convert a Panel y coordinate to a raw y value.
convertToPanelX(double) - Method in class weka.gui.visualize.Plot2D: Convert an raw x value to Panel x coordinate.
convertToPanelY(double) - Method in class weka.gui.visualize.Plot2D: Convert an raw y value to Panel y coordinate.
convertToRelativePath(File) - Static method in class weka.core.Utils: Converts a File's absolute path to a path relative to the user (ie start) directory.
convictionForRule(AprioriItemSet, AprioriItemSet, int, int) - Method in class weka.associations.AprioriItemSet: Outputs the conviction for a rule.
copy() - Method in class weka.associations.tertius.IndividualInstance
copy(ParentSet) - Method in class weka.classifiers.bayes.net.ParentSet: Copy makes current parents set equal to other parent set
copy() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Return a shallow copy of this kernel
copy() - Method in class weka.classifiers.rules.JRip.Antd: Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.NominalAntd: Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.NumericAntd: Implements Copyable
copy() - Method in class weka.classifiers.rules.JRip.RipperRule: Get a shallow copy of this rule
copy() - Method in class weka.classifiers.rules.Rule: Get a shallow copy of this rule
copy() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Makes a copy of this CorrelationSplitInfo object
copy() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: makes a copy of the SplitEvaluate object
copy() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Makes a copy of this SplitInfo object
copy() - Method in class weka.core.Attribute: Produces a shallow copy of this attribute.
copy(String) - Method in class weka.core.Attribute: Produces a shallow copy of this attribute with a new name.
copy() - Method in class weka.core.BinarySparseInstance: Produces a shallow copy of this instance.
copy() - Method in interface weka.core.Copyable: This method produces a shallow copy of an object.
copy() - Method in class weka.core.FastVector: Produces a shallow copy of this vector.
copy() - Method in class weka.core.Instance: Produces a shallow copy of this instance.
copy() - Method in class weka.core.matrix.DoubleVector: Makes a deep copy of the vector
copy() - Method in class weka.core.matrix.IntVector: Makes a deep copy of the vector
copy() - Method in class weka.core.matrix.Matrix: Make a deep copy of a matrix
copy() - Method in class weka.core.SparseInstance: Produces a shallow copy of this instance.
Copy - Class in weka.filters.unsupervised.attribute: An instance filter that copies a range of attributes in the dataset.
Copy() - Constructor for class weka.filters.unsupervised.attribute.Copy
Copyable - Interface in weka.core: Interface implemented by classes that can produce "shallow" copies of their objects.
copyArea(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
copyContent() - Method in class weka.gui.arffviewer.ArffPanel: copies the content of the selection to the clipboard
copyContent() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: copies the content of the selection to the clipboard
copyElements() - Method in class weka.core.FastVector: Clones the vector and shallow copies all its elements.
copyInto(Object[]) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Copies the components of this list into the specified array.
copyRelationalValues(Instance, Instances, AttributeLocator) - Static method in class weka.core.RelationalLocator: Copies relational values contained in the instance copied to a new dataset.
copyRelationalValues(Instance, boolean, Instances, AttributeLocator, Instances, AttributeLocator) - Static method in class weka.core.RelationalLocator: Takes relational values referenced by an Instance and copies them from a source dataset to a destination dataset.
Copyright - Class in weka.core: A class for providing centralized Copyright information.
Copyright() - Constructor for class weka.core.Copyright
copyStringValues(Instance, Instances, AttributeLocator) - Static method in class weka.core.StringLocator: Copies string values contained in the instance copied to a new dataset.
copyStringValues(Instance, boolean, Instances, AttributeLocator, Instances, AttributeLocator) - Static method in class weka.core.StringLocator: Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyToClipboard() - Method in class weka.gui.sql.InfoPanel: copies the currently selected error message to the clipboard
CORE_FILE_LOADERS - Static variable in class weka.core.converters.ConverterUtils: the core loaders - hardcoded list necessary for RMI/Remote Experiments (comma-separated list).
CORE_FILE_SAVERS - Static variable in class weka.core.converters.ConverterUtils: the core savers - hardcoded list necessary for RMI/Remote Experiments (comma-separated list).
coreDistance(int, double, DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Calculates the coreDistance for the specified DataObject.
coreDistance(int, double, DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Calculates the coreDistance for the specified DataObject.
correct() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of correct classifications (that is, for which a correct prediction was made).
correct() - Method in class weka.classifiers.Evaluation: Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correlation(double[], double[], int) - Static method in class weka.core.Utils: Returns the correlation coefficient of two double vectors.
correlation - Variable in class weka.experiment.PairedStats: The correlation coefficient
correlationCoefficient() - Method in class weka.classifiers.Evaluation: Returns the correlation coefficient if the class is numeric.
CorrelationSplitInfo - Class in weka.classifiers.trees.m5: Finds split points using correlation.
CorrelationSplitInfo(int, int, int) - Constructor for class weka.classifiers.trees.m5.CorrelationSplitInfo: Constructs an object which contains the split information
COS - Static variable in interface weka.core.mathematicalexpression.sym
COS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
CostBenefitAnalysis - Class in weka.gui.beans: Bean that aids in analyzing cost/benefit tradeoffs.
CostBenefitAnalysis() - Constructor for class weka.gui.beans.CostBenefitAnalysis: Constructor.
CostBenefitAnalysisBeanInfo - Class in weka.gui.beans: Bean info class for the cost/benefit analysis
CostBenefitAnalysisBeanInfo() - Constructor for class weka.gui.beans.CostBenefitAnalysisBeanInfo
CostCurve - Class in weka.classifiers.evaluation: Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes.
CostCurve() - Constructor for class weka.classifiers.evaluation.CostCurve
CostMatrix - Class in weka.classifiers: Class for storing and manipulating a misclassification cost matrix.
CostMatrix(int) - Constructor for class weka.classifiers.CostMatrix: Creates a default cost matrix of a particular size.
CostMatrix(CostMatrix) - Constructor for class weka.classifiers.CostMatrix: Creates a cost matrix that is a copy of another.
CostMatrix(Reader) - Constructor for class weka.classifiers.CostMatrix: Reads a matrix from a reader.
CostMatrixEditor - Class in weka.gui: Class for editing CostMatrix objects.
CostMatrixEditor() - Constructor for class weka.gui.CostMatrixEditor: Constructs a new CostMatrixEditor.
costMatrixSourceTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
costMatrixSourceTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
costMatrixSourceTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
costMatrixTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
costMatrixTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
costMatrixTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
CostSensitiveASEvaluation - Class in weka.attributeSelection: Abstract base class for cost-sensitive subset and attribute evaluators.
CostSensitiveASEvaluation() - Constructor for class weka.attributeSelection.CostSensitiveASEvaluation
CostSensitiveAttributeEval - Class in weka.attributeSelection: A meta subset evaluator that makes its base subset evaluator cost-sensitive.
CostSensitiveAttributeEval() - Constructor for class weka.attributeSelection.CostSensitiveAttributeEval: Default constructor.
CostSensitiveClassifier - Class in weka.classifiers.meta: A metaclassifier that makes its base classifier cost-sensitive.
CostSensitiveClassifier() - Constructor for class weka.classifiers.meta.CostSensitiveClassifier: Default constructor.
CostSensitiveClassifierSplitEvaluator - Class in weka.experiment: SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
CostSensitiveClassifierSplitEvaluator() - Constructor for class weka.experiment.CostSensitiveClassifierSplitEvaluator
CostSensitiveSubsetEval - Class in weka.attributeSelection: A meta subset evaluator that makes its base subset evaluator cost-sensitive.
CostSensitiveSubsetEval() - Constructor for class weka.attributeSelection.CostSensitiveSubsetEval: Default constructor.
costTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
costTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
count() - Method in class weka.associations.RuleGeneration: Gets the actual maximum value of the generation time
count - Variable in class weka.experiment.PairedStats: The number of data points seen
count - Variable in class weka.experiment.Stats: The number of values seen
countBagCiters(Instance) - Method in class weka.classifiers.mi.CitationKNN: calculates the citers associated to a bag
countBagReferences(Instance) - Method in class weka.classifiers.mi.CitationKNN: Calculates the references of the exemplar bag
countData() - Method in class weka.classifiers.rules.RuleStats: Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule
countData(int, Instances, double[][]) - Method in class weka.classifiers.rules.RuleStats: Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...(index-1), and the statistics of these rules are provided.
This procedure is typically useful when a temporary object of RuleStats is constructed in order to efficiently calculate the relative DL of rule in position index, thus all other stuff is not needed.
counter() - Method in class weka.associations.ItemSet: Gets the counter
counterInstance(Instance, Instance) - Method in class weka.associations.tertius.LiteralSet: Test if an individual instance, given a part instance of this individual, is a counter-instance of this LiteralSet.
counterInstance(Instance) - Method in class weka.associations.tertius.LiteralSet: Test if an instance is a counter-instance of this LiteralSet.
counterInstance(Instance) - Method in class weka.associations.tertius.Rule: Test if an instance is a counter-instance of this rule.
countsForInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet: Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
covers(Instance) - Method in class weka.classifiers.rules.JRip.Antd
covers(Instance) - Method in class weka.classifiers.rules.JRip.NominalAntd: Whether the instance is covered by this antecedent
covers(Instance) - Method in class weka.classifiers.rules.JRip.NumericAntd: Whether the instance is covered by this antecedent
covers(Instance) - Method in class weka.classifiers.rules.JRip.RipperRule: Whether the instance covered by this rule
covers(Instance) - Method in class weka.classifiers.rules.Rule: Whether the instance covered by this rule
CoverTree - Class in weka.core.neighboursearch: Class implementing the CoverTree datastructure.
The class is very much a translation of the c source code made available by the authors.

For more information and original source code see:

Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor.
CoverTree() - Constructor for class weka.core.neighboursearch.CoverTree: default constructor.
CoverTree.CoverTreeNode - Class in weka.core.neighboursearch: class representing a node of the cover tree.
CoverTreeNode() - Constructor for class weka.core.neighboursearch.CoverTree.CoverTreeNode: Constructor for the class.
CoverTreeNode(Integer, double, double, Stack<CoverTree.CoverTreeNode>, int, int) - Constructor for class weka.core.neighboursearch.CoverTree.CoverTreeNode: Constructor.
CramersV(double[][]) - Static method in class weka.core.ContingencyTables: Computes Cramer's V for a contingency table.
create(Reader) - Method in class weka.gui.treevisualizer.TreeBuild: This will build A node structure from the dotty format passed.
create() - Method in class weka.gui.visualize.PostscriptGraphics: Clone a PostscriptGraphics object
createExperimentIndex() - Method in class weka.experiment.DatabaseUtils: Attempts to create the experiment index table.
createExperimentIndexEntry(ResultProducer) - Method in class weka.experiment.DatabaseUtils: Attempts to insert a results entry for the table into the experiment index.
createNewVisualizerWindow(Classifier, Instances) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Creates a new GUI window with all of the BoundaryVisualizer trappings,
CreatePopulation(int) - Method in class weka.attributeSelection.ScatterSearchV1: Create the initial Population
createResultsTable(ResultProducer, String) - Method in class weka.experiment.DatabaseUtils: Creates a results table for the supplied result producer.
createSingleton(String[]) - Static method in class weka.gui.beans.KnowledgeFlowApp: Create the singleton instance of the KnowledgeFlow
createSingleton() - Static method in class weka.gui.GUIChooser: Create a singleton instance of the GUIChooser
createSingleton(String[]) - Static method in class weka.gui.Main: Create the singleton instance of the Main GUI.
createSubsampleWithoutReplacement(Random, int, int, int, int[]) - Method in class weka.filters.supervised.instance.Resample: creates the subsample without replacement.
createSubsampleWithoutReplacement(Random, int, int) - Method in class weka.filters.unsupervised.instance.Resample: creates the subsample without replacement
createSubsampleWithReplacement(Random, int, int, int, int[]) - Method in class weka.filters.supervised.instance.Resample: creates the subsample with replacement.
createSubsampleWithReplacement(Random, int, int) - Method in class weka.filters.unsupervised.instance.Resample: creates the subsample with replacement
criticalValueTipText() - Method in class weka.classifiers.bayes.AODEsr: Returns the tip text for this property
crossoverProbTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
crossValidate(NaiveBayesUpdateable, Instances, Random) - Static method in class weka.classifiers.trees.j48.NBTreeNoSplit: Utility method for fast 5-fold cross validation of a naive bayes model
CrossValidateAttributes() - Method in class weka.attributeSelection.AttributeSelection: Perform a cross validation for attribute selection.
crossValidateModel(Classifier, Instances, int, Random, Object...) - Method in class weka.classifiers.Evaluation: Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[], Random) - Method in class weka.classifiers.Evaluation: Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(DensityBasedClusterer, Instances, int, Random) - Static method in class weka.clusterers.ClusterEvaluation: Perform a cross-validation for DensityBasedClusterer on a set of instances.
crossValidateModel(String, Instances, int, String[], Random) - Static method in class weka.clusterers.ClusterEvaluation: Performs a cross-validation for a DensityBasedClusterer clusterer on a set of instances.
crossValidateTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property.
CrossValidationFoldMaker - Class in weka.gui.beans: Bean for splitting instances into training ant test sets according to a cross validation
CrossValidationFoldMaker() - Constructor for class weka.gui.beans.CrossValidationFoldMaker
CrossValidationFoldMakerBeanInfo - Class in weka.gui.beans: BeanInfo class for the cross validation fold maker bean
CrossValidationFoldMakerBeanInfo() - Constructor for class weka.gui.beans.CrossValidationFoldMakerBeanInfo
CrossValidationFoldMakerCustomizer - Class in weka.gui.beans: GUI Customizer for the cross validation fold maker bean
CrossValidationFoldMakerCustomizer() - Constructor for class weka.gui.beans.CrossValidationFoldMakerCustomizer
CrossValidationResultProducer - Class in weka.experiment: Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
CrossValidationResultProducer() - Constructor for class weka.experiment.CrossValidationResultProducer
crossValTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
CSVLoader - Class in weka.core.converters: Reads a source that is in comma separated or tab separated format.
CSVLoader() - Constructor for class weka.core.converters.CSVLoader: default constructor.
CSVResultListener - Class in weka.experiment: Takes results from a result producer and assembles them into comma separated value form.
CSVResultListener() - Constructor for class weka.experiment.CSVResultListener: Sets temporary file.
CSVSaver - Class in weka.core.converters: Writes to a destination that is in csv format
Valid options are:
CSVSaver() - Constructor for class weka.core.converters.CSVSaver: Constructor
cTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
cTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
cTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
cTipText() - Method in class weka.classifiers.mi.MISVM: Returns the tip text for this property
cumulate() - Method in class weka.core.matrix.DoubleVector: Returns a vector that stores the cumulated values of the original vector
cumulateInPlace() - Method in class weka.core.matrix.DoubleVector: Cumulates the original vector in place
cumulativeCV(BayesNet) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: CumulativeCV returns the accuracy calculated using cumulative cross validation.
CustomizerCloseRequester - Interface in weka.gui.beans: Customizers who want to be able to close the customizer window themselves can implement this window.
customizerClosing() - Method in class weka.gui.beans.ClassAssignerCustomizer
customizerClosing() - Method in class weka.gui.beans.ClassifierCustomizer
customizerClosing() - Method in class weka.gui.beans.ClassValuePickerCustomizer
customizerClosing() - Method in interface weka.gui.beans.CustomizerClosingListener: Customizer classes that want to know when they are being disposed of can implement this method.
CustomizerClosingListener - Interface in weka.gui.beans
CustomPanelSupplier - Interface in weka.gui: An interface for objects that are capable of supplying their own custom GUI components.
cutOffFactorTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
cutoffTipText() - Method in class weka.clusterers.Cobweb: Returns the tip text for this property
cutpointsToString(double[], boolean[]) - Static method in class weka.estimators.EstimatorUtils: Returns a string representing the cutpoints
CV_BASED - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
CVBasedHyperparameter() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Method computes the best hyperparameter value by doing cross -validation on the training data and compute the likelihood.
CVParameterSelection - Class in weka.classifiers.meta: Class for performing parameter selection by cross-validation for any classifier.

For more information, see:

R.
CVParameterSelection() - Constructor for class weka.classifiers.meta.CVParameterSelection
CVParametersTipText() - Method in class weka.classifiers.meta.CVParameterSelection: Returns the tip text for this property
CVResultsString() - Method in class weka.attributeSelection.AttributeSelection: returns a string summarizing the results of repeated attribute selection runs on splits of a dataset.
CVTypeTipText() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm

D

D_CONVCHCLOSER - Static variable in class weka.clusterers.XMeans: have a closer look at converge children.
D_CURR - Static variable in class weka.clusterers.XMeans: for current debug.
D_FOLLOWSPLIT - Static variable in class weka.clusterers.XMeans: follows the splitting of the centers.
D_GENERAL - Static variable in class weka.clusterers.XMeans: general debugging.
D_ITERCOUNT - Static variable in class weka.clusterers.XMeans: follow iterations.
D_KDTREE - Static variable in class weka.clusterers.XMeans: check on kdtree.
D_METH_MISUSE - Static variable in class weka.clusterers.XMeans: functions were maybe misused.
D_PRINTCENTERS - Static variable in class weka.clusterers.XMeans: print the centers.
D_RANDOMVECTOR - Static variable in class weka.clusterers.XMeans: check on random vectors.
Dagging - Class in weka.classifiers.meta: This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
Dagging() - Constructor for class weka.classifiers.meta.Dagging: Constructor.
Database - Interface in weka.clusterers.forOPTICSAndDBScan.Databases: Database.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 20, 2004
Time: 1:03:43 PM
$ Revision 1.4 $
database_distanceTypeTipText() - Method in class weka.clusterers.DBSCAN: Returns the tip text for this property
database_distanceTypeTipText() - Method in class weka.clusterers.OPTICS: Returns the tip text for this property
database_TypeTipText() - Method in class weka.clusterers.DBSCAN: Returns the tip text for this property
database_TypeTipText() - Method in class weka.clusterers.OPTICS: Returns the tip text for this property
DatabaseConnection - Class in weka.core.converters: Connects to a database.
DatabaseConnection() - Constructor for class weka.core.converters.DatabaseConnection: Sets up the database drivers
DatabaseConnectionDialog - Class in weka.gui: A dialog to enter URL, username and password for a database connection.
DatabaseConnectionDialog(Frame) - Constructor for class weka.gui.DatabaseConnectionDialog: Create database connection dialog.
DatabaseConnectionDialog(Frame, String, String) - Constructor for class weka.gui.DatabaseConnectionDialog: Create database connection dialog.
DatabaseConnectionDialog(Frame, String, String, boolean) - Constructor for class weka.gui.DatabaseConnectionDialog: Create database connection dialog.
DatabaseConverter - Interface in weka.core.converters: Marker interface for a loader/saver that uses a database
databaseForName(String, Instances) - Method in class weka.clusterers.DBSCAN: Returns a new Class-Instance of the specified database
databaseForName(String, Instances) - Method in class weka.clusterers.OPTICS: Returns a new Class-Instance of the specified database
DatabaseLoader - Class in weka.core.converters: Reads Instances from a Database.
DatabaseLoader() - Constructor for class weka.core.converters.DatabaseLoader: Constructor
databaseOutputTipText() - Method in class weka.clusterers.OPTICS: Returns the tip text for this property.
DatabaseResultListener - Class in weka.experiment: Takes results from a result producer and sends them to a database.
DatabaseResultListener() - Constructor for class weka.experiment.DatabaseResultListener: Sets up the database drivers
DatabaseResultProducer - Class in weka.experiment: Examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
DatabaseResultProducer() - Constructor for class weka.experiment.DatabaseResultProducer: Creates the DatabaseResultProducer, letting the parent constructor do it's thing.
DatabaseSaver - Class in weka.core.converters: Writes to a database (tested with MySQL, InstantDB, HSQLDB).
DatabaseSaver() - Constructor for class weka.core.converters.DatabaseSaver: Constructor.
databaseURLTipText() - Method in class weka.experiment.DatabaseUtils: Returns the tip text for this property.
DatabaseUtils - Class in weka.experiment: DatabaseUtils provides utility functions for accessing the experiment database.
DatabaseUtils() - Constructor for class weka.experiment.DatabaseUtils: Reads properties and sets up the database drivers.
dataDL(double, double, double, double, double) - Static method in class weka.classifiers.rules.RuleStats: The description length of data given the parameters of the data based on the ruleset.
DataFormatListener - Interface in weka.gui.beans: Listener interface that customizer classes that are interested in data format changes can implement.
DataGenerator - Class in weka.datagenerators: Abstract superclass for data generators that generate data for classifiers and clusterers.
DataGenerator() - Constructor for class weka.datagenerators.DataGenerator: initializes with default settings.
DataGenerator - Interface in weka.gui.boundaryvisualizer: Interface to something that can generate new instances based on a set of input instances
DataGeneratorPanel - Class in weka.gui.explorer: A panel for generating artificial data via DataGenerators.
DataGeneratorPanel() - Constructor for class weka.gui.explorer.DataGeneratorPanel: creates the panel
DataNearBalancedND - Class in weka.classifiers.meta.nestedDichotomies: A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure.

For more info, check

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
DataNearBalancedND() - Constructor for class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Constructor.
DataObject - Interface in weka.clusterers.forOPTICSAndDBScan.DataObjects: DataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:48:59 PM
$ Revision 1.4 $
dataObjectForName(String, Instance, String, Database) - Method in class weka.clusterers.DBSCAN: Returns a new Class-Instance of the specified database
dataObjectForName(String, Instance, String, Database) - Method in class weka.clusterers.OPTICS: Returns a new Class-Instance of the specified database
dataObjectIterator() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Returns an iterator over all the dataObjects in the database
dataObjectIterator() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Returns an iterator over all the dataObjects in the database
dataSeqIDTipText() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the dataSeqID option tip text for the Weka GUI.
dataset() - Method in class weka.core.Instance: Returns the dataset this instance has access to.
DATASET_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer: The name of the key field containing the dataset name
DATASET_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer: The name of the key field containing the dataset name
DataSetEvent - Class in weka.gui.beans: Event encapsulating a data set
DataSetEvent(Object, Instances) - Constructor for class weka.gui.beans.DataSetEvent
DatasetListPanel - Class in weka.gui.experiment: This panel controls setting a list of datasets for an experiment to iterate over.
DatasetListPanel(Experiment) - Constructor for class weka.gui.experiment.DatasetListPanel: Creates the dataset list panel with the given experiment.
DatasetListPanel() - Constructor for class weka.gui.experiment.DatasetListPanel: Create the dataset list panel initially disabled.
DataSink(String) - Constructor for class weka.core.converters.ConverterUtils.DataSink: initializes the sink to save the data to the given file.
DataSink(Saver) - Constructor for class weka.core.converters.ConverterUtils.DataSink: initializes the sink to save the data to the given Saver (expected to be fully configured).
DataSink(OutputStream) - Constructor for class weka.core.converters.ConverterUtils.DataSink: initializes the sink to save the data in the stream (always in ARFF format).
DataSink - Interface in weka.gui.beans: Indicator interface to something that can store instances to some destination
DataSource(String) - Constructor for class weka.core.converters.ConverterUtils.DataSource: Tries to load the data from the file.
DataSource(Instances) - Constructor for class weka.core.converters.ConverterUtils.DataSource: Initializes the datasource with the given dataset.
DataSource(Loader) - Constructor for class weka.core.converters.ConverterUtils.DataSource: Initializes the datasource with the given Loader.
DataSource(InputStream) - Constructor for class weka.core.converters.ConverterUtils.DataSource: Initializes the datasource with the given input stream.
DataSource - Interface in weka.gui.beans: Interface to something that is capable of being a source for data - either batch or incremental data
DataSourceListener - Interface in weka.gui.beans: Interface to something that can accept DataSetEvents
DATATYPE_LAYOUT - Static variable in class weka.gui.beans.xml.XMLBeans: the data that is about to be read/written contains a complete layout
DATATYPE_USERCOMPONENTS - Static variable in class weka.gui.beans.xml.XMLBeans: the data that is about to be read/written contains user-components, i.e., Metabeans
DataVisualizer - Class in weka.gui.beans: Bean that encapsulates weka.gui.visualize.VisualizePanel
DataVisualizer() - Constructor for class weka.gui.beans.DataVisualizer
DataVisualizerBeanInfo - Class in weka.gui.beans: Bean info class for the data visualizer
DataVisualizerBeanInfo() - Constructor for class weka.gui.beans.DataVisualizerBeanInfo
DATE - Static variable in class weka.core.Attribute: Constant set for attributes with date values.
DATE - Static variable in class weka.experiment.DatabaseUtils: Type mapping for DATE used for reading experiment results.
dateAttributesTipText() - Method in class weka.core.converters.CSVLoader: Returns the tip text for this property.
dateFormatTipText() - Method in class weka.core.converters.CSVLoader: Returns the tip text for this property.
dateFormatTipText() - Method in class weka.filters.unsupervised.attribute.Add: Returns the tip text for this property.
dateFormatTipText() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
DbConnectionDialog(String, String) - Method in class weka.gui.DatabaseConnectionDialog: Display the database connection dialog
DbConnectionDialog(String, String, boolean) - Method in class weka.gui.DatabaseConnectionDialog: Display the database connection dialog
DBO() - Constructor for class weka.core.Debug.DBO
DBSCAN - Class in weka.clusterers: Basic implementation of DBSCAN clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
DBSCAN() - Constructor for class weka.clusterers.DBSCAN
DbUtils - Class in weka.gui.sql: A little bit extended DatabaseUtils class.
DbUtils() - Constructor for class weka.gui.sql.DbUtils: initializes the object.
dchisq(double) - Static method in class weka.core.matrix.Maths: Returns the density of the Chi-squared distribution.
dchisq(double, double) - Static method in class weka.core.matrix.Maths: Returns the density of the noncentral Chi-squared distribution.
dchisq(double, DoubleVector) - Static method in class weka.core.matrix.Maths: Returns the density of the noncentral Chi-squared distribution.
dchisqLog(double) - Static method in class weka.core.matrix.Maths: Returns the log-density of the noncentral Chi-square distribution.
dchisqLog(double, double) - Static method in class weka.core.matrix.Maths: Returns the log-density value of a noncentral Chi-square distribution.
dchisqLog(double, DoubleVector) - Static method in class weka.core.matrix.Maths: Returns the log-density of a set of noncentral Chi-squared distributions.
DDConditionalEstimator - Class in weka.estimators: Conditional probability estimator for a discrete domain conditional upon a discrete domain.
DDConditionalEstimator(int, int, boolean) - Constructor for class weka.estimators.DDConditionalEstimator: Constructor
Debug - Class in weka.core: A helper class for debug output, logging, clocking, etc.
Debug() - Constructor for class weka.core.Debug: default constructor, prints only to stdout
Debug(String) - Constructor for class weka.core.Debug: logs the output to the specified file (and stdout).
Debug(String, int, int) - Constructor for class weka.core.Debug: logs the output
DEBUG - Static variable in class weka.gui.LogWindow: whether we're debugging - enables output on stdout
Debug.Clock - Class in weka.core: A little helper class for clocking and outputting times.
Debug.DBO - Class in weka.core: contains debug methods
Debug.Log - Class in weka.core: A helper class for logging stuff.
Debug.Random - Class in weka.core: This extended Random class enables one to print the generated random numbers etc., before they are returned.
Debug.SimpleLog - Class in weka.core: A little, simple helper class for logging stuff.
Debug.Timestamp - Class in weka.core: A class that can be used for timestamps in files, The toString() method simply returns the associated Date object in a timestamp format.
debugLevelTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
debugTipText() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the tip text for this property
debugTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
debugTipText() - Method in class weka.attributeSelection.ScatterSearchV1: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.Classifier: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.functions.LinearRegression: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.functions.Logistic: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.functions.PaceRegression: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
debugTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
debugTipText() - Method in class weka.clusterers.HierarchicalClusterer: Returns the tip text for this property
debugTipText() - Method in class weka.clusterers.sIB: Returns the tip text for this property
debugTipText() - Method in class weka.core.converters.TextDirectoryLoader: the tip text for this property
debugTipText() - Method in class weka.datagenerators.DataGenerator: Returns the tip text for this property
debugTipText() - Method in class weka.estimators.Estimator: Returns the tip text for this property
debugTipText() - Method in class weka.experiment.DatabaseUtils: Returns the tip text for this property.
debugTipText() - Method in class weka.filters.SimpleFilter: Returns the tip text for this property
debugTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the tip text for this property
debugVectorsFileTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
decayTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
decimalsTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
DecisionStump - Class in weka.classifiers.trees: Class for building and using a decision stump.
DecisionStump() - Constructor for class weka.classifiers.trees.DecisionStump
DecisionTable - Class in weka.classifiers.rules: Class for building and using a simple decision table majority classifier.

For more information see:

Ron Kohavi: The Power of Decision Tables.
DecisionTable() - Constructor for class weka.classifiers.rules.DecisionTable: Constructor for a DecisionTable
DecisionTableHashKey - Class in weka.classifiers.rules: Class providing hash table keys for DecisionTable
DecisionTableHashKey(Instance, int, boolean) - Constructor for class weka.classifiers.rules.DecisionTableHashKey: Constructor for a hashKey
DecisionTableHashKey(double[]) - Constructor for class weka.classifiers.rules.DecisionTableHashKey: Constructor for a hashKey
decompose() - Method in class weka.classifiers.BVDecompose: Carry out the bias-variance decomposition
decompose() - Method in class weka.classifiers.BVDecomposeSegCVSub: Carry out the bias-variance decomposition using the sub-sampled cross-validation method.
Decorate - Class in weka.classifiers.meta: DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
Decorate() - Constructor for class weka.classifiers.meta.Decorate: Constructor.
decreaseFrequency(int) - Method in class weka.associations.FPGrowth.BinaryItem: Decrease the frequency of this item.
decreaseFrequency() - Method in class weka.associations.FPGrowth.BinaryItem: Decrement the frequency of this item.
DEFAULT_COLORS - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanel: default colours for classes
DEFAULT_FORMAT - Static variable in class weka.core.Debug.Timestamp: the default format
DEFAULT_FORMAT - Static variable in class weka.gui.SimpleDateFormatEditor: the default format
DEFAULT_HEIGHT - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel: the default for height
DEFAULT_LEFT - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel: the default for left
DEFAULT_SEPARATORS - Static variable in class weka.core.TestInstances: the default word separators used in strings
DEFAULT_SHAPE_SIZE - Static variable in class weka.gui.visualize.Plot2D
DEFAULT_TOP - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel: the default for top
DEFAULT_WIDTH - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel: the default for width
DEFAULT_WORDS - Static variable in class weka.core.TestInstances: the default list of words used in strings
defaultEvaluatorString() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Return the name of the default evaluator.
defaultEvaluatorString() - Method in class weka.attributeSelection.CostSensitiveAttributeEval: Return the name of the default evaluator.
defaultOutput() - Method in class weka.datagenerators.DataGenerator: Gets the writer, which is used for outputting to stdout.
defaultWeightTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.LED24: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.classification.RDG1: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.regression.Expression: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.DataGenerator: Initializes the format for the dataset produced.
DefineFunction - Class in weka.core.pmml: Class encapsulating DefineFunction (used in TransformationDictionary).
DefineFunction(Element, TransformationDictionary) - Constructor for class weka.core.pmml.DefineFunction
degreeTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
del(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution: Deletes given instance from given bag.
delete(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Deletes an element from the set.
delete() - Method in class weka.core.Instances: Removes all instances from the set.
delete(int) - Method in class weka.core.Instances: Removes an instance at the given position from the set.
deleteArc(String, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Delete arc between two nodes.
deleteArc(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Delete arc between two nodes.
deleteAttribute() - Method in class weka.gui.arffviewer.ArffPanel: deletes the currently selected attribute
deleteAttribute(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: deletes the current selected Attribute or several chosen ones
deleteAttributeAt(int) - Method in class weka.core.Instance: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class weka.core.Instances: Deletes an attribute at the given position (0 to numAttributes() - 1).
deleteAttributeAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: deletes the attribute at the given col index
deleteAttributeAt(int) - Method in class weka.gui.arffviewer.ArffTableModel: deletes the attribute at the given col index.
deleteAttributeAt(int, boolean) - Method in class weka.gui.arffviewer.ArffTableModel: deletes the attribute at the given col index
deleteAttributes() - Method in class weka.gui.arffviewer.ArffPanel: deletes the chosen attributes
deleteAttributes(int[]) - Method in class weka.gui.arffviewer.ArffSortedTableModel: deletes the attributes at the given indices
deleteAttributes(int[]) - Method in class weka.gui.arffviewer.ArffTableModel: deletes the attributes at the given indices
deleteAttributeType(int) - Method in class weka.core.Instances: Deletes all attributes of the given type in the dataset.
deleteEmptyBinsTipText() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns the tip text for this property
deleteEvent(String) - Method in class weka.associations.gsp.Element: Deletes the first or last event of an Element.
deleteGraftedCases(Instances) - Method in class weka.classifiers.trees.j48.GraftSplit: deletes the cases in data that belong to leaf pointed to by the test (i.e.
deleteInfrequentSequences(FastVector, long) - Static method in class weka.associations.gsp.Sequence: Deletes Sequences of a given set which don't meet the minimum support count threshold.
deleteInstance() - Method in class weka.gui.arffviewer.ArffPanel: deletes the currently selected instance
deleteInstance(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: deletes the current selected Instance or several chosen ones
deleteInstanceAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: deletes the instance at the given index
deleteInstanceAt(int) - Method in class weka.gui.arffviewer.ArffTableModel: deletes the instance at the given index
deleteInstanceAt(int, boolean) - Method in class weka.gui.arffviewer.ArffTableModel: deletes the instance at the given index
deleteInstances() - Method in class weka.gui.arffviewer.ArffPanel: deletes all the currently selected instances
deleteInstances(int[]) - Method in class weka.gui.arffviewer.ArffSortedTableModel: deletes the instances at the given positions
deleteInstances(int[]) - Method in class weka.gui.arffviewer.ArffTableModel: deletes the instances at the given positions
deleteItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet: Deletes all item sets that don't have minimum support.
deleteItemSets(FastVector, int, int) - Static method in class weka.associations.LabeledItemSet: Deletes all item sets that don't have minimum support and have more than maximum support
deleteLastParent(Instances) - Method in class weka.classifiers.bayes.net.ParentSet: Delete last added parent from parent set and update internals (specifically the cardinality of the parent set)
deleteNode(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Delete node from the network, updating instances, parentsets, distributions Conditional distributions are condensed by taking the values for the target node to be its first value.
deleteNode(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Delete node from the network, updating instances, parentsets, distributions Conditional distributions are condensed by taking the values for the target node to be its first value.
deleteParent(int, Instances) - Method in class weka.classifiers.bayes.net.ParentSet: delete node from parent set
deleteSelection(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Delete nodes with indexes in selection from the network, updating instances, parentsets, distributions Conditional distributions are condensed by taking the values for the target node to be its first value.
deleteStringAttributes() - Method in class weka.core.Instances: Deletes all string attributes in the dataset.
deleteWithMissing(int) - Method in class weka.core.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(Attribute) - Method in class weka.core.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissingClass() - Method in class weka.core.Instances: Removes all instances with a missing class value from the dataset.
delimitersTipText() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer: Returns the tip text for this property
delNodeValue(int, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Delete node value from a node.
delRange(int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution: Deletes all instances in given range from given bag.
Delta - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Trust Region Radius
DeltaBeta - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Array to store Regression Coefficient updates.
DeltaR - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: This vector is used to store the increments on the R(i).
deltaTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
deltaTipText() - Method in class weka.associations.FPGrowth: Returns the tip text for this property
deltaTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
deltaTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
DeltaUpdate - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Trust Region Radius Update
DensityBasedClusterer - Interface in weka.clusterers: Interface for clusterers that can estimate the density for a given instance.
DensityBasedClustererSplitEvaluator - Class in weka.experiment: A SplitEvaluator that produces results for a density based clusterer.
DensityBasedClustererSplitEvaluator() - Constructor for class weka.experiment.DensityBasedClustererSplitEvaluator
densityBasedClustererTipText() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns a description of this option suitable for display as a tip text in the gui.
dependencies() - Method in class weka.core.Capabilities: Returns an Iterator over the stored dependencies
depth() - Method in class weka.gui.HierarchyPropertyParser: Get the depth of the tree, i.e.
DerivedFieldMetaInfo - Class in weka.core.pmml
DerivedFieldMetaInfo(Element, ArrayList<Attribute>, TransformationDictionary) - Constructor for class weka.core.pmml.DerivedFieldMetaInfo
descendantPopulationSizeTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
descendantPopulationSizeTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
description() - Method in class weka.associations.tertius.Predicate
description() - Method in class weka.core.Option: Returns the option's description.
deserialize(InputStream) - Static method in class weka.core.Jython: deserializes the Python Object from the stream
deSerialize(String) - Static method in class weka.core.xml.XStream: Deserializes an object from the supplied XML string
designatedClassTipText() - Method in class weka.classifiers.meta.ThresholdSelector
desiredSizeTipText() - Method in class weka.classifiers.meta.Decorate: Returns the tip text for this property
desiredWeightOfInstancesPerIntervalTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
det() - Method in class weka.core.matrix.LUDecomposition: Determinant
det() - Method in class weka.core.matrix.Matrix: Matrix determinant
detectionPerAttributeTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns the tip text for this property
determineBounds() - Method in class weka.gui.visualize.Plot2D: Determine the min and max values for axis and colouring attributes
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.AveragingResultProducer: Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.CSVResultListener: Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.DatabaseResultListener: Determines if there are any constraints (imposed by the destination) on any additional measures produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer: Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in interface weka.experiment.ResultListener: Determines if there are any constraints (imposed by the destination) on additional result columns to be produced by resultProducers.
determineValues(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: determines the values to retain, it is always at least 1 and up to the maximum number of distinct values
DIAMOND_SHAPE - Static variable in class weka.gui.visualize.Plot2D
differencesProbability - Variable in class weka.experiment.PairedStats: The probability of obtaining the observed differences
differencesSignificance - Variable in class weka.experiment.PairedStats: A significance indicator: 0 if the differences are not significant > 0 if x significantly greater than y < 0 if x significantly less than y
differencesStats - Variable in class weka.experiment.PairedStats: The stats associated with the paired differences
DIRECTED - Static variable in interface weka.gui.graphvisualizer.GraphConstants: Types of Edges
directionTipText() - Method in class weka.attributeSelection.BestFirst: Returns the tip text for this property
disable(Capabilities.Capability) - Method in class weka.core.Capabilities: disables the given capability Disabling NOMINAL_ATTRIBUTES also disables BINARY_ATTRIBUTES, UNARY_ATTRIBUTES and EMPTY_NOMINAL_ATTRIBUTES.
disable(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities: disables the given capability.
disableAll() - Method in class weka.core.Capabilities: disables all attribute and class types (including dependencies)
disableAllAttributeDependencies() - Method in class weka.core.Capabilities: disables all attribute type dependencies
disableAllAttributes() - Method in class weka.core.Capabilities: disables all attribute types
disableAllClassDependencies() - Method in class weka.core.Capabilities: disables all class type dependencies
disableAllClasses() - Method in class weka.core.Capabilities: disables all class types
disabled_getEquivalent() - Method in class weka.associations.Tertius: Get the value of equivalent.
disabled_getPartFile() - Method in class weka.associations.Tertius: Get the value of partFile.
disabled_getSameClause() - Method in class weka.associations.Tertius: Get the value of sameClause.
disabled_getSubsumption() - Method in class weka.associations.Tertius: Get the value of subsumption.
disabled_setEquivalent(boolean) - Method in class weka.associations.Tertius: Set the value of equivalent.
disabled_setPartFile(File) - Method in class weka.associations.Tertius: Set the value of partFile.
disabled_setSameClause(boolean) - Method in class weka.associations.Tertius: Set the value of sameClause.
disabled_setSubsumption(boolean) - Method in class weka.associations.Tertius: Set the value of subsumption.
disableDependency(Capabilities.Capability) - Method in class weka.core.Capabilities: disables the dependency of the given capability Disabling NOMINAL_ATTRIBUTES also disables BINARY_ATTRIBUTES, UNARY_ATTRIBUTES and EMPTY_NOMINAL_ATTRIBUTES.
disableNot(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities: disables the given "not to have" capability.
disconnect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection: Disconnects two units.
DISCONNECT - Static variable in class weka.gui.sql.event.ConnectionEvent: it was a disconnect
disconnectFromDatabase() - Method in class weka.experiment.DatabaseUtils: Closes the connection to the database.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractDataSink: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractEvaluator: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event named
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTestSetProducer: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Associator: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in interface weka.gui.beans.BeanCommon: Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.ClassAssigner: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Classifier: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.ClassValuePicker: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Clusterer: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in interface weka.gui.beans.ConnectionNotificationConsumer: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name This method should be implemented synchronized.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.CostBenefitAnalysis: Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Filter: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Loader: Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.MetaBean: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.PredictionAppender: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.SerializedModelSaver: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.StripChart: Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.TextViewer: Notify this object that it has been deregistered as a listener with a source for named event.
DiscreteEstimator - Class in weka.estimators: Simple symbolic probability estimator based on symbol counts.
DiscreteEstimator(int, boolean) - Constructor for class weka.estimators.DiscreteEstimator: Constructor
DiscreteEstimator(int, double) - Constructor for class weka.estimators.DiscreteEstimator: Constructor
DiscreteEstimatorBayes - Class in weka.classifiers.bayes.net.estimate: Symbolic probability estimator based on symbol counts and a prior.
DiscreteEstimatorBayes(int, double) - Constructor for class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Constructor
DiscreteEstimatorFullBayes - Class in weka.classifiers.bayes.net.estimate: Symbolic probability estimator based on symbol counts and a prior.
DiscreteEstimatorFullBayes(int, double, double, DiscreteEstimatorBayes, DiscreteEstimatorBayes, double) - Constructor for class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes: Constructor
DiscreteFunction - Class in weka.classifiers.functions.pace: Class for handling discrete functions.
DiscreteFunction() - Constructor for class weka.classifiers.functions.pace.DiscreteFunction: Constructs an empty discrete function
DiscreteFunction(DoubleVector) - Constructor for class weka.classifiers.functions.pace.DiscreteFunction: Constructs a discrete function with the point values provides and the function values are all 1/n.
DiscreteFunction(DoubleVector, DoubleVector) - Constructor for class weka.classifiers.functions.pace.DiscreteFunction: Constructs a discrete function with both the point values and function values provided.
Discretize - Class in weka.core.pmml: Class encapsulating a Discretize Expression.
Discretize(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.Discretize: Constructs a Discretize Expression
Discretize - Class in weka.filters.supervised.attribute: An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
Discretize() - Constructor for class weka.filters.supervised.attribute.Discretize: Constructor - initialises the filter
Discretize - Class in weka.filters.unsupervised.attribute: An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
Discretize() - Constructor for class weka.filters.unsupervised.attribute.Discretize: Constructor - initialises the filter
Discretize(String) - Constructor for class weka.filters.unsupervised.attribute.Discretize: Another constructor, sets the attribute indices immediately
discretizeBinTipText() - Method in class weka.classifiers.mi.MIBoost: Returns the tip text for this property
displayModelInOldFormatTipText() - Method in class weka.classifiers.bayes.NaiveBayes: Returns the tip text for this property
displayModelInOldFormatTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
displayResultset(int) - Method in class weka.experiment.PairedTTester: Checks whether the resultset with the given index shall be displayed.
displayResultset(int) - Method in interface weka.experiment.Tester: Checks whether the resultset with the given index shall be displayed.
displayRulesTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
displayStdDevsTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
dispose() - Method in class weka.gui.GUIChooser.ChildFrameSDI: de-registers the child frame with the parent first.
dispose() - Method in class weka.gui.Main.ChildFrameMDI: de-registers the child frame with the parent first.
dispose() - Method in class weka.gui.Main.ChildFrameSDI: de-registers the child frame with the parent first.
dispose() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
disposeSplash() - Static method in class weka.gui.SplashWindow: Closes the splash window.
distance(Instance, Instance) - Method in class weka.classifiers.mi.CitationKNN: distance between two instances
distance(DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Calculates the distance between dataObject and this.dataObject
distance(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Calculates the euclidian-distance between dataObject and this.dataObject
distance(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Calculates the manhattan-distance between dataObject and this.dataObject
distance(Instance, Instance, double, PerformanceStats) - Method in class weka.core.AbstractStringDistanceFunction: Calculates the distance between two instances.
distance(Instance, Instance) - Method in interface weka.core.DistanceFunction: Calculates the distance between two instances.
distance(Instance, Instance, PerformanceStats) - Method in interface weka.core.DistanceFunction: Calculates the distance between two instances.
distance(Instance, Instance, double) - Method in interface weka.core.DistanceFunction: Calculates the distance between two instances.
distance(Instance, Instance, double, PerformanceStats) - Method in interface weka.core.DistanceFunction: Calculates the distance between two instances.
distance(Instance, Instance) - Method in class weka.core.EuclideanDistance: Calculates the distance between two instances.
distance(Instance, Instance, PerformanceStats) - Method in class weka.core.EuclideanDistance: Calculates the distance (or similarity) between two instances.
distance(Instance, Instance) - Method in class weka.core.NormalizableDistance: Calculates the distance between two instances.
distance(Instance, Instance, PerformanceStats) - Method in class weka.core.NormalizableDistance: Calculates the distance between two instances.
distance(Instance, Instance, double) - Method in class weka.core.NormalizableDistance: Calculates the distance between two instances.
distance(Instance, Instance, double, PerformanceStats) - Method in class weka.core.NormalizableDistance: Calculates the distance between two instances.
distanceFTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
DistanceFunction - Interface in weka.core: Interface for any class that can compute and return distances between two instances.
distanceFunctionTipText() - Method in class weka.clusterers.HierarchicalClusterer
distanceFunctionTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property.
distanceFunctionTipText() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns the tip text for this property.
distanceIsBranchLengthTipText() - Method in class weka.clusterers.HierarchicalClusterer
distanceSet(Instance, Instance) - Method in class weka.classifiers.mi.CitationKNN: Calculates the distance between two instances
distanceWeightingTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property.
distinctCount - Variable in class weka.core.AttributeStats: The number of distinct values
distMultTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
distributedExperimentSelected() - Method in class weka.gui.experiment.DistributeExperimentPanel: Returns true if the distribute experiment checkbox is selected
DistributeExperimentPanel - Class in weka.gui.experiment: This panel enables an experiment to be distributed to multiple hosts; it also allows remote host names to be specified.
DistributeExperimentPanel() - Constructor for class weka.gui.experiment.DistributeExperimentPanel: Constructor
DistributeExperimentPanel(Experiment) - Constructor for class weka.gui.experiment.DistributeExperimentPanel: Creates the panel with the supplied initial experiment.
distribution() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the predicted probabilities
distribution() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns the distribution of class values induced by the model.
Distribution - Class in weka.classifiers.trees.j48: Class for handling a distribution of class values.
Distribution(int, int) - Constructor for class weka.classifiers.trees.j48.Distribution: Creates and initializes a new distribution.
Distribution(double[][]) - Constructor for class weka.classifiers.trees.j48.Distribution: Creates and initializes a new distribution using the given array.
Distribution(Instances) - Constructor for class weka.classifiers.trees.j48.Distribution: Creates a distribution with only one bag according to instances in source.
Distribution(Instances, ClassifierSplitModel) - Constructor for class weka.classifiers.trees.j48.Distribution: Creates a distribution according to given instances and split model.
Distribution(Distribution) - Constructor for class weka.classifiers.trees.j48.Distribution: Creates distribution with only one bag by merging all bags of given distribution.
Distribution(Distribution, int) - Constructor for class weka.classifiers.trees.j48.Distribution: Creates distribution with two bags by merging all bags apart of the indicated one.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.AODE: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.AODEsr: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.DMNBtext: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.HNB: Calculates the class membership probabilities for the given test instance
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayes: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesSimple: Calculates the class membership probabilities for the given test instance.
distributionForInstance(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Calculates the class membership probabilities for the given test instance.
distributionForInstance(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Calculates the class membership probabilities for the given test instance.
distributionForInstance(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.WAODE: Calculates the class membership probabilities for the given test instance
distributionForInstance(Instance) - Method in class weka.classifiers.Classifier: Predicts the class memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.LibLINEAR: Computes the distribution for a given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.LibSVM: Computes the distribution for a given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.Logistic: Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.functions.MultilayerPerceptron: Call this function to predict the class of an instance once a classification model has been built with the buildClassifier call.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.RBFNetwork: Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.functions.SimpleLogistic: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.SMO: Estimates class probabilities for given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.SPegasos: Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.functions.VotedPerceptron: Outputs the distribution for the given output.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.IBk: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.KStar: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.LBR: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.LWL: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.AdaBoostM1: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Classifies a given instance after attribute selection
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Bagging: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.ClassificationViaRegression: Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns class probabilities.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.CVParameterSelection: Predicts the class distribution for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Dagging: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Decorate: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.END: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.FilteredClassifier: Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Grading: Returns class probabilities for a given instance using the stacked classifier.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.GridSearch: Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.meta.LogitBoost: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MetaCost: Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiClassClassifier: Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiScheme: Returns class probabilities.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Predicts the class distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Predicts the class distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.meta.nestedDichotomies.ND: Predicts the class distribution for a given instance
distributionForInstance(Instance) - Method in class weka.classifiers.meta.OrdinalClassClassifier: Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Computes class distribution of an instance using the best committee.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RandomCommittee: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RandomSubSpace: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RotationForest: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Stacking: Returns class probabilities.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.StackingC: Classifies a given instance using the stacked classifier.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.ThresholdSelector: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Vote: Classifies a given instance using the selected combination rule.
distributionForInstance(Instance) - Method in class weka.classifiers.mi.CitationKNN: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MDD: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIBoost: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIDD: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIEMDD: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MILR: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIOptimalBall: Computes the distribution for a given multiple instance
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MISMO: Estimates class probabilities for given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MISVM: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.MIWrapper: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.mi.SimpleMI: Computes the distribution for a given exemplar
distributionForInstance(Instance) - Method in class weka.classifiers.misc.HyperPipes: Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.misc.SerializedClassifier: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.misc.VFI: Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.pmml.consumer.GeneralRegression: Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.pmml.consumer.NeuralNetwork: Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.pmml.consumer.Regression: Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.ConjunctiveRule: Computes class distribution for the given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.DecisionTable: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.DTNB: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.JRip: Classify the test instance with the rule learner and provide the class distributions
distributionForInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList: Returns class probabilities for a weighted instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.PART: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.part.MakeDecList: Returns the class distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.ZeroR: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ADTree: Returns the class probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.BFTree: Computes class probabilities for instance using the decision tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.DecisionStump: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.FT: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTInnerNode: Returns the class probabilities for an instance given by the Functional tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTLeavesNode: Returns the class probabilities for an instance given by the Functional Leaves tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTNode: Returns the class probabilities for an instance given by the Functional Tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTtree: Returns the class probabilities for an instance given by the Functional tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.Id3: Computes class distribution for instance using decision tree.
distributionForInstance(Instance, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns class probabilities for a weighted instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.J48: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.J48graft: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.LADTree: Returns the class probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.LMT: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the class probabilities for an instance given by the logistic model tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.NBTree: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.RandomForest: Returns the class probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.RandomTree: Computes class distribution of an instance using the tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.REPTree: Computes class distribution of an instance using the tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.SimpleCart: Computes class probabilities for instance using the decision tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.UserClassifier: Call this function to get a double array filled with the probability of how likely each class type is the class of the instance.
distributionForInstance(Instance) - Method in class weka.clusterers.AbstractClusterer: Predicts the cluster memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.clusterers.AbstractDensityBasedClusterer: Returns the cluster probability distribution for an instance.
distributionForInstance(Instance) - Method in interface weka.clusterers.Clusterer: Predicts the cluster memberships for a given instance.
distributionForInstance(Instance) - Method in interface weka.clusterers.DensityBasedClusterer: Returns the cluster probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.clusterers.FilteredClusterer: Classifies a given instance after filtering.
distributionForInstance(Instance) - Method in class weka.clusterers.HierarchicalClusterer
distributionsByOriginalIndex(double[]) - Method in class weka.filters.supervised.attribute.ClassOrder: Convert the given class distribution back to the distributions with the original internal class index
distributionSpreadTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns the tip text for this property
distributionTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
divergence(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader: calculates the divergence between the probability distribution represented by this network and that of another, that is, \sum_{x\in X} P(x)log P(x)/Q(x) where X is the set of values the nodes in the network can take, P(x) the probability of this network for configuration x Q(x) the probability of the other network for configuration x
divide(Instances, boolean) - Static method in class weka.associations.LabeledItemSet: Splits the class attribute away.
dividedBy(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Divided by another DoubleVector element by element
dividedByEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Divided by another DoubleVector element by element in place
DIVISION - Static variable in interface weka.core.mathematicalexpression.sym
DIVISION - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
DKConditionalEstimator - Class in weka.estimators: Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DKConditionalEstimator(int, double) - Constructor for class weka.estimators.DKConditionalEstimator: Constructor
dl(int) - Method in class weka.core.Debug.DBO: Return true if the debug level is set same method as outpuTypeSet but better name
DMNBtext - Class in weka.classifiers.bayes: Class for building and using a Discriminative Multinomial Naive Bayes classifier.
DMNBtext() - Constructor for class weka.classifiers.bayes.DMNBtext
DMNBtext.DNBBinary - Class in weka.classifiers.bayes
DNBBinary() - Constructor for class weka.classifiers.bayes.DMNBtext.DNBBinary
DNConditionalEstimator - Class in weka.estimators: Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DNConditionalEstimator(int, double) - Constructor for class weka.estimators.DNConditionalEstimator: Constructor
dnorm(double) - Static method in class weka.core.matrix.Maths: Returns the density of the standard normal.
dnorm(double, double, double) - Static method in class weka.core.matrix.Maths: Returns the density value of a standard normal.
dnorm(double, DoubleVector, double) - Static method in class weka.core.matrix.Maths: Returns the density values of a set of normal distributions with different means.
dnormLog(double) - Static method in class weka.core.matrix.Maths: Returns the log-density of the standard normal.
dnormLog(double, double, double) - Static method in class weka.core.matrix.Maths: Returns the log-density value of a standard normal.
dnormLog(double, DoubleVector, double) - Static method in class weka.core.matrix.Maths: Returns the log-density values of a set of normal distributions with different means.
do_action(int, lr_parser, Stack, int) - Method in class weka.core.mathematicalexpression.Parser: Invoke a user supplied parse action.
do_action(int, lr_parser, Stack, int) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Invoke a user supplied parse action.
doCommandlineCompletion(KeyEvent) - Method in class weka.gui.SimpleCLIPanel: performs commandline completion on packages and classnames.
DOCTYPE - Static variable in class weka.core.xml.XMLInstances: the DTD
DOCTYPE - Static variable in class weka.core.xml.XMLOptions: the DTD for the XML file.
DOCTYPE - Static variable in class weka.core.xml.XMLSerialization: the DOCTYPE for the serialization
doGrafting(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Initializes variables for grafting.
doHistory(KeyEvent) - Method in class weka.gui.SimpleCLIPanel: Changes the currently displayed command line when certain keys are pressed.
doMetaConnection(BeanInstance, BeanInstance, EventSetDescriptor, JComponent) - Static method in class weka.gui.beans.BeanConnection
done() - Method in interface weka.classifiers.IterativeClassifier: Signal end of iterating, useful for any house-keeping/cleanup
done() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Signal that a scoring run has been completed.
done() - Method in class weka.classifiers.trees.ADTree: Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.
done() - Method in class weka.classifiers.trees.LADTree
doNotOperateOnPerClassBasisTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
doNotReplaceMissingValuesTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
doNotReplaceMissingValuesTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
doNotWeightBagsTipText() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Returns the tip text for this property
dontFilterAfterFirstBatchTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property.
dontNormalizeTipText() - Method in class weka.classifiers.functions.SPegasos: Returns the tip text for this property
dontNormalizeTipText() - Method in class weka.core.NormalizableDistance: Returns the tip text for this property.
dontReplaceMissingTipText() - Method in class weka.classifiers.functions.SPegasos: Returns the tip text for this property
dontReplaceMissingValuesTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
doRun(int) - Method in class weka.experiment.AveragingResultProducer: Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.CrossValidationResultProducer: Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.DatabaseResultProducer: Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.LearningRateResultProducer: Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.RandomSplitResultProducer: Gets the results for a specified run number.
doRun(int) - Method in interface weka.experiment.ResultProducer: Gets the results for a specified run number.
doRunKeys(int) - Method in class weka.experiment.AveragingResultProducer: Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.CrossValidationResultProducer: Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.DatabaseResultProducer: Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.LearningRateResultProducer: Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.RandomSplitResultProducer: Gets the keys for a specified run number.
doRunKeys(int) - Method in interface weka.experiment.ResultProducer: Gets the keys for a specified run number.
doTests() - Method in class weka.associations.CheckAssociator: Begin the tests, reporting results to System.out
doTests() - Method in class weka.attributeSelection.CheckAttributeSelection: Begin the tests, reporting results to System.out
doTests() - Method in class weka.classifiers.CheckClassifier: Begin the tests, reporting results to System.out
doTests() - Method in class weka.classifiers.functions.supportVector.CheckKernel: Begin the tests, reporting results to System.out
doTests() - Method in class weka.clusterers.CheckClusterer: Begin the tests, reporting results to System.out
doTests() - Method in class weka.core.Check: Begin the tests, reporting results to System.out
doTests() - Method in class weka.core.CheckGOE: Runs some diagnostic tests on the object.
doTests() - Method in class weka.core.CheckOptionHandler: Runs some diagnostic tests on an optionhandler object.
doTests() - Method in class weka.core.CheckScheme: Begin the tests, reporting results to System.out
doTests() - Method in class weka.estimators.CheckEstimator: Begin the tests, reporting results to System.out
dotMultiply(AlgVector) - Method in class weka.core.AlgVector: Returns the inner (or dot) product of two vectors
DotParser - Class in weka.gui.graphvisualizer: This class parses input in DOT format, and builds the datastructures that are passed to it.
DotParser(Reader, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.DotParser: Dot parser Constructor
DOUBLE - Static variable in class weka.experiment.DatabaseUtils: Type mapping for DOUBLE used for reading experiment results.
DOUBLE - Static variable in interface weka.gui.graphvisualizer.GraphConstants: Types of Edges
doubleToString(double, int) - Static method in class weka.core.Utils: Rounds a double and converts it into String.
doubleToString(double, int, int) - Static method in class weka.core.Utils: Rounds a double and converts it into a formatted decimal-justified String.
DoubleVector - Class in weka.core.matrix: A vector specialized on doubles.
DoubleVector() - Constructor for class weka.core.matrix.DoubleVector: Constructs a null vector.
DoubleVector(int) - Constructor for class weka.core.matrix.DoubleVector: Constructs an n-vector of zeros.
DoubleVector(int, double) - Constructor for class weka.core.matrix.DoubleVector: Constructs a constant n-vector.
DoubleVector(double[]) - Constructor for class weka.core.matrix.DoubleVector: Constructs a vector directly from a double array
dp(String) - Method in class weka.core.Debug.DBO: prints out text if verbose is on.
dp(int, String) - Method in class weka.core.Debug.DBO: prints out text but only if debug level is set.
dpln(String) - Method in class weka.core.Debug.DBO: prints out text + endofline if verbose is on.
dpln(int, String) - Method in class weka.core.Debug.DBO: prints out text + endofline but only if parameter debug type is set.
draw(Shape) - Method in class weka.gui.visualize.PostscriptGraphics
draw3DRect(int, int, int, int, boolean) - Method in class weka.gui.visualize.PostscriptGraphics: Draw an outlined rectangle with 3D effect in current pen color.
Drawable - Interface in weka.core: Interface to something that can be drawn as a graph.
drawArc(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
drawBytes(byte[], int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: simply calls drawString(String,int,int)
drawChars(char[], int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: simply calls drawString(String,int,int)
drawGlyphVector(GlyphVector, float, float) - Method in class weka.gui.visualize.PostscriptGraphics
drawHighlight(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to draw the node highlighted.
drawImage(Image, int, int, Color, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics: calls drawImage(Image,int,int,int,int,Color,ImageObserver)
drawImage(Image, int, int, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics: calls drawImage(Image,int,int,Color,ImageObserver) with Color.WHITE as background color
drawImage(Image, int, int, int, int, Color, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics: PS see http://astronomy.swin.edu.au/~pbourke/geomformats/postscript/ Java http://show.docjava.com:8086/book/cgij/doc/ip/graphics/SimpleImageFrame.java.html
drawImage(Image, int, int, int, int, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics: calls drawImage(Image,int,int,int,int,Color,ImageObserver) with the color WHITE as background
drawImage(Image, int, int, int, int, int, int, int, int, Color, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
drawImage(Image, int, int, int, int, int, int, int, int, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics: calls drawImage(Image,int,int,int,int,int,int,int,int,Color,ImageObserver) with Color.WHITE as background color
drawImage(BufferedImage, BufferedImageOp, int, int) - Method in class weka.gui.visualize.PostscriptGraphics
drawImage(Image, AffineTransform, ImageObserver) - Method in class weka.gui.visualize.PostscriptGraphics
drawInputLines(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to draw the nodes input connections.
drawLine(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Draw a line in current pen color.
drawNode(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to draw the node.
drawOutputLines(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to draw the nodes output connections.
drawOval(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Draw an Oval outline in current pen color.
drawPolygon(int[], int[], int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
drawPolyline(int[], int[], int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
drawRect(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Draw an outlined rectangle in current pen color.
drawRenderableImage(RenderableImage, AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
drawRenderedImage(RenderedImage, AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
drawRoundRect(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
drawString(AttributedCharacterIterator, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
drawString(String, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Draw text in current pen color.
drawString(AttributedCharacterIterator, float, float) - Method in class weka.gui.visualize.PostscriptGraphics
drawString(String, float, float) - Method in class weka.gui.visualize.PostscriptGraphics
DTD_ANY - Static variable in class weka.core.xml.XMLDocument: the ANY placeholder.
DTD_AT_LEAST_ONE - Static variable in class weka.core.xml.XMLDocument: the at least one marker.
DTD_ATTLIST - Static variable in class weka.core.xml.XMLDocument: the AttList definition.
DTD_CDATA - Static variable in class weka.core.xml.XMLDocument: the CDATA placeholder.
DTD_DOCTYPE - Static variable in class weka.core.xml.XMLDocument: the DocType definition.
DTD_ELEMENT - Static variable in class weka.core.xml.XMLDocument: the Element definition.
DTD_IMPLIED - Static variable in class weka.core.xml.XMLDocument: the #IMPLIED placeholder.
DTD_OPTIONAL - Static variable in class weka.core.xml.XMLDocument: the optional marker.
DTD_PCDATA - Static variable in class weka.core.xml.XMLDocument: the #PCDATA placeholder.
DTD_REQUIRED - Static variable in class weka.core.xml.XMLDocument: the #REQUIRED placeholder.
DTD_SEPARATOR - Static variable in class weka.core.xml.XMLDocument: the option separator.
DTD_ZERO_OR_MORE - Static variable in class weka.core.xml.XMLDocument: the zero or more marker.
DTNB - Class in weka.classifiers.rules: Class for building and using a decision table/naive bayes hybrid classifier.
DTNB() - Constructor for class weka.classifiers.rules.DTNB
DUMMY_STRING_VAL - Static variable in class weka.core.Attribute: Dummy first value for String attributes (useful for sparse instances)
dumpDistribution() - Method in class weka.classifiers.trees.j48.Distribution: Prints distribution.
dumpLabel(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Prints label for subset index of instances (eg class).
dumpLabelG(int, Instances) - Method in class weka.classifiers.trees.j48.GraftSplit: Prints label for subset index of instances (eg class).
dumpModel(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Prints the split model.

E

EAST_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
Edge - Class in weka.gui.treevisualizer: This class is used in conjunction with the Node class to form a tree structure.
Edge(String, String, String) - Constructor for class weka.gui.treevisualizer.Edge: This constructs an Edge with the specified label and parent , child serial tags.
edit() - Method in class weka.gui.explorer.PreprocessPanel: edits the current instances object in the viewer
EditableBayesNet - Class in weka.classifiers.bayes.net: Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier.
EditableBayesNet() - Constructor for class weka.classifiers.bayes.net.EditableBayesNet: standard constructor *
EditableBayesNet(Instances) - Constructor for class weka.classifiers.bayes.net.EditableBayesNet: constructor, creates empty network with nodes based on the attributes in a data set
EditableBayesNet(BIFReader) - Constructor for class weka.classifiers.bayes.net.EditableBayesNet: constructor, copies Bayesian network structure from a Bayesian network encapsulated in a BIFReader
EditableBayesNet(boolean) - Constructor for class weka.classifiers.bayes.net.EditableBayesNet: constructor that potentially initializes instances as well
editableProperties() - Method in class weka.gui.PropertySheetPanel: Gets the number of editable properties for the current target.
EditDistance - Class in weka.core: Computes the Levenshtein edit distance between two strings.
EditDistance() - Constructor for class weka.core.EditDistance
EditDistance(Instances) - Constructor for class weka.core.EditDistance
eig() - Method in class weka.core.matrix.Matrix: Eigenvalue Decomposition
EigenvalueDecomposition - Class in weka.core.matrix: Eigenvalues and eigenvectors of a real matrix.
eigenvalueDecomposition(double[][], double[]) - Method in class weka.core.Matrix: Deprecated.

Performs Eigenvalue Decomposition using Householder QR Factorization Matrix must be symmetrical.
EigenvalueDecomposition(Matrix) - Constructor for class weka.core.matrix.EigenvalueDecomposition: Check for symmetry, then construct the eigenvalue decomposition
Element - Class in weka.associations.gsp: Class representing an Element, i.e., a set of events/items.
Element() - Constructor for class weka.associations.gsp.Element: Constructor
Element(int) - Constructor for class weka.associations.gsp.Element: Constructor accepting an initial size of the events Array as parameter.
element(int) - Method in class weka.core.neighboursearch.covertrees.Stack: Returns the ith element in the stack.
elementAt(int) - Method in class weka.core.FastVector: Returns the element at the given position.
elementAt(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Returns the component at the specified index.
elements() - Method in class weka.core.FastVector: Returns an enumeration of this vector.
elements(int) - Method in class weka.core.FastVector: Returns an enumeration of this vector, skipping the element with the given index.
elements - Variable in class weka.core.neighboursearch.covertrees.Stack: The elements inside the stack.
elements() - Method in class weka.core.Stopwords: Returns a sorted enumeration over all stored stopwords
eliminateColinearAttributesTipText() - Method in class weka.classifiers.functions.LinearRegression: Returns the tip text for this property
EM - Class in weka.clusterers: Simple EM (expectation maximisation) class.

EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
EM() - Constructor for class weka.clusterers.EM: Constructor.
empiricalBayesEstimate(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Returns the empirical Bayes estimate of a single value.
empiricalBayesEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Returns the empirical Bayes estimate of a vector.
empiricalProbability(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Computes the empirical probabilities of the data over a set of intervals.
empty() - Method in class weka.core.Queue: Checks if queue is empty.
enable(Capabilities.Capability) - Method in class weka.core.Capabilities: enables the given capability.
enable(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities: enables the given capability.
enableAll() - Method in class weka.core.Capabilities: enables all attribute and class types (including dependencies)
enableAllAttributeDependencies() - Method in class weka.core.Capabilities: enables all attribute type dependencies
enableAllAttributes() - Method in class weka.core.Capabilities: enables all attribute types
enableAllClassDependencies() - Method in class weka.core.Capabilities: enables all class type dependencies
enableAllClasses() - Method in class weka.core.Capabilities: enables all class types
enableDependency(Capabilities.Capability) - Method in class weka.core.Capabilities: enables the dependency flag for the given capability Enabling NOMINAL_ATTRIBUTES also enables BINARY_ATTRIBUTES, UNARY_ATTRIBUTES and EMPTY_NOMINAL_ATTRIBUTES.
enableNot(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities: enables the given "not to have" capability.
enclosureCharactersTipText() - Method in class weka.core.converters.CSVLoader: Returns the tip text for this property.
END - Class in weka.classifiers.meta: A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies.

For more info, check

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
END() - Constructor for class weka.classifiers.meta.END: Constructor.
entropicAutoBlendTipText() - Method in class weka.classifiers.lazy.KStar: Returns the tip text for this property
ENTROPY - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
entropy(double[]) - Static method in class weka.core.ContingencyTables: Computes the entropy of the given array.
EntropyBasedSplitCrit - Class in weka.classifiers.trees.j48: "Abstract" class for computing splitting criteria based on the entropy of a class distribution.
EntropyBasedSplitCrit() - Constructor for class weka.classifiers.trees.j48.EntropyBasedSplitCrit
entropyConditionedOnColumns(double[][]) - Static method in class weka.core.ContingencyTables: Computes conditional entropy of the rows given the columns.
entropyConditionedOnRows(double[][]) - Static method in class weka.core.ContingencyTables: Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double) - Static method in class weka.core.ContingencyTables: Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyGain() - Method in class weka.classifiers.trees.lmt.ResidualSplit: Computes entropy gain for current split.
entropyOverColumns(double[][]) - Static method in class weka.core.ContingencyTables: Computes the columns' entropy for the given contingency table.
entropyOverRows(double[][]) - Static method in class weka.core.ContingencyTables: Computes the rows' entropy for the given contingency table.
EntropySplitCrit - Class in weka.classifiers.trees.j48: Class for computing the entropy for a given distribution.
EntropySplitCrit() - Constructor for class weka.classifiers.trees.j48.EntropySplitCrit
enumerateAttributes() - Method in class weka.core.Instance: Returns an enumeration of all the attributes.
enumerateAttributes() - Method in class weka.core.Instances: Returns an enumeration of all the attributes.
enumerateInstances() - Method in class weka.core.Instances: Returns an enumeration of all instances in the dataset.
enumerateLiterals() - Method in class weka.associations.tertius.LiteralSet: Enumerate the literals contained in this set.
enumerateMeasures() - Method in class weka.classifiers.bayes.BayesNet: Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.functions.SimpleLogistic: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.functions.SMOreg: Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.lazy.IBk: Returns an enumeration of the additional measure names produced by the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
enumerateMeasures() - Method in class weka.classifiers.lazy.LWL: Returns an enumeration of the additional measure names produced by the neighbour search algorithm.
enumerateMeasures() - Method in class weka.classifiers.meta.AdditiveRegression: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.meta.Bagging: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.meta.GridSearch: Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.rules.DecisionTable: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.DTNB: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.JRip: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.PART: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.Ridor: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.ADTree: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.BFTree: Return an enumeration of the measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.FT: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.J48: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.J48graft: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.LADTree: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.LMT: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.m5.M5Base: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.NBTree: Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.trees.RandomForest: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.REPTree: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.SimpleCart: Return an enumeration of the measure names.
enumerateMeasures() - Method in interface weka.core.AdditionalMeasureProducer: Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.BallTree: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.CoverTree: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.KDTree: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.PerformanceStats: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.experiment.AveragingResultProducer: Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns an enumeration of any additional measure names that might be in the classifier
enumerateMeasures() - Method in class weka.experiment.CrossValidationResultProducer: Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.DatabaseResultProducer: Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Returns an enumeration of any additional measure names that might be in the classifier
enumerateMeasures() - Method in class weka.experiment.LearningRateResultProducer: Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.RandomSplitResultProducer: Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.RegressionSplitEvaluator: Returns an enumeration of any additional measure names that might be in the classifier
enumerateRequests() - Method in class weka.gui.beans.Associator: Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.AttributeSummarizer: Return an enumeration of actions that the user can ask this bean to perform
enumerateRequests() - Method in class weka.gui.beans.Classifier: Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Return an enumeration of user activated requests for this bean
enumerateRequests() - Method in class weka.gui.beans.Clusterer: Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Return an enumeration of user activated requests for this bean
enumerateRequests() - Method in class weka.gui.beans.CostBenefitAnalysis
enumerateRequests() - Method in class weka.gui.beans.CrossValidationFoldMaker: Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.DataVisualizer: Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.Filter: Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.GraphViewer: Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.MetaBean: Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.ModelPerformanceChart: Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.StripChart: Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.TextViewer: Get a list of user requests
enumerateRequests() - Method in class weka.gui.beans.TrainTestSplitMaker: Get list of user requests
enumerateRequests() - Method in interface weka.gui.beans.UserRequestAcceptor: Get a list of performable requests
enumerateValues() - Method in class weka.core.Attribute: Returns an enumeration of all the attribute's values if the attribute is nominal, string, or relation-valued, null otherwise.
Environment - Class in weka.core: This class encapsulates a map of all environment and java system properties.
Environment() - Constructor for class weka.core.Environment
EnvironmentHandler - Interface in weka.core: Interface for something that can utilize environment variables.
EOF - Static variable in interface weka.core.mathematicalexpression.sym
EOF - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
EOF_sym() - Method in class weka.core.mathematicalexpression.Parser: EOF Symbol index.
EOF_sym() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: EOF Symbol index.
epochsTipText() - Method in class weka.classifiers.functions.SPegasos: Returns the tip text for this property
EPSILON - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
epsilonParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
epsilonParameterTipText() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Returns the tip text for this property
EpsilonRange_ListElement - Class in weka.clusterers.forOPTICSAndDBScan.Utils: EpsilonRange_ListElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Sep 7, 2004
Time: 2:12:34 PM
$ Revision 1.4 $
EpsilonRange_ListElement(double, DataObject) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement: Constructs a new Element that is stored in the ArrayList which is built in the k_nextNeighbourQuery-method from a specified database.
epsilonRangeQuery(double, DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Performs an epsilon range query for this dataObject
epsilonRangeQuery(double, DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Performs an epsilon range query for this dataObject
epsilonTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
epsilonTipText() - Method in class weka.classifiers.functions.supportVector.RegSMO: Returns the tip text for this property
epsilonTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
epsilonTipText() - Method in class weka.clusterers.DBSCAN: Returns the tip text for this property
epsilonTipText() - Method in class weka.clusterers.OPTICS: Returns the tip text for this property
epsTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
epsTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
EQ - Static variable in interface weka.core.mathematicalexpression.sym
eq(double, double) - Static method in class weka.core.Utils: Tests if a is equal to b.
EQ - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
equalCondset(Object) - Method in class weka.associations.LabeledItemSet: Compares two item sets
equalExemplars(Instance, Instance) - Method in class weka.classifiers.mi.CitationKNN: Wether the instances of two exemplars are or are not equal
equalHeaders(Instance) - Method in class weka.core.Instance: Tests if the headers of two instances are equivalent.
equalHeaders(Instances) - Method in class weka.core.Instances: Checks if two headers are equivalent.
equals(Object) - Method in class weka.associations.AssociatorEvaluation: Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.associations.FPGrowth.AssociationRule: Return true if this rule is equal to the supplied one.
equals(Object) - Method in class weka.associations.FPGrowth.BinaryItem
equals(Object) - Method in class weka.associations.gsp.Element: Checks if two Elements are equal.
equals(Object) - Method in class weka.associations.gsp.Sequence: Checks if two Sequences are equal.
equals(Object) - Method in class weka.associations.ItemSet: Tests if two item sets are equal.
equals(Object) - Method in class weka.associations.LabeledItemSet: Tests if two item sets are equal.
equals(Object) - Method in class weka.associations.RuleItem: returns whether two RuleItems are equal
equals(Object) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Tests if two instances are equal
equals(Object) - Method in class weka.classifiers.Evaluation: Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation: Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.classifiers.rules.DecisionTableHashKey: Tests if two instances are equal
equals(Object) - Method in class weka.clusterers.ClusterEvaluation: Tests whether the current evaluation object is equal to another evaluation object
equals(DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Compares two DataObjects in respect to their attribute-values
equals(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Compares two DataObjects in respect to their attribute-values
equals(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Compares two DataObjects in respect to their attribute-values
equals(Object) - Method in class weka.core.Attribute: Tests if given attribute is equal to this attribute.
equals(Object) - Method in class weka.core.AttributeLocator: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class weka.core.ClassDiscovery.StringCompare: Indicates whether some other object is "equal to" this Comparator.
equals(Object) - Method in class weka.core.SelectedTag: Returns true if this SelectedTag equals another object
equals(Object) - Method in class weka.core.SerializedObject
equals(Object) - Method in class weka.core.Trie: Compares the specified object with this collection for equality.
equals(Object) - Method in class weka.core.Trie.TrieNode: Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class weka.core.Version: whether the given version string is equal to this version
equals(Object) - Method in class weka.estimators.Estimator: Tests whether the current estimation object is equal to another estimation object
equals(Object) - Method in class weka.gui.graphvisualizer.GraphEdge
equals(Object) - Method in class weka.gui.graphvisualizer.GraphNode: Returns true if passed in argument is an instance of GraphNode and is equal to this node.
equals(Object) - Method in class weka.gui.SortedTableModel.SortContainer: Indicates whether some other object is "equal to" this one.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.Splitter: Tests whether two splitters are equivalent.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Tests whether two splitters are equivalent.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Tests whether two splitters are equivalent.
equalTo(Test) - Method in class weka.datagenerators.Test: Compares the test with the test that is given as parameter.
equivalentTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
equivalentTo(Rule) - Method in class weka.associations.tertius.Rule: Test if this rule is equivalent to another rule.
errms(StreamTokenizer, String) - Static method in class weka.core.converters.ConverterUtils: Throws error message with line number and last token read.
error() - Method in class weka.classifiers.evaluation.NumericPrediction: Calculates the prediction error.
error - Static variable in interface weka.core.mathematicalexpression.sym
error - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
ERROR_SHAPE - Static variable in class weka.gui.visualize.Plot2D
error_sym() - Method in class weka.core.mathematicalexpression.Parser: error Symbol index.
error_sym() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: error Symbol index.
ErrorBasedMeritEvaluator - Interface in weka.attributeSelection: Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme
errorFunction(double) - Static method in class weka.core.Statistics: Returns the error function of the normal distribution.
errorFunctionComplemented(double) - Static method in class weka.core.Statistics: Returns the complementary Error function of the normal distribution.
errorOnProbabilitiesTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
errorOnProbabilitiesTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
errorOnProbabilitiesTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
errorRate() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Returns the estimated error rate.
errorRate() - Method in class weka.classifiers.Evaluation: Returns the estimated error rate or the root mean squared error (if the class is numeric).
errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit: This function calculates what the error value should be.
errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to get the error value of this unit.
errorValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod: This function calculates what the error value should be.
errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to get the error value of this unit.
errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.SigmoidUnit: This function calculates what the error value should be.
ErrorVisualizePlugin - Interface in weka.gui.visualize.plugins: Interface implemented by classes loaded dynamically to visualize classifier errors in the explorer.
estimateCPTs() - Method in class weka.classifiers.bayes.BayesNet: estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimateCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimateCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimateCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimateCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator: estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
estimatePrior() - Method in class weka.associations.PriorEstimation: Method to estimate the prior probabilities
Estimator - Class in weka.estimators: Abstract class for all estimators.
Estimator() - Constructor for class weka.estimators.Estimator
estimatorTipText() - Method in class weka.classifiers.bayes.BayesNet: This will return a string describing the BayesNetEstimator.
estimatorTipText() - Method in class weka.classifiers.functions.PaceRegression: Returns the tip text for this property
EstimatorUtils - Class in weka.estimators: Contains static utility functions for Estimators.
EstimatorUtils() - Constructor for class weka.estimators.EstimatorUtils
EstTypes() - Constructor for class weka.estimators.CheckEstimator.EstTypes: Constructor
EstTypes(boolean, boolean, boolean) - Constructor for class weka.estimators.CheckEstimator.EstTypes: Constructor
EuclideanDataObject - Class in weka.clusterers.forOPTICSAndDBScan.DataObjects: EuclideanDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:50:22 PM
$ Revision 1.4 $
EuclideanDataObject(Instance, String, Database) - Constructor for class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Constructs a new DataObject.
EuclideanDistance - Class in weka.core: Implementing Euclidean distance (or similarity) function.

One object defines not one distance but the data model in which the distances between objects of that data model can be computed.

Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.

For more information, see:

Wikipedia.
EuclideanDistance() - Constructor for class weka.core.EuclideanDistance: Constructs an Euclidean Distance object, Instances must be still set.
EuclideanDistance(Instances) - Constructor for class weka.core.EuclideanDistance: Constructs an Euclidean Distance object and automatically initializes the ranges.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.CachedKernel: Implements the abstract function of Kernel using the cache.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.Kernel: Computes the result of the kernel function for two instances.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel: Computes the result of the kernel function for two instances.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.StringKernel: Computes the result of the kernel function for two instances.
EVAL_ACCURACY - Static variable in class weka.classifiers.rules.DecisionTable
EVAL_AUC - Static variable in class weka.classifiers.rules.DecisionTable
EVAL_CROSS_VALIDATION - Static variable in class weka.classifiers.meta.ThresholdSelector: n-fold cross-validation
EVAL_DEFAULT - Static variable in class weka.classifiers.rules.DecisionTable: default is accuracy for discrete class and RMSE for numeric class
EVAL_MAE - Static variable in class weka.classifiers.rules.DecisionTable
EVAL_RMSE - Static variable in class weka.classifiers.rules.DecisionTable
EVAL_TRAINING_SET - Static variable in class weka.classifiers.meta.ThresholdSelector: entire training set
EVAL_TUNED_SPLIT - Static variable in class weka.classifiers.meta.ThresholdSelector: single tuned fold
evalBoolean(String) - Method in class weka.core.xml.XMLDocument: Evaluates and returns the boolean result of the XPath expression.
evalDouble(String) - Method in class weka.core.xml.XMLDocument: Evaluates and returns the double result of the XPath expression.
evalString(String) - Method in class weka.core.xml.XMLDocument: Evaluates and returns the boolean result of the XPath expression.
evaluate(String, String[]) - Static method in class weka.associations.AssociatorEvaluation: Evaluates an associator with the options given in an array of strings.
evaluate(Associator, String[]) - Static method in class weka.associations.AssociatorEvaluation: Evaluates the associator with the given commandline options and returns the evaluation string.
evaluate(Associator, Instances) - Method in class weka.associations.AssociatorEvaluation: Evaluates the associator with the given commandline options and returns the evaluation string.
evaluate(Kernel, String[]) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation: Evaluates the Kernel with the given commandline options and returns the evaluation string.
evaluate(String, String[]) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation: Evaluates a kernel with the options given in an array of strings.
evaluate(Kernel, Instances) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation: Evaluates the Kernel with the given commandline options and returns the evaluation string.
evaluate(String, HashMap) - Static method in class weka.core.MathematicalExpression: Parses and evaluates the given expression.
evaluateAttribute(int) - Method in interface weka.attributeSelection.AttributeEvaluator: evaluates an individual attribute
evaluateAttribute(int) - Method in class weka.attributeSelection.AttributeSetEvaluator: evaluates an individual attribute
evaluateAttribute(int[], int[]) - Method in class weka.attributeSelection.AttributeSetEvaluator: Evaluates a set of attributes
evaluateAttribute(int) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: evaluates an individual attribute by measuring its chi-squared value.
evaluateAttribute(int) - Method in class weka.attributeSelection.CostSensitiveAttributeEval: Evaluates an individual attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.FilteredAttributeEval: Evaluates an individual attribute by delegating to the base evaluator.
evaluateAttribute(int) - Method in class weka.attributeSelection.GainRatioAttributeEval: evaluates an individual attribute by measuring the gain ratio of the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.InfoGainAttributeEval: evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.LatentSemanticAnalysis: Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.OneRAttributeEval: evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.PrincipalComponents: Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Evaluates an individual attribute using ReliefF's instance based approach.
evaluateAttribute(int) - Method in class weka.attributeSelection.SVMAttributeEval: Evaluates an attribute by returning the rank of the square of its coefficient in a linear support vector machine.
evaluateAttribute(int) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.
evaluateClusterer(Instances) - Method in class weka.clusterers.ClusterEvaluation: Evaluate the clusterer on a set of instances.
evaluateClusterer(Instances, String) - Method in class weka.clusterers.ClusterEvaluation: Evaluate the clusterer on a set of instances.
evaluateClusterer(Instances, String, boolean) - Method in class weka.clusterers.ClusterEvaluation: Evaluate the clusterer on a set of instances.
evaluateClusterer(Clusterer, String[]) - Static method in class weka.clusterers.ClusterEvaluation: Evaluates a clusterer with the options given in an array of strings.
evaluateExpression(Instance) - Method in class weka.core.AttributeExpression: Evaluate the expression using the supplied Instance.
evaluateExpression(double[]) - Method in class weka.core.AttributeExpression: Evaluate the expression using the supplied array of attribute values.
evaluateModel(String, String[]) - Static method in class weka.classifiers.Evaluation: Evaluates a classifier with the options given in an array of strings.
evaluateModel(Classifier, String[]) - Static method in class weka.classifiers.Evaluation: Evaluates a classifier with the options given in an array of strings.
evaluateModel(Classifier, Instances, Object...) - Method in class weka.classifiers.Evaluation: Evaluates the classifier on a given set of instances.
evaluateModelOnce(Classifier, Instance) - Method in class weka.classifiers.Evaluation: Evaluates the classifier on a single instance.
evaluateModelOnce(double[], Instance) - Method in class weka.classifiers.Evaluation: Evaluates the supplied distribution on a single instance.
evaluateModelOnce(double, Instance) - Method in class weka.classifiers.Evaluation: Evaluates the supplied prediction on a single instance.
evaluateModelOnceAndRecordPrediction(Classifier, Instance) - Method in class weka.classifiers.Evaluation: Evaluates the classifier on a single instance and records the prediction (if the class is nominal).
evaluateModelOnceAndRecordPrediction(double[], Instance) - Method in class weka.classifiers.Evaluation: Evaluates the supplied distribution on a single instance.
evaluateSubset(BitSet) - Method in class weka.attributeSelection.CfsSubsetEval: evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.ClassifierSubsetEval: Evaluates a subset of attributes
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval: Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval: Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet) - Method in class weka.attributeSelection.ConsistencySubsetEval: Evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.CostSensitiveSubsetEval: Evaluates a subset of attributes.
evaluateSubset(BitSet) - Method in class weka.attributeSelection.FilteredSubsetEval: evaluates a subset of attributes
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator: Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator: Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet) - Method in interface weka.attributeSelection.SubsetEvaluator: evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.WrapperSubsetEval: Evaluates a subset of attributes
Evaluation - Class in weka.classifiers: Class for evaluating machine learning models.
Evaluation(Instances) - Constructor for class weka.classifiers.Evaluation: Initializes all the counters for the evaluation.
Evaluation(Instances, CostMatrix) - Constructor for class weka.classifiers.Evaluation: Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
EVALUATION_ACC - Static variable in class weka.classifiers.meta.GridSearch: evaluation via: Accuracy
EVALUATION_CC - Static variable in class weka.classifiers.meta.GridSearch: evaluation via: Correlation coefficient
EVALUATION_COMBINED - Static variable in class weka.classifiers.meta.GridSearch: evaluation via: Combined = (1-CC) + RRSE + RAE
EVALUATION_KAPPA - Static variable in class weka.classifiers.meta.GridSearch: evaluation via: kappa statistic
EVALUATION_MAE - Static variable in class weka.classifiers.meta.GridSearch: evaluation via: Mean absolute error
EVALUATION_RAE - Static variable in class weka.classifiers.meta.GridSearch: evaluation via: Relative absolute error
EVALUATION_RMSE - Static variable in class weka.classifiers.meta.GridSearch: evaluation via: Root mean squared error
EVALUATION_RRSE - Static variable in class weka.classifiers.meta.GridSearch: evaluation via: Root relative squared error
evaluationMeasureTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
evaluationModeTipText() - Method in class weka.classifiers.meta.ThresholdSelector
evaluationTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
EvaluationUtils - Class in weka.classifiers.evaluation: Contains utility functions for generating lists of predictions in various manners.
EvaluationUtils() - Constructor for class weka.classifiers.evaluation.EvaluationUtils
evaluatorTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Returns the tip text for this property
evaluatorTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the tip text for this property
evaluatorTipText() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns the tip text for this property
evalUsingTrainingDataTipText() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string for this option suitable for display in the gui as a tip text
EventConstraints - Interface in weka.gui.beans: Interface for objects that want to be able to specify at any given time whether their current configuration allows a particular event to be generated.
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.Associator: Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Associator: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClassAssigner: Returns true, if at the current time, the named event could be generated.
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.Classifier: Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Classifier: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClassValuePicker: Returns true, if at the current time, the named event could be generated.
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.Clusterer: Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Clusterer: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.CrossValidationFoldMaker: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in interface weka.gui.beans.EventConstraints: Returns true if, at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Filter: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Returns true if, at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Loader: Returns true if the named event can be generated at this time
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.MetaBean: Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.MetaBean: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.PredictionAppender: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TestSetMaker: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TextViewer: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TrainingSetMaker: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TrainTestSplitMaker: Returns true, if at the current time, the named event could be generated.
exclusiveTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
execute() - Method in class weka.classifiers.CheckSource: performs the comparison test
execute(String) - Method in class weka.experiment.DatabaseUtils: Executes a SQL query.
execute() - Method in class weka.experiment.RemoteExperimentSubTask: Run the experiment
execute() - Method in interface weka.experiment.Task: Execute this task.
execute() - Method in class weka.filters.CheckSource: performs the comparison test
execute() - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Perform the sub task
execute() - Method in class weka.gui.explorer.DataGeneratorPanel: generates the instances, returns TRUE if successful
execute() - Method in class weka.gui.GenericPropertiesCreator: generates the props-file for the GenericObjectEditor and stores it
execute(boolean) - Method in class weka.gui.GenericPropertiesCreator: generates the props-file for the GenericObjectEditor and stores it only if the the param store is TRUE.
execute() - Method in class weka.gui.sql.QueryPanel: executes the current query.
executeTask(Task) - Method in interface weka.experiment.Compute: Execute a task
executeTask(Task) - Method in class weka.experiment.RemoteEngine: Takes a task object and queues it for execution
ExhaustiveSearch - Class in weka.attributeSelection: ExhaustiveSearch :

Performs an exhaustive search through the space of attribute subsets starting from the empty set of attrubutes.
ExhaustiveSearch() - Constructor for class weka.attributeSelection.ExhaustiveSearch: Constructor
exists(TechnicalInformation.Field) - Method in class weka.core.TechnicalInformation: returns TRUE if the field is stored and has a value different from the empty string.
EXP - Static variable in interface weka.core.mathematicalexpression.sym
EXP - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
EXP_INDEX_TABLE - Static variable in class weka.experiment.DatabaseUtils: The name of the table containing the index to experiments.
EXP_RESULT_COL - Static variable in class weka.experiment.DatabaseUtils: The name of the column containing the results table name.
EXP_RESULT_PREFIX - Static variable in class weka.experiment.DatabaseUtils: The prefix for result table names.
EXP_SETUP_COL - Static variable in class weka.experiment.DatabaseUtils: The name of the column containing the experiment setup (parameters).
EXP_TYPE_COL - Static variable in class weka.experiment.DatabaseUtils: The name of the column containing the experiment type (ResultProducer).
expectation(double, int, double[], Hashtable) - Static method in class weka.associations.RuleGeneration: calculates the expected predctive accuracy of a rule
expectedCosts(double[]) - Method in class weka.classifiers.CostMatrix: Calculates the expected misclassification cost for each possible class value, given class probability estimates.
expectedCosts(double[], Instance) - Method in class weka.classifiers.CostMatrix: Calculates the expected misclassification cost for each possible class value, given class probability estimates.
expectedResultsPerAverageTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
Experiment - Class in weka.experiment: Holds all the necessary configuration information for a standard type experiment.
Experiment() - Constructor for class weka.experiment.Experiment
Experimenter - Class in weka.gui.experiment: The main class for the experiment environment.
Experimenter(boolean) - Constructor for class weka.gui.experiment.Experimenter: Creates the experiment environment gui with no initial experiment
ExperimenterDefaults - Class in weka.gui.experiment: This class offers get methods for the default Experimenter settings in the props file weka/gui/experiment/Experimenter.props.
ExperimenterDefaults() - Constructor for class weka.gui.experiment.ExperimenterDefaults
experimentIndexExists() - Method in class weka.experiment.DatabaseUtils: Returns true if the experiment index exists.
EXPLICIT - Static variable in class weka.associations.Tertius: Way of handling missing values: min counterinstances
Explorer - Class in weka.gui.explorer: The main class for the Weka explorer.
Explorer() - Constructor for class weka.gui.explorer.Explorer: Creates the experiment environment gui with no initial experiment
Explorer.CapabilitiesFilterChangeEvent - Class in weka.gui.explorer: This event can be fired in case the capabilities filter got changed
Explorer.CapabilitiesFilterChangeListener - Interface in weka.gui.explorer: Interface for classes that listen for filter changes.
Explorer.ExplorerPanel - Interface in weka.gui.explorer: A common interface for panels to be displayed in the Explorer
Explorer.LogHandler - Interface in weka.gui.explorer: A common interface for panels in the explorer that can handle logs
ExplorerDefaults - Class in weka.gui.explorer: This class offers get methods for the default Explorer settings in the props file weka/gui/explorer/Explorer.props.
ExplorerDefaults() - Constructor for class weka.gui.explorer.ExplorerDefaults
ExponentialFormat - Class in weka.core.matrix
ExponentialFormat() - Constructor for class weka.core.matrix.ExponentialFormat
ExponentialFormat(int) - Constructor for class weka.core.matrix.ExponentialFormat
ExponentialFormat(int, boolean) - Constructor for class weka.core.matrix.ExponentialFormat
ExponentialFormat(int, int, boolean, boolean) - Constructor for class weka.core.matrix.ExponentialFormat
exponentTipText() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Returns the tip text for this property
exponentTipText() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the tip text for this property
Expression - Class in weka.core.pmml
Expression(FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.Expression
Expression - Class in weka.datagenerators.classifiers.regression: A data generator for generating y according to a given expression out of randomly generated x.
E.g., the mexican hat can be generated like this:
sin(abs(a1)) / abs(a1)
In addition to this function, the amplitude can be changed and gaussian noise can be added.
Expression() - Constructor for class weka.datagenerators.classifiers.regression.Expression: initializes the generator
expressionTipText() - Method in class weka.datagenerators.classifiers.regression.Expression: Returns the tip text for this property
expressionTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the tip text for this property
expressionTipText() - Method in class weka.filters.unsupervised.attribute.MathExpression: Returns the tip text for this property
expressionTipText() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Returns the tip text for this property.
ExtensionFileFilter - Class in weka.gui: Provides a file filter for FileChoosers that accepts or rejects files based on their extension.
ExtensionFileFilter(String, String) - Constructor for class weka.gui.ExtensionFileFilter: Creates the ExtensionFileFilter
ExtensionFileFilter(String[], String) - Constructor for class weka.gui.ExtensionFileFilter: Creates an ExtensionFileFilter that accepts files that have any of the extensions contained in the supplied array.
extraArcs(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader: Count nr of exta arcs from other network compared to current network Note that an arc is not 'extra' if it is reversed.
extract(RevisionHandler) - Static method in class weka.core.RevisionUtils: Extracts the revision string returned by the RevisionHandler.
extract(String) - Static method in class weka.core.RevisionUtils: Extracts the revision string.
extractFilterAttributes(String) - Method in class weka.associations.GeneralizedSequentialPatterns: Parses a given String containing attribute numbers which are used for result filtering.
extremeValuesAsOutliersTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns the tip text for this property
extremeValuesFactorTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns the tip text for this property

F

f(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of f(x) given the mixture.
f(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of f(x) given the mixture, where x is a vector.
f(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Computes the value of f(x) given the mixture.
f(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Computes the value of f(x) given the mixture, where x is a vector.
FAILED - Static variable in class weka.experiment.TaskStatusInfo
failed() - Method in class weka.gui.sql.event.ConnectionEvent: whether an exception happened and is stored
failed() - Method in class weka.gui.sql.event.QueryExecuteEvent: is TRUE in case the exception is not NULL, i.e.
FALLOUT_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: Fallout
FALSE - Static variable in interface weka.core.mathematicalexpression.sym
FALSE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
FALSE_NEG_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: False Negatives
FALSE_POS_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: False Positives
falseNegativeRate(int) - Method in class weka.classifiers.Evaluation: Calculate the false negative rate with respect to a particular class.
falsePositiveRate(int) - Method in class weka.classifiers.Evaluation: Calculate the false positive rate with respect to a particular class.
FarthestFirst - Class in weka.clusterers: Cluster data using the FarthestFirst algorithm.

For more information see:

Hochbaum, Shmoys (1985).
FarthestFirst() - Constructor for class weka.clusterers.FarthestFirst
fastRegressionTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
FastVector - Class in weka.core: Implements a fast vector class without synchronized methods.
FastVector() - Constructor for class weka.core.FastVector: Constructs an empty vector with initial capacity zero.
FastVector(int) - Constructor for class weka.core.FastVector: Constructs a vector with the given capacity.
FastVector.FastVectorEnumeration - Class in weka.core: Class for enumerating the vector's elements.
FastVectorEnumeration(FastVector) - Constructor for class weka.core.FastVector.FastVectorEnumeration: Constructs an enumeration.
FastVectorEnumeration(FastVector, int) - Constructor for class weka.core.FastVector.FastVectorEnumeration: Constructs an enumeration with a special element.
FieldMetaInfo - Class in weka.core.pmml: Abstract superclass for various types of field meta data.
FieldMetaInfo(Element) - Constructor for class weka.core.pmml.FieldMetaInfo: Construct a new FieldMetaInfo.
FieldMetaInfo.Interval - Class in weka.core.pmml: Inner class for an Interval.
FieldMetaInfo.Interval.Closure - Enum in weka.core.pmml: Enumerated type for the closure.
FieldMetaInfo.Optype - Enum in weka.core.pmml: Enumerated type for the Optype
FieldMetaInfo.Value - Class in weka.core.pmml: Inner class for Values
FieldMetaInfo.Value.Property - Enum in weka.core.pmml: Enumerated type for the property.
FieldRef - Class in weka.core.pmml: Class encapsulating a FieldRef Expression.
FieldRef(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.FieldRef
fields() - Method in class weka.core.TechnicalInformation: returns an enumeration over all the stored fields
FILE_EXTENSION - Static variable in class weka.classifiers.CostMatrix: The deafult file extension for cost matrix files
FILE_EXTENSION - Static variable in class weka.core.converters.ArffLoader: the file extension
FILE_EXTENSION - Static variable in class weka.core.converters.C45Loader: the file extension
FILE_EXTENSION - Static variable in class weka.core.converters.CSVLoader: the file extension.
FILE_EXTENSION - Static variable in class weka.core.converters.LibSVMLoader: the file extension.
FILE_EXTENSION - Static variable in class weka.core.converters.LibSVMSaver: the file extension
FILE_EXTENSION - Static variable in class weka.core.converters.SerializedInstancesLoader: the file extension
FILE_EXTENSION - Static variable in class weka.core.converters.SVMLightLoader: the file extension.
FILE_EXTENSION - Static variable in class weka.core.converters.SVMLightSaver: the file extension.
FILE_EXTENSION - Static variable in class weka.core.converters.XRFFLoader: the file extension
FILE_EXTENSION - Static variable in class weka.core.Instances: The filename extension that should be used for arff files
FILE_EXTENSION - Static variable in class weka.core.xml.KOML: the extension for KOML files (including '.')
FILE_EXTENSION - Static variable in class weka.core.xml.XMLInstances: The filename extension that should be used for xrff files
FILE_EXTENSION - Static variable in class weka.core.xml.XStream: the extension for XStream files (including '.')
FILE_EXTENSION - Static variable in class weka.experiment.Experiment: The filename extension that should be used for experiment files
FILE_EXTENSION - Static variable in class weka.gui.beans.Classifier: the extension for serialized models (binary Java serialization)
FILE_EXTENSION - Static variable in class weka.gui.beans.KnowledgeFlowApp: the extension for the serialized setups (Java serialization)
FILE_EXTENSION - Static variable in class weka.gui.beans.SerializedModelSaver: the extension for serialized models (binary Java serialization)
FILE_EXTENSION_COMPRESSED - Static variable in class weka.core.converters.AbstractFileLoader: the extension for compressed files
FILE_EXTENSION_COMPRESSED - Static variable in class weka.core.converters.ArffLoader
FILE_EXTENSION_COMPRESSED - Static variable in class weka.core.converters.XRFFLoader: the extension for compressed files
FILE_EXTENSION_XML - Static variable in class weka.gui.beans.KnowledgeFlowApp: the extension for the serialized setups (Java serialization)
FileEditor - Class in weka.gui: A PropertyEditor for File objects that lets the user select a file.
FileEditor() - Constructor for class weka.gui.FileEditor
FileLogger - Class in weka.core.logging: A simple file logger, that just logs to a single file.
FileLogger() - Constructor for class weka.core.logging.FileLogger
filePrefix() - Method in class weka.core.converters.AbstractFileSaver: Gets the file name prefix
filePrefix() - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
filePrefix() - Method in interface weka.core.converters.Saver: Gets the file prefix This method is used in the KnowledgeFlow GUI.
FileSourcedConverter - Interface in weka.core.converters: Interface to a loader/saver that loads/saves from a file source.
fill(Shape) - Method in class weka.gui.visualize.PostscriptGraphics
fill3DRect(int, int, int, int, boolean) - Method in class weka.gui.visualize.PostscriptGraphics: Draw a filled rectangle with 3D effect in current pen color.
fillArc(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
fillFrame(Component) - Method in interface weka.gui.MainMenuExtension: Fills the frame with life, like adding components, window listeners, setting size, location, etc.
fillIn(int[], boolean[][]) - Method in class weka.classifiers.bayes.net.MarginCalculator: Apply Tarjan and Yannakakis (1984) fill in algorithm for graph triangulation.
fillOval(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Draw a filled Oval in current pen color.
fillPolygon(int[], int[], int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
fillPolygon(Polygon) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
fillRect(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Draw a filled rectangle in current pen color.
fillRoundRect(int, int, int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
fillWithMissingTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns the tip text for this property
Filter - Class in weka.filters: An abstract class for instance filters: objects that take instances as input, carry out some transformation on the instance and then output the instance.
Filter() - Constructor for class weka.filters.Filter
filter(String, Instances) - Static method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Filters the input dataset against the provided expression.
Filter - Class in weka.gui.beans: A wrapper bean for Weka filters
Filter() - Constructor for class weka.gui.beans.Filter
FILTER_NONE - Static variable in class weka.classifiers.functions.GaussianProcesses: no filter
FILTER_NONE - Static variable in class weka.classifiers.functions.SMO: filter: No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.functions.SMOreg: The filter to apply to the training data: None
FILTER_NONE - Static variable in class weka.classifiers.mi.MDD: No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MIDD: No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MIEMDD: No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MIOptimalBall: No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MISMO: No normalization/standardization
FILTER_NONE - Static variable in class weka.classifiers.mi.MISVM: No normalization/standardization
FILTER_NONE - Static variable in class weka.filters.unsupervised.attribute.StringToWordVector: normalization: No normalization.
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.GaussianProcesses: normalizes the data
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.SMO: filter: Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.SMOreg: The filter to apply to the training data: Normalzie
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MDD: Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MIDD: Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MIEMDD: Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MIOptimalBall: Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MISMO: Normalize training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.mi.MISVM: Normalize training data
FILTER_NORMALIZE_ALL - Static variable in class weka.filters.unsupervised.attribute.StringToWordVector: normalization: Normalize all data.
FILTER_NORMALIZE_TEST_ONLY - Static variable in class weka.filters.unsupervised.attribute.StringToWordVector: normalization: Normalize test data only.
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.GaussianProcesses: standardizes the data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.SMO: filter: Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.SMOreg: The filter to apply to the training data: Standardize
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MDD: Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MIDD: Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MIEMDD: Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MIOptimalBall: Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MISMO: Standardize training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.mi.MISVM: Standardize training data
filterAfterFirstBatchTipText() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Returns the tip text for this property.
filterAttributesTipText() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the filterAttributes option tip text for the Weka GUI.
FilterBeanInfo - Class in weka.gui.beans: Bean info class for the Filter bean
FilterBeanInfo() - Constructor for class weka.gui.beans.FilterBeanInfo
FilterCustomizer - Class in weka.gui.beans: GUI customizer for the filter bean
FilterCustomizer() - Constructor for class weka.gui.beans.FilterCustomizer
FilteredAssociator - Class in weka.associations: Class for running an arbitrary associator on data that has been passed through an arbitrary filter.
FilteredAssociator() - Constructor for class weka.associations.FilteredAssociator: Default constructor.
FilteredAttributeEval - Class in weka.attributeSelection: Class for running an arbitrary attribute evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
FilteredAttributeEval() - Constructor for class weka.attributeSelection.FilteredAttributeEval
FilteredClassifier - Class in weka.classifiers.meta: Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
FilteredClassifier() - Constructor for class weka.classifiers.meta.FilteredClassifier: Default constructor.
FilteredClusterer - Class in weka.clusterers: Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
FilteredClusterer() - Constructor for class weka.clusterers.FilteredClusterer: Default constructor.
FilteredSubsetEval - Class in weka.attributeSelection: Class for running an arbitrary subset evaluator on data that has been passed through an arbitrary filter (note: filters that alter the order or number of attributes are not allowed).
FilteredSubsetEval() - Constructor for class weka.attributeSelection.FilteredSubsetEval
filterFile(Filter, String[]) - Static method in class weka.filters.Filter: Method for testing filters.
filtersTipText() - Method in class weka.filters.MultiFilter: Returns the tip text for this property
filtersTipText() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Returns the tip text for this property.
filterSubset(List<ScatterSearchV1.Subset>, int) - Method in class weka.attributeSelection.ScatterSearchV1: Filter a given Lis of Subsets removing the equals subsets
filterTipText() - Method in class weka.associations.FilteredAssociator: Returns the tip text for this property
filterTipText() - Method in class weka.attributeSelection.FilteredAttributeEval: Returns the tip text for this property
filterTipText() - Method in class weka.attributeSelection.FilteredSubsetEval: Returns the tip text for this property
filterTipText() - Method in class weka.classifiers.functions.PLSClassifier: Returns the tip text for this property
filterTipText() - Method in class weka.classifiers.meta.FilteredClassifier: Returns the tip text for this property
filterTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
filterTipText() - Method in class weka.clusterers.FilteredClusterer: Returns the tip text for this property.
filterTipText() - Method in class weka.filters.unsupervised.attribute.Wavelet: Returns the tip text for this property
filterTypeTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
filterTypeTipText() - Method in class weka.classifiers.functions.GaussianProcesses: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MDD: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MIDD: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MIEMDD: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MIOptimalBall: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.mi.MISVM: Returns the tip text for this property
finalize() - Method in class weka.gui.sql.ResultSetTable: frees up the memory
finalize() - Method in class weka.gui.sql.ResultSetTableModel: frees up the memory.
finalize() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
find(String, String[]) - Static method in class weka.core.ClassDiscovery: Checks the given packages for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(String, String) - Static method in class weka.core.ClassDiscovery: Checks the given package for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(Class, String[]) - Static method in class weka.core.ClassDiscovery: Checks the given packages for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find(Class, String) - Static method in class weka.core.ClassDiscovery: Checks the given package for classes that inherited from the given class, in case it's a class, or implement this class, in case it's an interface.
find() - Method in class weka.core.FindWithCapabilities: returns a list with all the classnames that fit the criteria.
find(Object, PropertyPath.Path) - Static method in class weka.core.PropertyPath: returns the property and object associated with the given path, null if a problem occurred.
find(String) - Method in class weka.core.Trie.TrieNode: returns the node with the given suffix
findAllRulesForSupportLevelTipText() - Method in class weka.associations.FPGrowth: Tip text for this property suitable for displaying in the GUI.
findArgmin(double[], double[][]) - Method in class weka.core.Optimization: Main algorithm.
findBestLeaf(double[], RuleNode[]) - Method in class weka.classifiers.trees.m5.RuleNode: Find the leaf with greatest coverage
findCentralTendencies(double[]) - Method in class weka.classifiers.BVDecomposeSegCVSub: Finds the central tendency, given the classifications for an instance.
findInstance(Point) - Static method in class weka.gui.beans.BeanInstance: Looks for a bean (if any) whose bounds contain the supplied point
findInstances(Rectangle) - Static method in class weka.gui.beans.BeanInstance: Looks for all beans (if any) located within the supplied bounding box.
findIntervall(double) - Method in class weka.associations.PriorEstimation: searches the mid point of the interval a given confidence value falls into
findMinDistance(Instances, int) - Static method in class weka.estimators.EstimatorUtils: Find the minimum distance between values
findNodes(String) - Method in class weka.core.xml.XMLDocument: Returns the nodes that the given xpath expression will find in the document.
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns the tip text for this property
findPackages() - Static method in class weka.core.ClassDiscovery: Lists all packages it can find in the classpath.
findRadius(Instances) - Method in class weka.classifiers.mi.MIOptimalBall: Find the maximum radius for the optimal ball.
findReadMethod(Object, String) - Static method in class weka.core.xml.XMLSerializationMethodHandler: returns the method with the given name that has the same signature as readFromXML() of the XMLSerialiation class.
findWeights(int, double[][]) - Method in class weka.classifiers.mi.MINND: Use gradient descent to distort the MU parameter for the exemplar.
FindWithCapabilities - Class in weka.core: Locates all classes with certain capabilities.
FindWithCapabilities() - Constructor for class weka.core.FindWithCapabilities
findWriteMethod(Object, String) - Static method in class weka.core.xml.XMLSerializationMethodHandler: returns the method with the given name that has the same signature as writeToXML() of the XMLSerialiation class.
FINE - Static variable in class weka.core.Debug: the log level Fine
FINER - Static variable in class weka.core.Debug: the log level Finer
FINEST - Static variable in class weka.core.Debug: the log level Finest
finished() - Method in class weka.experiment.OutputZipper: Closes the zip file.
FINISHED - Static variable in class weka.experiment.TaskStatusInfo
finished() - Method in class weka.gui.visualize.PostscriptGraphics: Finalizes output file.
fireLayoutCompleteEvent(LayoutCompleteEvent) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Fires a LayoutCompleteEvent.
fireLayoutCompleteEvent(LayoutCompleteEvent) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This fires a LayoutCompleteEvent once a layout has been completed.
FIRST - Static variable in class weka.filters.unsupervised.attribute.ClassAssigner: use the first attribute as class.
firstElement() - Method in class weka.core.FastVector: Returns the first element of the vector.
firstElement() - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Returns the first component of this list.
firstInstance() - Method in class weka.core.Instances: Returns the first instance in the set.
FirstOrder - Class in weka.filters.unsupervised.attribute: This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
FirstOrder() - Constructor for class weka.filters.unsupervised.attribute.FirstOrder
firstValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
firstValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
fit(DoubleVector) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Fits the mixture (or mixing) distribution to the data.
fit(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Fits the mixture (or mixing) distribution to the data.
fitForSingleCluster(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Fits the mixture (or mixing) distribution to the data.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Contructs the set of fitting intervals for mixture estimation.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Contructs the set of fitting intervals for mixture estimation.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Contructs the set of fitting intervals for mixture estimation.
fitToScreen() - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer: Fits the tree to the current screen size.
fitToScreen() - Method in class weka.gui.treevisualizer.TreeVisualizer: Fits the tree to the current screen size.
FlexibleDecimalFormat - Class in weka.core.matrix
FlexibleDecimalFormat() - Constructor for class weka.core.matrix.FlexibleDecimalFormat
FlexibleDecimalFormat(int) - Constructor for class weka.core.matrix.FlexibleDecimalFormat
FlexibleDecimalFormat(int, boolean) - Constructor for class weka.core.matrix.FlexibleDecimalFormat
FlexibleDecimalFormat(int, boolean, boolean, boolean) - Constructor for class weka.core.matrix.FlexibleDecimalFormat
FlexibleDecimalFormat(double) - Constructor for class weka.core.matrix.FlexibleDecimalFormat
FLOAT - Static variable in class weka.experiment.DatabaseUtils: Type mapping for FLOAT used for reading experiment results.
floatingForwardSearch(int, BitSet, int[], int, boolean, int, Instances, SubsetEvaluator, boolean) - Method in class weka.attributeSelection.LFSMethods: Performs linear floating forward selection ( the stopping criteria cannot be changed to a specific size value )
FloatingPointFormat - Class in weka.core.matrix: Class for the format of floating point numbers
FloatingPointFormat() - Constructor for class weka.core.matrix.FloatingPointFormat: Default constructor
FloatingPointFormat(int) - Constructor for class weka.core.matrix.FloatingPointFormat
FloatingPointFormat(int, int) - Constructor for class weka.core.matrix.FloatingPointFormat
FloatingPointFormat(int, int, boolean) - Constructor for class weka.core.matrix.FloatingPointFormat
FLOOR - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
FLOOR - Static variable in interface weka.core.mathematicalexpression.sym
FLOOR - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
FLOOR1 - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
FlowRunner - Class in weka.gui.beans: Small utility class for executing KnowledgeFlow flows outside of the KnowledgeFlow application
FlowRunner() - Constructor for class weka.gui.beans.FlowRunner: Constructor
FlowRunner.SimpleLogger - Class in weka.gui.beans
flush() - Method in class weka.core.logging.OutputLogger.OutputPrintStream: ignored.
flush() - Method in class weka.core.Tee: flushes all the printstreams.
fMeasure(int) - Method in class weka.classifiers.Evaluation: Calculate the F-Measure with respect to a particular class.
FMEASURE - Static variable in class weka.classifiers.meta.ThresholdSelector: F-measure
FMEASURE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: FMeasure
FOLD_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer: The name of the key field containing the fold number
foldsTipText() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string for this option suitable for display in the gui as a tip text
foldsTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
foldsTipText() - Method in class weka.gui.beans.CrossValidationFoldMaker: Tip text for this property
foldsTypeTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
foldTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
foldTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
forCapabilities(Capabilities) - Static method in class weka.core.TestInstances: returns a TestInstances instance setup already for the the given capabilities.
forInstances(Instances) - Static method in class weka.core.Capabilities: returns a Capabilities object specific for this data.
forInstances(Instances, boolean) - Static method in class weka.core.Capabilities: returns a Capabilities object specific for this data.
format(double, StringBuffer, FieldPosition) - Method in class weka.core.matrix.ExponentialFormat
format(double, StringBuffer, FieldPosition) - Method in class weka.core.matrix.FlexibleDecimalFormat
format(double, StringBuffer, FieldPosition) - Method in class weka.core.matrix.FloatingPointFormat
FORMAT_AVAILABLE - Static variable in class weka.gui.beans.InstanceEvent
FORMAT_AVAILABLE - Static variable in class weka.gui.streams.InstanceEvent: Specifies that the instance format is available
FORMAT_HHMMSS - Static variable in class weka.core.Debug.Clock: the output format in hours:minutes:seconds, with fraction of msecs
FORMAT_MILLISECONDS - Static variable in class weka.core.Debug.Clock: the output format in milli-seconds
FORMAT_SECONDS - Static variable in class weka.core.Debug.Clock: the output format in seconds, with fraction of msecs
formatDate(double) - Method in class weka.core.Attribute: Returns the given amount of milliseconds formatted according to the current Date format.
formatString(String) - Method in class weka.core.matrix.FlexibleDecimalFormat
formatTipText() - Method in class weka.datagenerators.DataGenerator: Returns the tip text for this property
forName(String, String[]) - Static method in class weka.associations.AbstractAssociator: Creates a new instance of a associator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.attributeSelection.ASEvaluation: Creates a new instance of an attribute/subset evaluator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.attributeSelection.ASSearch: Creates a new instance of a search class given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.classifiers.Classifier: Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.classifiers.functions.supportVector.Kernel: Creates a new instance of a kernel given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.clusterers.AbstractClusterer: Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method.
forName(Class, String, String[]) - Static method in class weka.core.Utils: Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.estimators.Estimator: Creates a new instance of a estimatorr given it's class name and (optional) arguments to pass to it's setOptions method.
forward(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Forward ordering of columns in terms of response explanation.
forwardSearch(int, BitSet, int[], int, boolean, int, int, Instances, SubsetEvaluator, boolean) - Method in class weka.attributeSelection.LFSMethods: Performs linear forward selection
forwardSelectionMethodTipText() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the tip text for this property
foundUsefulAttribute() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns true if a usable attribute was found.
FP_RATE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: False Positive Rate"
FPGrowth - Class in weka.associations: Class implementing the FP-growth algorithm for finding large item sets without candidate generation.
FPGrowth() - Constructor for class weka.associations.FPGrowth: Construct a new FPGrowth object.
FPGrowth.AssociationRule - Class in weka.associations
FPGrowth.AssociationRule.METRIC_TYPE - Enum in weka.associations: Enum for holding different metric types
FPGrowth.BinaryItem - Class in weka.associations: Inner class that handles a single binary item
FProbability(double, int, int) - Static method in class weka.core.Statistics: Computes probability of F-ratio.
freeNotCoveredInstances() - Method in class weka.classifiers.trees.m5.Rule: Free up memory consumed by the set of instances not covered by this rule.
FREQ_ASCEND - Static variable in class weka.filters.supervised.attribute.ClassOrder: The class values are sorted in ascending order based on their frequencies
FREQ_DESCEND - Static variable in class weka.filters.supervised.attribute.ClassOrder: The class values are sorted in descending order based on their frequencies
frequencyLimitTipText() - Method in class weka.classifiers.bayes.AODE: Returns the tip text for this property
frequencyLimitTipText() - Method in class weka.classifiers.bayes.AODEsr: Returns the tip text for this property
frequencyThresholdTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
FromFile - Class in weka.classifiers.bayes.net.search.fixed: The FromFile reads the structure of a Bayes net from a file in BIFF format.
FromFile() - Constructor for class weka.classifiers.bayes.net.search.fixed.FromFile
fromXML(Document) - Method in class weka.core.xml.XMLSerialization: returns the given DOM document as an instance of the specified class
FT - Class in weka.classifiers.trees: Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
FT() - Constructor for class weka.classifiers.trees.FT: Creates an instance of FT with standard options
FTInnerNode - Class in weka.classifiers.trees.ft: Class for Functional Inner tree structure.
FTInnerNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTInnerNode: Constructor for Functional Inner tree node.
FTLeavesNode - Class in weka.classifiers.trees.ft: Class for Functional Leaves tree version.
FTLeavesNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTLeavesNode: Constructor for Functional Leaves tree node.
FTNode - Class in weka.classifiers.trees.ft: Class for Functional tree structure.
FTNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTNode: Constructor for Functional tree node.
FTtree - Class in weka.classifiers.trees.ft: Abstract class for Functional tree structure.
FTtree() - Constructor for class weka.classifiers.trees.ft.FTtree
fullValue() - Method in class weka.gui.HierarchyPropertyParser: The full value of the current node, i.e.
Function - Class in weka.core.pmml: Abstract superclass for PMML built-in and DefineFunctions.
Function() - Constructor for class weka.core.pmml.Function
FUNCTION_1 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 1
FUNCTION_10 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 10
FUNCTION_2 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 2
FUNCTION_3 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 3
FUNCTION_4 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 4
FUNCTION_5 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 5
FUNCTION_6 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 6
FUNCTION_7 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 7
FUNCTION_8 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 8
FUNCTION_9 - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: function 9
FUNCTION_TAGS - Static variable in class weka.datagenerators.classifiers.classification.Agrawal: the funtion tags
functionTipText() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Returns the tip text for this property

G

g1(double, double) - Method in class weka.classifiers.functions.pace.PaceMatrix: Constructs the Givens rotation
g2(double[], int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Performs the Givens rotation
gainRatio() - Method in class weka.classifiers.trees.j48.BinC45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio() - Method in class weka.classifiers.trees.j48.C45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio(double[][]) - Static method in class weka.core.ContingencyTables: Computes gain ratio for contingency table (split on rows).
GainRatioAttributeEval - Class in weka.attributeSelection: GainRatioAttributeEval :

Evaluates the worth of an attribute by measuring the gain ratio with respect to the class.

GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute).

Valid options are:
GainRatioAttributeEval() - Constructor for class weka.attributeSelection.GainRatioAttributeEval: Constructor
GainRatioSplitCrit - Class in weka.classifiers.trees.j48: Class for computing the gain ratio for a given distribution.
GainRatioSplitCrit() - Constructor for class weka.classifiers.trees.j48.GainRatioSplitCrit
gamma(double) - Static method in class weka.core.Statistics: Returns the Gamma function of the argument.
gammaTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
gammaTipText() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Returns the tip text for this property
GAUSSIAN - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression: Distributions available
GAUSSIAN - Static variable in class weka.datagenerators.clusterers.SubspaceCluster: cluster type: gaussian
GAUSSIAN - Static variable in class weka.filters.unsupervised.attribute.RandomProjection: distribution type: gaussian
GaussianPriorImpl - Class in weka.classifiers.bayes.blr: Implementation of the Gaussian Prior update function based on CLG Algorithm with a certain Trust Region Update.
GaussianPriorImpl() - Constructor for class weka.classifiers.bayes.blr.GaussianPriorImpl
GaussianProcesses - Class in weka.classifiers.functions: Implements Gaussian Processes for regression without hyperparameter-tuning.
GaussianProcesses() - Constructor for class weka.classifiers.functions.GaussianProcesses: the default constructor
GE - Static variable in interface weka.core.mathematicalexpression.sym
GE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
GeneralizedSequentialPatterns - Class in weka.associations: Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set.
The attribute identifying the distinct data sequences contained in the set can be determined by the respective option.
GeneralizedSequentialPatterns() - Constructor for class weka.associations.GeneralizedSequentialPatterns: Constructor.
GeneralRegression - Class in weka.classifiers.pmml.consumer: Class implementing import of PMML General Regression model.
GeneralRegression(Element, Instances, MiningSchema) - Constructor for class weka.classifiers.pmml.consumer.GeneralRegression: Constructs a GeneralRegression classifier.
generate() - Method in class weka.core.Javadoc: generates either the plain Javadoc (if no filename specified) or the updated file (if a filename is specified).
generate() - Method in class weka.core.ListOptions: generates the options string.
generate() - Method in class weka.core.TestInstances: Generates a new dataset
generate(String) - Method in class weka.core.TestInstances: generates a new dataset.
generateDistribution() - Method in class weka.associations.PriorEstimation: Calculates the prior distribution.
generateExample() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.classification.LED24: Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.classification.RDG1: Generate an example of the dataset dataset.
generateExample() - Method in class weka.datagenerators.classifiers.regression.Expression: Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Generates one example of the dataset.
generateExample() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Generate an example of the dataset.
generateExample() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Generate an example of the dataset.
generateExample() - Method in class weka.datagenerators.DataGenerator: Generates one example of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.LED24: Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.classification.RDG1: Generate all examples of the dataset.
generateExamples(int, Random, Instances) - Method in class weka.datagenerators.classifiers.classification.RDG1: Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.regression.Expression: Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Generates all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Generate all examples of the dataset.
generateExamples(Random, Instances) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.DataGenerator: Generates all examples of the dataset.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.LED24: Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.classification.RDG1: Compiles documentation about the data generation.
generateFinished() - Method in class weka.datagenerators.classifiers.regression.Expression: Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Generates a comment string that documentats the data generator.
generateFinished() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Compiles documentation about the data generation after the generation process
generateFinished() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Compiles documentation about the data generation after the generation process
generateFinished() - Method in class weka.datagenerators.DataGenerator: Generates a comment string that documentates the data generator.
generateHelp() - Method in class weka.core.Javadoc: generates a string to print as help on the console
generateHelp() - Method in class weka.core.ListOptions: generates a string to print as help on the console
generateInstances() - Method in class weka.classifiers.bayes.net.BayesNetGenerator: GenerateInstances generates random instances sampling from the distribution represented by the Bayes network structure.
generateInstances(int[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Generate an instance.
generateInstances(int[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Generates a new instance using one kernel estimator.
generateInstances() - Method in class weka.gui.explorer.PreprocessPanel: sets Instances generated via DataGenerators (pops up a Dialog)
generateOutput() - Method in class weka.gui.visualize.BMPWriter: generates the actual output
generateOutput() - Method in class weka.gui.visualize.JPEGWriter: generates the actual output.
generateOutput() - Method in class weka.gui.visualize.PNGWriter: generates the actual output
generateOutput() - Method in class weka.gui.visualize.PostscriptWriter: generates the actual output
generatePartition(Instances) - Method in class weka.classifiers.trees.RandomTree: Builds the classifier to generate a partition.
generateRandomNetwork() - Method in class weka.classifiers.bayes.net.BayesNetGenerator: Generate random connected Bayesian network with discrete nodes having all the same cardinality.
generateRandomNetworkStructure(int, int) - Method in class weka.classifiers.bayes.net.BayesNetGenerator: GenerateRandomNetworkStructure generate random connected Bayesian network
generateRandomNumber(int) - Method in class weka.attributeSelection.ScatterSearchV1
generateRankingTipText() - Method in class weka.attributeSelection.GreedyStepwise: Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
GenerateReferenceSet(List<ScatterSearchV1.Subset>, int, int) - Method in class weka.attributeSelection.ScatterSearchV1: Generate the a ReferenceSet containing the n best solutions and the m most diverse solutions of the initial Population.
generateRuleItem(ItemSet, ItemSet, Instances, int, int, double[], Hashtable) - Method in class weka.associations.RuleItem: Constructs a new RuleItem if the support of the given rule is above the support threshold.
generateRules(double, FastVector, int) - Method in class weka.associations.AprioriItemSet: Generates all rules for an item set.
generateRules(int, double[], Hashtable, double, Instances, TreeSet, int) - Method in class weka.associations.CaRuleGeneration: Generates all rules for an item set.
generateRules(double, boolean) - Method in class weka.associations.LabeledItemSet: Generates rules out of item sets
generateRules(int, double[], Hashtable, double, Instances, TreeSet, int) - Method in class weka.associations.RuleGeneration: Generates all rules for an item set.
generateRulesBruteForce(double, int, FastVector, int, int, double) - Method in class weka.associations.AprioriItemSet: Generates all significant rules for an item set.
generateRulesBruteForce(FPGrowth.FrequentItemSets, FPGrowth.AssociationRule.METRIC_TYPE, double, int, int, int) - Static method in class weka.associations.FPGrowth.AssociationRule: Generate all association rules, from the supplied frequet item sets, that meet a given minimum metric threshold.
generateRulesTipText() - Method in class weka.classifiers.trees.m5.M5Base: Returns the tip text for this property
generateStart() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.classification.LED24: Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.classification.RDG1: Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.regression.Expression: Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Generates a comment string that documentates the data generator.
generateStart() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Compiles documentation about the data generation before the generation process
generateStart() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Compiles documentation about the data generation before the generation process
generateStart() - Method in class weka.datagenerators.DataGenerator: Generates a comment string that documentates the data generator.
GeneratorPropertyIteratorPanel - Class in weka.gui.experiment: This panel controls setting a list of values for an arbitrary resultgenerator property for an experiment to iterate over.
GeneratorPropertyIteratorPanel() - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel: Creates the property iterator panel initially disabled.
GeneratorPropertyIteratorPanel(Experiment) - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel: Creates the property iterator panel and sets the experiment.
GenericArrayEditor - Class in weka.gui: A PropertyEditor for arrays of objects that themselves have property editors.
GenericArrayEditor() - Constructor for class weka.gui.GenericArrayEditor: Sets up the array editor.
GenericObjectEditor - Class in weka.gui: A PropertyEditor for objects.
GenericObjectEditor() - Constructor for class weka.gui.GenericObjectEditor: Default constructor.
GenericObjectEditor(boolean) - Constructor for class weka.gui.GenericObjectEditor: Constructor that allows specifying whether it is possible to change the class within the editor dialog.
GenericObjectEditor.CapabilitiesFilterDialog - Class in weka.gui: A dialog for selecting Capabilities to look for in the GOE tree.
GenericObjectEditor.GOEPanel - Class in weka.gui: Handles the GUI side of editing values.
GenericObjectEditor.GOETreeNode - Class in weka.gui: A specialized TreeNode for supporting filtering via Capabilities.
GenericObjectEditor.JTreePopupMenu - Class in weka.gui: Creates a popup menu containing a tree that is aware of the screen dimensions.
GenericPropertiesCreator - Class in weka.gui: This class can generate the properties object that is normally loaded from the GenericObjectEditor.props file (= PROPERTY_FILE).
GenericPropertiesCreator() - Constructor for class weka.gui.GenericPropertiesCreator: initializes the creator, locates the props file with the Utils class.
GenericPropertiesCreator(String) - Constructor for class weka.gui.GenericPropertiesCreator: initializes the creator, the given file overrides the props-file search of the Utils class
GeneticSearch - Class in weka.attributeSelection: GeneticSearch:

Performs a search using the simple genetic algorithm described in Goldberg (1989).

For more information see:

David E.
GeneticSearch() - Constructor for class weka.attributeSelection.GeneticSearch: Constructor.
GeneticSearch - Class in weka.classifiers.bayes.net.search.global: This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
GeneticSearch() - Constructor for class weka.classifiers.bayes.net.search.global.GeneticSearch
GeneticSearch - Class in weka.classifiers.bayes.net.search.local: This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
GeneticSearch() - Constructor for class weka.classifiers.bayes.net.search.local.GeneticSearch
get(int) - Method in class weka.core.matrix.DoubleVector: Gets a single element.
get(int) - Method in class weka.core.matrix.IntVector: Gets the value of an element.
get(int, int) - Method in class weka.core.matrix.Matrix: Get a single element.
get(int) - Method in class weka.core.PropertyPath.Path: returns the element at the given index
get(int) - Method in class weka.core.Tee: returns the specified PrintStream from the list.
get(String) - Method in class weka.core.xml.MethodHandler: returns the stored method for the given property
get(Class) - Method in class weka.core.xml.MethodHandler: returns the stored method for the given class
get(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Returns the element at the specified position in this list.
get(String, String) - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the value for the specified property, if non-existent then the default value.
get(String, String) - Static method in class weka.gui.explorer.ExplorerDefaults: returns the value for the specified property, if non-existent then the default value.
getAboutPanel() - Method in class weka.gui.PropertySheetPanel: Return the panel containing global info and help for the object being edited.
getAccu() - Method in class weka.classifiers.rules.JRip.Antd
getAccuRate() - Method in class weka.classifiers.rules.JRip.Antd
getActionListener(JFrame) - Method in interface weka.gui.MainMenuExtension: If the extension has a custom ActionListener for the menu item, then it must be returned here.
getActualIndex(int) - Method in class weka.core.AttributeLocator: returns actual index in the Instances object.
getActualRow(int) - Method in class weka.gui.SortedTableModel: Returns the actual underlying row the given visible one represents.
getAcuity() - Method in class weka.clusterers.Cobweb: get the acuity value
getAddress() - Static method in class weka.core.Copyright: returns the address of the owner
getAdjustWeights() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns true if instance weights will be adjusted to maintain total weight per class.
getADTree() - Method in class weka.classifiers.bayes.BayesNet: get ADTree strucrture containing efficient representation of counts.
getAdvanceDataSetFirst() - Method in class weka.experiment.Experiment: Get the value of m_DataSetFirstFirst.
getAlgorithm() - Method in class weka.filters.supervised.attribute.PLSFilter: Gets the type of algorithm to use
getAlgorithm() - Method in class weka.filters.unsupervised.attribute.Wavelet: Gets the type of algorithm to use
getAlgorithmStart() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the time/date string the algorithm was started
getAlgorithmType() - Method in class weka.classifiers.mi.MILR: Gets the type of algorithm.
getAllBits(List<ScatterSearchV1.Subset>) - Method in class weka.attributeSelection.ScatterSearchV1: Save in Bitset all the gens that are in many others subsets.
getAllowedIndices() - Method in class weka.core.AttributeLocator: returns the indices that are allowed to check for the attribute type
getAllowUnclassifiedInstances() - Method in class weka.classifiers.trees.RandomTree: Get the value of NumFolds.
getAllTheRules() - Method in class weka.associations.Apriori: returns all the rules
getAllTheRules() - Method in class weka.associations.PredictiveApriori: returns all the rules
getAlpha() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Get prior used in probability table estimation
getAlpha() - Method in class weka.classifiers.functions.Winnow: Get the value of Alpha.
getAmplitude() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Gets the amplitude multiplier.
getAnimatedIcon() - Method in class weka.gui.beans.BeanVisual: Returns the animated icon
getAnimatedIconPath() - Method in class weka.gui.beans.BeanVisual: returns the path for the animated icon
getAntds() - Method in class weka.classifiers.rules.JRip.RipperRule: Return the antecedents
getAppendPredictedProbabilities() - Method in class weka.gui.beans.PredictionAppender: Return true if predicted probabilities are to be appended rather than class value
getArffFile() - Method in class weka.gui.streams.InstanceLoader
getArffFile() - Method in class weka.gui.streams.InstanceSavePanel
getArray() - Method in class weka.core.matrix.DoubleVector: Access the internal one-dimensional array.
getArray() - Method in class weka.core.matrix.IntVector: Access the internal one-dimensional array.
getArray() - Method in class weka.core.matrix.Matrix: Access the internal two-dimensional array.
getArrayClass(Class) - Static method in class weka.core.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayCopy() - Method in class weka.core.matrix.DoubleVector: Returns a copy of the DoubleVector usng a double array.
getArrayCopy() - Method in class weka.core.matrix.IntVector: Returns a copy of the internal one-dimensional array.
getArrayCopy() - Method in class weka.core.matrix.Matrix: Copy the internal two-dimensional array.
getArrayDimensions(Class) - Static method in class weka.core.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class weka.core.Utils: Returns the dimensions of the given array.
getArtificialSize() - Method in class weka.classifiers.meta.Decorate: Factor that determines number of artificial examples to generate.
getASCrossvalidationFolds() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default number of folds of the CV in the attribute selection panel.
getASEvaluator() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default attribute evalautor (fully configured) for the attribute selection panel.
getAsInstance(Instances, Random) - Method in class weka.core.AlgVector: Gets the elements of the vector as an instance.
getASRandomSeed() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default random seed value in the attribute selection panel.
getASSearch() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default attribute selection search scheme (fully configured) for the attribute selection panel.
getAssignments() - Method in class weka.clusterers.SimpleKMeans: Gets the assignments for each instance
getAssociatedConnections() - Method in class weka.gui.beans.MetaBean
getAssociationRules() - Method in class weka.associations.FPGrowth: Gets the list of mined association rules.
getAssociator() - Method in class weka.associations.CheckAssociator: Get the associator being tested
getAssociator() - Method in class weka.associations.SingleAssociatorEnhancer: Get the associator used as the base associator.
getAssociator() - Method in class weka.gui.beans.Associator: Get the associator currently set for this wrapper
getAssociator() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default associator (fully configured) for the associations panel.
getASTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default attribute selection test mode for the attribute selection panel.
getAsText() - Method in class weka.gui.CostMatrixEditor: Some objects can be represented as text, but a cost matrix cannot.
getAsText() - Method in class weka.gui.GenericArrayEditor: Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.SelectedTagEditor: Gets the current value as text.
getAsText() - Method in class weka.gui.SimpleDateFormatEditor: Returns the date format string.
getAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Attribute Indexes array
getAttList_Irr() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the array that defines which of the attributes are seen to be irrelevant.
getAttr() - Method in class weka.classifiers.rules.JRip.Antd
getAttribute() - Method in class weka.associations.FPGrowth.BinaryItem: Get the attribute that this item corresponds to.
getAttribute1() - Method in class weka.gui.visualize.VisualizePanelEvent
getAttribute2() - Method in class weka.gui.visualize.VisualizePanelEvent
getAttributeAt(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns the attribute at the given index, can be NULL if not an attribute column
getAttributeAt(int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the attribute at the given index, can be NULL if not an attribute column
getAttributeCapabilities() - Method in class weka.core.Capabilities: returns all attribute capabilities
getAttributeColumn(String) - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns the column of the given attribute name, -1 if not found
getAttributeColumn(String) - Method in class weka.gui.arffviewer.ArffTableModel: returns the column of the given attribute name, -1 if not found
getAttributeEvaluator() - Method in class weka.attributeSelection.FilteredAttributeEval: Get the attribute evaluator to use
getAttributeEvaluator() - Method in class weka.attributeSelection.RaceSearch: Get the attribute evaluator used to generate the ranking.
getAttributeEvaluator() - Method in class weka.attributeSelection.RankSearch: Get the attribute evaluator used to generate the ranking.
getAttributeID() - Method in class weka.experiment.ClassifierSplitEvaluator: Get the index of Attibute Identifying the instances
getAttributeIndex() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns the index of the attribute used in the regression.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.Add: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.AddNoise: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.AddValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Gets the index of the attribute converted.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the index of the attribute used.
getAttributeIndexes() - Method in class weka.filters.unsupervised.attribute.NominalToString: Get the index of the attribute used.
getAttributeIndices() - Method in class weka.core.AttributeLocator: Returns the indices of the attributes.
getAttributeIndices() - Method in interface weka.core.DistanceFunction: Gets the range of attributes used in the calculation of the distance.
getAttributeIndices() - Method in class weka.core.NormalizableDistance: Gets the range of attributes used in the calculation of the distance.
getAttributeIndices() - Method in class weka.filters.supervised.attribute.Discretize: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Copy: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Gets the selection of the columns, e.g., first-last or first-3,5-last
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Remove: Get the current range selection.
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Reorder: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets the current range selection.
getAttributeMaxValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Returns the array of maximum-values for each attribute
getAttributeMaxValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Returns the array of maximum-values for each attribute
getAttributeMinValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Returns the array of minimum-values for each attribute
getAttributeMinValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Returns the array of minimum-values for each attribute
getAttributeName() - Method in class weka.filters.unsupervised.attribute.Add: Get the name of the attribute to be created.
getAttributeName() - Method in class weka.filters.unsupervised.attribute.AddID: Get the name of the attribute to be created
getAttributeNamePrefix() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the attribute name prefix.
getAttributeRange() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Get the range of indices of the attributes used.
getAttributes() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
getAttributes() - Method in class weka.gui.arffviewer.ArffPanel: returns a list with the attributes
getAttributeSelectionMethod() - Method in class weka.classifiers.functions.LinearRegression: Gets the method used to select attributes for use in the linear regression.
getAttributeType() - Method in class weka.filters.unsupervised.attribute.Add: Gets the type of attribute to generate.
getAttributeType() - Method in class weka.filters.unsupervised.attribute.RemoveType: Gets the attribute type to be deleted by the filter.
getAttributeTypes() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Returns the current attribute - attribute-type relation in use.
getAttrIndexRange() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: returns the attribute range(s).
getAttrValue() - Method in class weka.classifiers.rules.JRip.Antd
getAttsToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval: Get the constant rate of attribute elimination per iteration
getAutoBuild() - Method in class weka.classifiers.functions.MultilayerPerceptron
getAutoKeyGeneration() - Method in class weka.core.converters.DatabaseSaver: Gets whether or not a primary key will be generated automatically.
getAverage(int) - Method in class weka.experiment.ResultMatrix: returns the average of the mean at the given position, if the position is valid, otherwise 0
getBackground() - Method in class weka.gui.visualize.BMPWriter: returns the current background color
getBackground() - Method in class weka.gui.visualize.JPEGWriter: returns the current background color.
getBackground() - Method in class weka.gui.visualize.PNGWriter: returns the current background color
getBackground() - Method in class weka.gui.visualize.PostscriptGraphics
getBackup() - Method in class weka.gui.GenericObjectEditor: Returns the backup object (may be null if there is no backup.
getBagSizePercent() - Method in class weka.classifiers.meta.Bagging: Gets the size of each bag, as a percentage of the training set size.
getBagSizePercent() - Method in class weka.classifiers.meta.MetaCost: Gets the size of each bag, as a percentage of the training set size.
getBalanceClass() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Gets whether the class is balanced.
getBalanced() - Method in class weka.classifiers.functions.Winnow: Get the value of Balanced.
getBallSplitter() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Returns the BallSplitter algorithm set that would be used by the TopDown BallTree constructor.
getBallTreeConstructor() - Method in class weka.core.neighboursearch.BallTree: Returns the BallTreeConstructor currently in use.
getBase() - Method in class weka.core.neighboursearch.CoverTree: Returns the base in use for expansion constant.
getBaseExperiment() - Method in class weka.experiment.RemoteExperiment: Get the base experiment used by this remote experiment
getBean() - Method in class weka.gui.beans.BeanInstance: Gets the bean encapsulated in this instance
getBeanContext() - Method in class weka.gui.beans.AbstractDataSource: Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.CostBenefitAnalysis
getBeanContext() - Method in class weka.gui.beans.DataVisualizer: Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.GraphViewer: Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.ModelPerformanceChart: Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.TextViewer: Return the bean context (if any) that this bean is embedded in
getBeanDescriptor() - Method in class weka.gui.beans.AssociatorBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.ClassAssignerBeanInfo
getBeanDescriptor() - Method in class weka.gui.beans.ClassifierBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.ClassValuePickerBeanInfo
getBeanDescriptor() - Method in class weka.gui.beans.ClustererBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo: Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.FilterBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo: Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.LoaderBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.PredictionAppenderBeanInfo: Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.SaverBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.SerializedModelSaverBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.StripChartBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo: Get the bean descriptor for this bean
getBeanInfoInputs() - Method in class weka.gui.beans.MetaBean
getBeanInfoOutputs() - Method in class weka.gui.beans.MetaBean
getBeanInfoSubFlow() - Method in class weka.gui.beans.MetaBean
getBeanInstances() - Static method in class weka.gui.beans.BeanInstance: Return the list of displayed beans
getBeansInInputs() - Method in class weka.gui.beans.MetaBean: Return all the beans in the inputs
getBeansInOutputs() - Method in class weka.gui.beans.MetaBean: Return all the beans in the outputs
getBeansInSubFlow() - Method in class weka.gui.beans.MetaBean: Return all the beans in the sub flow
getBestClassifier() - Method in class weka.classifiers.meta.GridSearch: returns the best Classifier setup
getBestClassifierIndex() - Method in class weka.classifiers.meta.MultiScheme: Get the index of the classifier that was determined as best during cross-validation.
getBestClassifierOptions() - Method in class weka.classifiers.meta.CVParameterSelection: Returns (a copy of) the best options found for the classifier.
getBestCommitteeChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the best committee chunk size
getBestCommitteeErrorEstimate() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the best committee's error on the validation data
getBestCommitteeLLEstimate() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the best committee's log likelihood on the validation data
getBestCommitteeSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the number of members in the best committee
getBestFilter() - Method in class weka.classifiers.meta.GridSearch: returns the best filter setup
getBestgen(ScatterSearchV1.Subset, BitSet) - Method in class weka.attributeSelection.ScatterSearchV1: Evaluate each gen of a BitSet inserted in a Subset and get the most significant for that Subset
getBestGroup() - Method in class weka.attributeSelection.LFSMethods
getBestGroupOfSize(int) - Method in class weka.attributeSelection.LFSMethods
getBestMerit() - Method in class weka.attributeSelection.LFSMethods
getBeta() - Method in class weka.classifiers.functions.Winnow: Get the value of Beta.
getBias() - Method in class weka.classifiers.BVDecompose: Get the calculated bias squared
getBias() - Method in class weka.classifiers.functions.LibLINEAR: Returns bias term value (default 1) No bias term is added if value < 0
getBias() - Method in class weka.classifiers.misc.VFI: Get the value of the bias parameter
getBiasToUniformClass() - Method in class weka.filters.supervised.instance.Resample: Gets the bias towards a uniform class.
getBIFFile() - Method in class weka.classifiers.bayes.BayesNet: Get name of network in BIF file to compare with
getBIFFile() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Get name of network in BIF file to read structure from
getBIFHeader() - Method in class weka.classifiers.bayes.BayesNet
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: get whether numeric attributes are just being binarized.
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.InfoGainAttributeEval: get whether numeric attributes are just being binarized.
getBinaryAttributesNominal() - Method in class weka.filters.supervised.attribute.NominalToBinary: Gets if binary attributes are to be treated as nominal ones.
getBinaryAttributesNominal() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets if binary attributes are to be treated as nominal ones.
getBinarySplits() - Method in class weka.classifiers.rules.PART: Get the value of binarySplits.
getBinarySplits() - Method in class weka.classifiers.trees.J48: Get the value of binarySplits.
getBinarySplits() - Method in class weka.classifiers.trees.J48graft: Get the value of binarySplits.
getBins() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the number of bins numeric attributes will be divided into
getBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Ignored
getBinSplit() - Method in class weka.classifiers.trees.FT: Get the value of binarySplits.
getBinValue() - Method in class weka.clusterers.XMeans: Gets value that represents true in a new numeric attribute.
getBooleanCols() - Method in class weka.datagenerators.ClusterGenerator: returns the range of boolean attributes.
getBuilder() - Method in class weka.core.xml.XMLDocument: returns the DocumentBuilder.
getBuildLogisticModels() - Method in class weka.classifiers.functions.SMO: Get the value of buildLogisticModels.
getBuildLogisticModels() - Method in class weka.classifiers.mi.MISMO: Get the value of buildLogisticModels.
getBuildRegressionTree() - Method in class weka.classifiers.trees.m5.M5Base: Get the value of regressionTree.
getC() - Method in class weka.classifiers.functions.SMO: Get the value of C.
getC() - Method in class weka.classifiers.functions.SMOreg: Get the value of C.
getC() - Method in class weka.classifiers.mi.MISMO: Get the value of C.
getC() - Method in class weka.classifiers.mi.MISVM: Get the value of C.
getCacheHits() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: return the number of kernel cache hits
getCacheKeyName() - Method in class weka.experiment.DatabaseResultListener: Get the value of CacheKeyName.
getCacheSize() - Method in class weka.classifiers.functions.LibSVM: Gets cache memory size in MB
getCacheSize() - Method in class weka.classifiers.functions.supportVector.CachedKernel: Gets the size of the cache
getCacheSize() - Method in class weka.classifiers.functions.supportVector.StringKernel: Gets the size of the cache
getCacheValues(double) - Method in class weka.classifiers.lazy.kstar.KStarCache: Returns the values in the cache mapped by the specified key
getCalcOutOfBag() - Method in class weka.classifiers.meta.Bagging: Get whether the out of bag error is calculated.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.GreedyStepwise: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.RaceSearch: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.Ranker: Gets the calculated number to select.
getCalculateStdDevs() - Method in class weka.experiment.AveragingResultProducer: Get the value of CalculateStdDevs.
getCanChangeClassInDialog() - Method in class weka.gui.GenericObjectEditor: Returns whether the user can change the class in the dialog.
getCapabilities() - Method in class weka.associations.AbstractAssociator: Returns the Capabilities of this associator.
getCapabilities() - Method in class weka.associations.Apriori: Returns default capabilities of the classifier.
getCapabilities() - Method in interface weka.associations.Associator: Returns the Capabilities of this associator.
getCapabilities() - Method in class weka.associations.FilteredAssociator: Returns default capabilities of the associator.
getCapabilities() - Method in class weka.associations.FPGrowth: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the Capabilities of the algorithm.
getCapabilities() - Method in class weka.associations.PredictiveApriori: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.associations.SingleAssociatorEnhancer: Returns default capabilities of the base associator.
getCapabilities() - Method in class weka.associations.Tertius: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.attributeSelection.ASEvaluation: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.CfsSubsetEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.ConsistencySubsetEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.attributeSelection.FilteredAttributeEval: Returns default capabilities of the evaluator.
getCapabilities() - Method in class weka.attributeSelection.FilteredSubsetEval: Returns default capabilities of the evaluator.
getCapabilities() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.OneRAttributeEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.PrincipalComponents: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.SVMAttributeEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.classifiers.bayes.AODE: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.AODEsr: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: This method tests what kind of data this classifier can handle.
getCapabilities() - Method in class weka.classifiers.bayes.BayesNet: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.DMNBtext: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.HNB: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayes: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.NaiveBayesSimple: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.WAODE: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.Classifier: Returns the Capabilities of this classifier.
getCapabilities() - Method in class weka.classifiers.functions.GaussianProcesses: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.IsotonicRegression: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.LeastMedSq: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.LibLINEAR: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.LibSVM: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.LinearRegression: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.Logistic: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.MultilayerPerceptron: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.PaceRegression: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.PLSClassifier: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.RBFNetwork: Returns default capabilities of the classifier, i.e., and "or" of Logistic and LinearRegression.
getCapabilities() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.SimpleLogistic: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.SMO: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.SMOreg: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.SPegasos: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.Puk: Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.functions.VotedPerceptron: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.functions.Winnow: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.IB1: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.IBk: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.KStar: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.LBR: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.lazy.LWL: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.AdaBoostM1: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.AdditiveRegression: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.ClassificationViaClustering: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.ClassificationViaRegression: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.CVParameterSelection: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.Decorate: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.END: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.FilteredClassifier: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.GridSearch: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.LogitBoost: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.MetaCost: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.MultiClassClassifier: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.nestedDichotomies.ND: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.Stacking: Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
getCapabilities() - Method in class weka.classifiers.meta.ThresholdSelector: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.meta.Vote: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.CitationKNN: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MDD: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIBoost: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIDD: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIEMDD: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MILR: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MINND: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIOptimalBall: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MISMO: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MISVM: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.MIWrapper: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.SimpleMI: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel: Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel: Returns the Capabilities of this kernel.
getCapabilities() - Method in class weka.classifiers.misc.HyperPipes: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.misc.SerializedClassifier: Returns default capabilities of the base classifier.
getCapabilities() - Method in class weka.classifiers.misc.VFI: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.MultipleClassifiersCombiner: Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
getCapabilities() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.DecisionTable: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.DTNB: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.JRip: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.NNge: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.OneR: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.PART: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.part.MakeDecList: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.Prism: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.Ridor: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.rules.ZeroR: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.SingleClassifierEnhancer: Returns default capabilities of the base classifier.
getCapabilities() - Method in class weka.classifiers.trees.ADTree: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.BFTree: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.DecisionStump: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.FT: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.Id3: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree: Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.J48: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree: Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree: Returns default capabilities of the classifier tree.
getCapabilities() - Method in class weka.classifiers.trees.J48graft: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.LADTree: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.LMT: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.m5.M5Base: Returns default capabilities of the classifier, i.e., of LinearRegression.
getCapabilities() - Method in class weka.classifiers.trees.NBTree: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.RandomForest: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.RandomTree: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.REPTree: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.SimpleCart: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.trees.UserClassifier: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.clusterers.AbstractClusterer: Returns the Capabilities of this clusterer.
getCapabilities() - Method in class weka.clusterers.CLOPE: Returns default capabilities of the clusterer.
getCapabilities() - Method in interface weka.clusterers.Clusterer: Returns the Capabilities of this clusterer.
getCapabilities() - Method in class weka.clusterers.Cobweb: Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.DBSCAN: Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.EM: Returns default capabilities of the clusterer (i.e., the ones of SimpleKMeans).
getCapabilities() - Method in class weka.clusterers.FarthestFirst: Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.FilteredClusterer: Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.HierarchicalClusterer
getCapabilities() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns default capabilities of the clusterer (i.e., of the wrapper clusterer).
getCapabilities() - Method in class weka.clusterers.OPTICS: Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.sIB: Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.SimpleKMeans: Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.SingleClustererEnhancer: Returns default capabilities of the clusterer.
getCapabilities() - Method in class weka.clusterers.XMeans: Returns default capabilities of the clusterer.
getCapabilities() - Method in interface weka.core.CapabilitiesHandler: Returns the capabilities of this object.
getCapabilities() - Method in class weka.core.converters.AbstractSaver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.ArffSaver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.C45Saver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.CSVSaver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.DatabaseSaver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.LibSVMSaver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.SerializedInstancesSaver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.SVMLightSaver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.converters.XRFFSaver: Returns the Capabilities of this saver.
getCapabilities() - Method in class weka.core.FindWithCapabilities: The capabilities to search for.
getCapabilities() - Method in class weka.estimators.DiscreteEstimator: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.estimators.Estimator: Returns the Capabilities of this Estimator.
getCapabilities() - Method in class weka.estimators.KernelEstimator: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.estimators.MahalanobisEstimator: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.estimators.NormalEstimator: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.estimators.PoissonEstimator: Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.filters.AllFilter: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.Filter: Returns the Capabilities of this filter.
getCapabilities(Instances) - Method in class weka.filters.Filter: Returns the Capabilities of this filter, customized based on the data.
getCapabilities() - Method in class weka.filters.MultiFilter: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.AddClassification: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.Discretize: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.instance.Resample: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.instance.SMOTE: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Add: Returns the Capabilities of this filter.
getCapabilities(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns the Capabilities of this filter, makes sure that the class is never set (for the clusterer).
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddID: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.AddValues: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Center: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ClassAssigner: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns the Capabilities of this filter.
getCapabilities(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns the Capabilities of this filter, makes sure that the class is never set (for the clusterer).
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Copy: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.MathExpression: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NominalToString: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Obfuscate: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Returns the capabilities of this evaluator.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Remove: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Reorder: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Standardize: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.SwapValues: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.attribute.Wavelet: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.Normalize: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.Randomize: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.Resample: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Returns the Capabilities of this filter.
getCapabilities() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog: returns the currently selected capabilities.
getCapabilitiesFilter() - Method in class weka.gui.ConverterFileChooser: returns the capabilities filter for the savers, can be null if all are listed.
getCapabilitiesFilter() - Method in class weka.gui.GenericObjectEditor: Returns the current Capabilities filter, can be null.
getCar() - Method in class weka.associations.Apriori: Gets whether class association ruels are mined
getCar() - Method in class weka.associations.PredictiveApriori: Gets whether class association ruels are mined
getCardinality(int) - Method in class weka.classifiers.bayes.BayesNet: get number of values a node can take
getCardinality() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Gets the cardinality of the attributes (incl class attribute)
getCardinalityOfParents() - Method in class weka.classifiers.bayes.net.ParentSet: returns cardinality of parents
getCell(int, int) - Method in class weka.classifiers.CostMatrix: Return the contents of a particular cell.
getCellEditor(int, int) - Method in class weka.gui.arffviewer.ArffTable: returns the cell editor for the given cell
getCells() - Method in class weka.gui.sql.ResultSetHelper: returns an 2-dimensional array with the content of the resultset, the first dimension is the row, the second the column (i.e., getCells()[y][x]).
getCenter() - Method in class weka.gui.treevisualizer.Node: Get the value of center.
getCenterData() - Method in class weka.attributeSelection.PrincipalComponents: Get whether to center (rather than standardize) the data.
getCenterData() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Get whether to center (rather than standardize) the data.
getChangeInWeights() - Method in class weka.classifiers.functions.neural.NeuralNode: call this function to get the chnage in weights array.
getChar() - Method in class weka.core.Trie.TrieNode: returns the stored character
getCharSet() - Method in class weka.core.converters.TextDirectoryLoader: Get the character set to use when reading text files.
getChecked(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: returns the checked state of the element at the given index
getChecked(int) - Method in class weka.gui.CheckBoxList: returns the checked state of the element at the given index
getCheckedIndices() - Method in class weka.gui.CheckBoxList: returns an array with the indices of all checked items
getCheckErrorRate() - Method in class weka.classifiers.rules.JRip: Gets whether to check for error rate is in stopping criterion
getChecksTurnedOff() - Method in class weka.classifiers.functions.SMO: Returns whether the checks are turned off or not.
getChecksTurnedOff() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns whether the checks are turned off or not.
getChecksTurnedOff() - Method in class weka.classifiers.mi.MISMO: Returns whether the checks are turned off or not.
getChecksTurnedOff() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns whether the checks are turned off or not.
getChild(int) - Method in class weka.gui.treevisualizer.Node: Get the Edge for the child number 'i'.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.Splitter: Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the child for a branch of the split.
getChildren(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: return list of children of a node
getChildren() - Method in class weka.classifiers.trees.adtree.PredictionNode: Gets the children of this node.
getChildTags(Node) - Static method in class weka.core.xml.XMLDocument: returns all non tag-children from the given node.
getChildTags(Node, String) - Static method in class weka.core.xml.XMLDocument: returns all non tag-children from the given node.
getChooseClassPopupMenu() - Method in class weka.gui.GenericObjectEditor: Returns a popup menu that allows the user to change the class of object.
getCindex() - Method in class weka.gui.visualize.PlotData2D: Get the currently set colouring index of the data
getCIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute selected for coloring
getClassAttribute() - Method in class weka.gui.beans.ThresholdDataEvent: Return the class attribute for which the threshold data was generated for.
getClassCapabilities() - Method in class weka.core.Capabilities: returns all class capabilities
getClassColumn() - Method in class weka.gui.beans.ClassAssigner
getClassCounts() - Method in class weka.filters.supervised.attribute.ClassOrder: Get the class distribution of the sorted class values.
getClassesToClusters() - Method in class weka.clusterers.ClusterEvaluation: Return the array (ordered by cluster number) of minimum error class to cluster mappings
getClassFlag() - Method in class weka.datagenerators.ClusterGenerator: Gets the class flag.
getClassForIRStatistics() - Method in class weka.experiment.ClassifierSplitEvaluator: Get the value of ClassForIRStatistics.
getClassification() - Method in class weka.associations.Tertius: Get the value of classification.
getClassifier() - Method in class weka.attributeSelection.ClassifierSubsetEval: Get the classifier used as the base learner.
getClassifier() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the classifier used as the base learner.
getClassifier() - Method in class weka.classifiers.BVDecompose: Gets the name of the classifier being analysed
getClassifier() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets the name of the classifier being analysed
getClassifier() - Method in class weka.classifiers.CheckClassifier: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.CheckSource: Gets the classifier being used for the tests, can be null.
getClassifier(int) - Method in class weka.classifiers.meta.MultiScheme: Gets a single classifier from the set of available classifiers.
getClassifier(int) - Method in class weka.classifiers.MultipleClassifiersCombiner: Gets a single classifier from the set of available classifiers.
getClassifier() - Method in class weka.classifiers.SingleClassifierEnhancer: Get the classifier used as the base learner.
getClassifier() - Method in class weka.experiment.ClassifierSplitEvaluator: Get the value of Classifier.
getClassifier() - Method in class weka.experiment.RegressionSplitEvaluator: Get the value of Classifier.
getClassifier() - Method in class weka.filters.supervised.attribute.AddClassification: Gets the classifier used by the filter.
getClassifier() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the classifier used by the filter.
getClassifier() - Method in class weka.gui.beans.BatchClassifierEvent: Get the classifier
getClassifier() - Method in class weka.gui.beans.Classifier: Get the classifier currently set for this wrapper
getClassifier() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the classifier
getClassifier() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default classifier (fully configured) for the classify panel.
getClassifierCostSensitiveEval() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether the evaluation of the classifier is done cost-sensitively.
getClassifierCrossvalidationFolds() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default number of folds of the CV in the classify panel.
getClassifierOutputAdditionalAttributes() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the string with the additional indices to output alongside the predictions.
getClassifierOutputConfusionMatrix() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether the confusion matrix for the classifier is output.
getClassifierOutputEntropyEvalMeasures() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether entropy-based evaluation meastures of the classifier are output.
getClassifierOutputModel() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether the built model is output.
getClassifierOutputPerClassStats() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether additional per-class stats of the classifier are output.
getClassifierOutputPredictions() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether the predictions of the classifier are output as well.
getClassifierOutputSourceCode() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether the source of a sourcable Classifier is output in the classify tab.
getClassifierPercentageSplit() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default classifier test mode for the classify panel (0-99).
getClassifierPreserveOrder() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether the order is preserved in case of the percentage split in the classify tab.
getClassifierRandomSeed() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default random seed value for the classifier for the classify panel.
getClassifiers() - Method in class weka.classifiers.meta.MultiScheme: Gets the list of possible classifers to choose from.
getClassifiers() - Method in class weka.classifiers.MultipleClassifiersCombiner: Gets the list of possible classifers to choose from.
getClassifierSourceCodeClass() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default classname for a sourcable Classifier in the classify tab.
getClassifierStorePredictionsForVis() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether the predictions of the classifier are stored for visualization.
getClassifierTemplate() - Method in class weka.gui.beans.Classifier: Return the classifier template currently in use.
getClassifierTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default classifier test mode for the classify panel.
getClassifyIterations() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets the number of times an instance is classified
getClassIndex() - Method in class weka.associations.Apriori: Gets the class index
getClassIndex() - Method in class weka.associations.FilteredAssociator: Gets the class index
getClassIndex() - Method in class weka.associations.PredictiveApriori: Gets the index of the class attribute
getClassIndex() - Method in class weka.associations.Tertius: Get the value of classIndex.
getClassIndex() - Method in class weka.classifiers.BVDecompose: Get the index (starting from 1) of the attribute used as the class.
getClassIndex() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the index (starting from 1) of the attribute used as the class.
getClassIndex() - Method in class weka.classifiers.CheckSource: Gets the class index of the dataset.
getClassIndex() - Method in class weka.core.converters.LibSVMSaver: Get the index of the class attribute.
getClassIndex() - Method in class weka.core.converters.SVMLightSaver: Get the index of the class attribute.
getClassIndex() - Method in class weka.core.converters.XRFFSaver: Get the index of the class attribute.
getClassIndex() - Method in class weka.core.FindWithCapabilities: returns the current current class index, -1 if no class attribute.
getClassIndex() - Method in class weka.core.TestInstances: returns the current class index (0-based), -1 is last attribute
getClassIndex() - Method in class weka.filters.CheckSource: Gets the class index of the dataset.
getClassIndex() - Method in class weka.filters.unsupervised.attribute.ClassAssigner: returns the class index.
getClassIndex() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the attribute on which misclassifications are based.
getClassMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: returns all the class/package matches with the partial search string.
getClassname() - Method in class weka.core.Javadoc: returns the current classname
getClassname() - Method in class weka.core.ListOptions: returns the current classname
getClassName() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the class containing the transformation method.
getClassname(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: returns the classname part of the partial classname.
getClassnames(String) - Static method in class weka.gui.GenericObjectEditor: Returns the available classnames for a certain property in the props file.
getClassOrder() - Method in class weka.filters.supervised.attribute.ClassOrder: Get the wanted class order
getClassPriors() - Method in class weka.classifiers.Evaluation: Get the current weighted class counts
getClassType() - Method in class weka.core.TestInstances: returns the current class type
getClassValue() - Method in class weka.filters.supervised.instance.SMOTE: Gets the index of the class value to which SMOTE should be applied.
getClassValue() - Method in class weka.gui.beans.ClassValuePicker: Gets the class value considered to be the "positive" class value.
getClearEachDataset() - Method in class weka.gui.streams.InstanceViewer
getClip() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
getClipBounds() - Method in class weka.gui.visualize.PostscriptGraphics: This returns the full current drawing area
getClipBounds(Rectangle) - Method in class weka.gui.visualize.PostscriptGraphics: This returns the full current drawing area
getClipRect() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
getClock() - Method in class weka.core.Debug: returns the instance of the Clock that is internally used
getClosestConnections(Point, int) - Static method in class weka.gui.beans.BeanConnection: Return a list of connections within some delta of a point
getClosestConnectorPoint(Point) - Method in class weka.gui.beans.BeanVisual: Returns the coordinates of the closest "connector" point to the supplied point.
getCloseTo() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Get the "close to" number.
getCloseToDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Get the "close to" default.
getCloseToTolerance() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Get the "close to" Tolerance.
getClusterAssignments() - Method in class weka.clusterers.ClusterEvaluation: Return an array of cluster assignments corresponding to the most recent set of instances clustered.
getClusterCenters() - Method in class weka.clusterers.XMeans: Return the centers of the clusters as an Instances object
getClusterCentroids() - Method in class weka.clusterers.SimpleKMeans: Gets the the cluster centroids
getClusterDefinitions() - Method in class weka.datagenerators.clusterers.SubspaceCluster: returns the currently set clusters
getClusterer() - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator: Get the clusterer
getClusterer() - Method in class weka.classifiers.meta.ClassificationViaClustering: Get the clusterer used as the base learner.
getClusterer() - Method in class weka.clusterers.CheckClusterer: Get the clusterer used as the clusterer
getClusterer() - Method in class weka.clusterers.MakeDensityBasedClusterer: Gets the clusterer being wrapped.
getClusterer() - Method in class weka.clusterers.SingleClustererEnhancer: Get the clusterer used as the base clusterer.
getClusterer() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Get the value of clusterer
getClusterer() - Method in class weka.filters.unsupervised.attribute.AddCluster: Gets the clusterer used by the filter.
getClusterer() - Method in class weka.gui.beans.BatchClustererEvent: Get the clusterer
getClusterer() - Method in class weka.gui.beans.Clusterer: Get the clusterer currently set for this wrapper
getClusterer() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default clusterer (fully configured) for the clusterer panel.
getClustererStoreClustersForVis() - Static method in class weka.gui.explorer.ExplorerDefaults: returns whether the clusters are storeed for visualization purposes in the cluster panel.
getClustererTestMode() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default cluster test mode for the cluster panel.
getClusteringSeed() - Method in class weka.classifiers.functions.RBFNetwork: Get the random seed used by K-means.
getClusterLabel() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Returns the clusterID, to which this DataObject belongs to
getClusterLabel() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Returns the clusterID, to which this DataObject belongs to
getClusterLabel() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Returns the clusterID, to which this DataObject belongs to
getClusterModelsNumericAtts() - Method in class weka.clusterers.EM: Return the normal distributions for the cluster models
getClusterNominalCounts() - Method in class weka.clusterers.SimpleKMeans: Returns for each cluster the frequency counts for the values of each nominal attribute
getClusterPriors() - Method in class weka.clusterers.EM: Return the priors for the clusters
getClusterSizes() - Method in class weka.clusterers.SimpleKMeans: Gets the number of instances in each cluster
getClusterStandardDevs() - Method in class weka.clusterers.SimpleKMeans: Gets the standard deviations of the numeric attributes in each cluster
getClusterSubType() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Gets the cluster sub type.
getClusterType() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Gets the cluster type.
getCoef0() - Method in class weka.classifiers.functions.LibSVM: Gets coef
getCoefficients() - Method in class weka.core.matrix.LinearRegression: returns the calculated coefficients
getColCount() - Method in class weka.experiment.ResultMatrix: returns the number of columns
getColHidden(int) - Method in class weka.experiment.ResultMatrix: returns the hidden status of the column, if the index is valid, otherwise false
getColName(int) - Method in class weka.experiment.ResultMatrix: returns the name of the row, if the index is valid, otherwise null.
getColNameWidth() - Method in class weka.experiment.ResultMatrix: returns the current width for the column names
getColor() - Method in class weka.gui.treevisualizer.Node: Get the value of color.
getColor() - Method in class weka.gui.visualize.PostscriptGraphics: Get current pen color.
getColorBox() - Method in class weka.gui.AttributeVisualizationPanel: Returns the class selection combo box if the parent component wants to place it in itself or in some component other than this component.
getColOrder() - Method in class weka.experiment.ResultMatrix: returns the current order of the columns, null means the default order
getColoringIndex() - Method in class weka.gui.AttributeVisualizationPanel: Get the coloring (class) index for the plot
getColoringIndex() - Method in class weka.gui.beans.AttributeSummarizer: Return the coloring index for the attribute summary plots
getColors() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the current vector of Color objects used for the classes
getColumn(int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Return a DoubleVector that stores a column of the matrix
getColumn(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Return a DoubleVector that stores some elements of a column of the matrix
getColumn(int) - Method in class weka.core.Matrix: Deprecated.

Gets a column of the matrix and returns it as a double array.
getColumn() - Static method in class weka.gui.experiment.ExperimenterDefaults: the comma-separated list of attribute names that identify a column
getColumnClass(int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the most specific superclass for all the cell values in the column (always String)
getColumnClass(int) - Method in class weka.gui.SortedTableModel: Returns the most specific superclass for all the cell values in the column.
getColumnClass(int) - Method in class weka.gui.sql.ResultSetTableModel: returns the most specific superclass for all the cell values in the column (always String).
getColumnClasses() - Method in class weka.gui.sql.ResultSetHelper: returns the classes for the columns.
getColumnCount() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel: Returns the number of columns of this model.
getColumnCount() - Method in class weka.gui.arffviewer.ArffTableModel: returns the number of columns in the model
getColumnCount() - Method in class weka.gui.SortedTableModel: Returns the number of columns in the model
getColumnCount() - Method in class weka.gui.sql.ResultSetHelper: returns the number of columns in the resultset.
getColumnCount() - Method in class weka.gui.sql.ResultSetTableModel: returns the number of columns in the model.
getColumnDimension() - Method in class weka.core.matrix.Matrix: Get column dimension.
getColumnName(int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the name of the column at columnIndex
getColumnName(int) - Method in class weka.gui.SortedTableModel: Returns the name of the column at columnIndex
getColumnName(int) - Method in class weka.gui.sql.ResultSetTableModel: returns the name of the column at columnIndex.
getColumnNames() - Method in class weka.gui.sql.ResultSetHelper: returns an array with the names of the columns in the resultset.
getColumnPackedCopy() - Method in class weka.core.matrix.Matrix: Make a one-dimensional column packed copy of the internal array.
getCombination() - Method in class weka.attributeSelection.ScatterSearchV1: Get the combination
getCombinationRule() - Method in class weka.classifiers.meta.Vote: Gets the combination rule used
getCommand() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
getComment() - Method in enum weka.core.TechnicalInformation.Field: returns the comment string
getComment() - Method in enum weka.core.TechnicalInformation.Type: returns the comment string
getCommonPrefix() - Method in class weka.core.Trie: returns the common prefix for all the nodes
getCommonPrefix() - Method in class weka.core.Trie.TrieNode: returns the common prefix for all the nodes starting with this node.
getCommonPrefix(String) - Method in class weka.core.Trie.TrieNode: returns the common prefix for all the nodes starting with the node for the specified prefix.
getCommonPrefix(Vector<String>) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: returns the common prefix for all the items in the list.
getComparisonField() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the name of the field used for comparison
getCompatibilityState() - Method in class weka.experiment.AveragingResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.CrossValidationResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.DatabaseResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.LearningRateResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.RandomSplitResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in interface weka.experiment.ResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getComplexityParameter() - Method in class weka.attributeSelection.SVMAttributeEval: Get the value of C used with SMO
getComponent() - Method in class weka.gui.visualize.JComponentWriter: returns the component that is stored in the output format
getComponent() - Method in class weka.gui.visualize.PrintableComponent: returns the GUI component this print dialog is part of.
getComposite() - Method in class weka.gui.visualize.PostscriptGraphics
getCompressOutput() - Method in class weka.core.converters.ArffSaver: Gets whether the output data is compressed.
getCompressOutput() - Method in class weka.core.converters.XRFFSaver: Gets whether the output data is compressed.
getConfidenceFactor() - Method in class weka.classifiers.rules.PART: Get the value of CF.
getConfidenceFactor() - Method in class weka.classifiers.trees.J48: Get the value of CF.
getConfidenceFactor() - Method in class weka.classifiers.trees.J48graft: Get the value of CF.
getConfirmation() - Method in class weka.associations.tertius.Rule: Get the confirmation value of this rule.
getConfirmationThreshold() - Method in class weka.associations.Tertius: Get the value of confirmationThreshold.
getConfirmationValues() - Method in class weka.associations.Tertius: Get the value of confirmationValues.
getConfirmExit() - Method in class weka.gui.arffviewer.ArffViewer: returns the setting of whether to display a confirm messagebox or not on exit
getConfirmExit() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the setting of whether to display a confirm messagebox or not on exit
getConfusionMatrix() - Method in class weka.classifiers.evaluation.TwoClassStats: Generates a ConfusionMatrix representing the current two-class statistics, using class names "negative" and "positive".
getConnectedFormat() - Method in class weka.gui.beans.ClassAssigner: Returns the structure of the incoming instances (if any)
getConnectedFormat() - Method in class weka.gui.beans.ClassValuePicker: Returns the structure of the incoming instances (if any)
getConnection() - Method in class weka.gui.sql.DbUtils: returns the current database connection.
getConnections() - Static method in class weka.gui.beans.BeanConnection: Returns the list of connections
getConnectorPoint(int) - Method in class weka.gui.beans.BeanVisual: Returns the coordinates of the connector point given a compass point
getConsequence() - Method in class weka.associations.FPGrowth.AssociationRule: Get the consequence of this rule.
getConsequenceSupport() - Method in class weka.associations.FPGrowth.AssociationRule: Get the support for the consequence.
getConsequent() - Method in class weka.classifiers.rules.JRip.RipperRule: Gets the internal representation of the class label to be predicted
getConsequent() - Method in class weka.classifiers.rules.Rule: Get the consequent of this rule, i.e.
getConservativeForwardSelection() - Method in class weka.attributeSelection.GreedyStepwise: Gets whether conservative selection has been enabled
getConstError(double[]) - Method in class weka.classifiers.trees.ft.FTtree
getContainChildBalls() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Gets whether if a parent ball should completely enclose its two child balls.
getContent(Element) - Method in class weka.classifiers.bayes.net.BIFReader: Returns all TEXT children of the given node in one string.
getContent(Element) - Method in class weka.classifiers.bayes.net.EditableBayesNet: XML helper function.
getContent(Element) - Static method in class weka.core.xml.XMLDocument: returns the text between the opening and closing tag of a node (performs a trim() on the result).
getControlPanel() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: This method returns a handle to the extra controls panel, so that the visualizing class can add it to some of it's own gui panel.
getControlPanel() - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method returns the extra controls panel for the LayoutEngine, if there is any.
getConvertNominal() - Method in class weka.classifiers.trees.LMT: Get the value of convertNominal.
getConvertNominalToBinary() - Method in class weka.classifiers.functions.LibLINEAR: Gets whether conversion of nominal to binary is turned on.
getCoreConvertersOnly() - Method in class weka.gui.ConverterFileChooser: Returns whether only the hardcoded core converters are displayed.
getCoreDistance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Returns the coreDistance for this dataObject
getCoreDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Returns the coreDistance for this dataObject
getCoreDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Returns the coreDistance for this dataObject
getCost() - Method in class weka.classifiers.functions.LibLINEAR: Returns the cost parameter C
getCost() - Method in class weka.classifiers.functions.LibSVM: Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR
getCostMatrix() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Gets the misclassification cost matrix.
getCostMatrix() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the misclassification cost matrix.
getCostMatrix() - Method in class weka.classifiers.meta.MetaCost: Gets the misclassification cost matrix.
getCostMatrixSource() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Gets the source location method of the cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the source location method of the cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.MetaCost: Gets the source location method of the cost matrix.
getCount(double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Get a counts for a value
getCount(double) - Method in class weka.estimators.DiscreteEstimator: Get the count for a value
getCount(int) - Method in class weka.experiment.ResultMatrix: returns the count for the row.
getCount(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the number of visible nodes there are (this may accidentally count some of the invis nodes).
getCounterInstancesFrequency() - Method in class weka.associations.tertius.LiteralSet: Get the frequency of counter-instances of this LiteralSet in the data.
getCounterInstancesNumber() - Method in class weka.associations.tertius.LiteralSet: Get the number of counter-instances of this LiteralSet.
getCounts(int[], int[], int[], int, int, boolean) - Method in class weka.classifiers.bayes.net.ADNode: get counts for specific instantiation of a set of nodes
getCounts(int[], int[], int[], int, int, ADNode, boolean) - Method in class weka.classifiers.bayes.net.VaryNode: get counts for specific instantiation of a set of nodes
getCountWidth() - Method in class weka.experiment.ResultMatrix: returns the current width for the counts
getCover() - Method in class weka.classifiers.rules.JRip.Antd
getCreatorApplication() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Get the name of the application that created this model
getCreatorApplication() - Method in interface weka.core.pmml.PMMLModel: Get the name of the application that created this model.
getCriticalValue() - Method in class weka.classifiers.bayes.AODEsr: Gets the critical value.
getCrossoverProb() - Method in class weka.attributeSelection.GeneticSearch: get the probability of crossover
getCrossVal() - Method in class weka.classifiers.rules.DecisionTable: Gets the number of folds for cross validation
getCrossValidate() - Method in class weka.classifiers.lazy.IBk: Gets whether hold-one-out cross-validation will be used to select the best k value.
getCurrent() - Method in class weka.core.Memory: returns the currently used size of the JVM heap, obtains a fresh MemoryUsage object to do so.
getCurrentDatasetNumber() - Method in class weka.experiment.Experiment: When an experiment is running, this returns the current dataset number.
getCurrentDir() - Static method in class weka.core.Debug: returns the current working directory of the user
getCurrentFilename() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the filename of the current tab
getCurrentIndex() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the currently selected tab index
getCurrentInstance() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the current instance
getCurrentModel() - Method in class weka.classifiers.misc.SerializedClassifier: Gets the currently loaded model (can be null).
getCurrentPanel() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the currently selected panel
getCurrentPropertyNumber() - Method in class weka.experiment.Experiment: When an experiment is running, this returns the index of the current custom property value.
getCurrentRunNumber() - Method in class weka.experiment.Experiment: When an experiment is running, this returns the current run number.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.CostCurve: Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.CostCurve: Calculates the performance stats for the desired class and return results as a set of Instances.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.MarginCurve: Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the performance stats for the desired class and return results as a set of Instances.
getCustomEditor() - Method in class weka.gui.CostMatrixEditor: Gets a GUI component with which the user can edit the cost matrix.
getCustomEditor() - Method in class weka.gui.FileEditor: Gets the custom editor component.
getCustomEditor() - Method in class weka.gui.GenericArrayEditor: Returns the array editing component.
getCustomEditor() - Method in class weka.gui.GenericObjectEditor: Returns the array editing component.
getCustomEditor() - Method in class weka.gui.SimpleDateFormatEditor: Gets a GUI component with which the user can edit the date format.
getCustomHeight() - Method in class weka.gui.visualize.JComponentWriter: gets the custom height currently used
getCustomName() - Method in class weka.gui.beans.Associator: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in interface weka.gui.beans.BeanCommon: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.ClassAssigner: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Classifier: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.ClassValuePicker: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Clusterer: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.CostBenefitAnalysis: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.CrossValidationFoldMaker: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Filter: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Loader: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.MetaBean: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.PredictionAppender: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.Saver: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.SerializedModelSaver: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.StripChart: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.TestSetMaker: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.TextViewer: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.TrainingSetMaker: Get the custom (descriptive) name for this bean (if one has been set)
getCustomName() - Method in class weka.gui.beans.TrainTestSplitMaker: Get the custom (descriptive) name for this bean (if one has been set)
getCustomPanel() - Method in interface weka.gui.CustomPanelSupplier: Gets the custom panel for the object.
getCustomPanel() - Method in class weka.gui.GenericObjectEditor: Gets the custom panel used for editing the object.
getCustomWidth() - Method in class weka.gui.visualize.JComponentWriter: gets the custom width currently used
getCutoff() - Method in class weka.clusterers.Cobweb: get the cutoff
getCutOffFactor() - Method in class weka.clusterers.XMeans: Gets the cutoff factor.
getCutPoints(int) - Method in class weka.filters.supervised.attribute.Discretize: Gets the cut points for an attribute
getCutPoints(int) - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the cut points for an attribute
getCVisible() - Method in class weka.gui.treevisualizer.Node: Get If this node's childs are visible.
getCVParameter(int) - Method in class weka.classifiers.meta.CVParameterSelection: Gets the scheme paramter with the given index.
getCVParameters() - Method in class weka.classifiers.meta.CVParameterSelection: Get method for CVParameters.
getCVPredictions(Classifier, Instances, int) - Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.
getCVType() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: get cross validation strategy to be used in searching for networks.
getCycleEnd() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the time/date string the cycle ended
getCycleStart() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the time/date string the cycle was started
getD() - Method in class weka.core.matrix.EigenvalueDecomposition: Return the block diagonal eigenvalue matrix
getData() - Method in class weka.attributeSelection.BestFirst.Link2: Get a group
getData() - Method in class weka.attributeSelection.LFSMethods.Link2: Get a group
getData() - Method in class weka.classifiers.rules.RuleStats: Get the data of the stats
getData() - Method in class weka.core.AttributeLocator: returns the underlying data
getData() - Method in class weka.core.converters.ArffLoader.ArffReader: Returns the data that was read
getData() - Method in class weka.core.TestInstances: returns the current dataset, can be null
getDatabase_distanceType() - Method in class weka.clusterers.DBSCAN: Returns the distance-type
getDatabase_distanceType() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the distance-type
getDatabase_distanceType() - Method in class weka.clusterers.OPTICS: Returns the distance-type
getDatabase_Type() - Method in class weka.clusterers.DBSCAN: Returns the type of the used index (database)
getDatabase_Type() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the type of the used index (database)
getDatabase_Type() - Method in class weka.clusterers.OPTICS: Returns the type of the used index (database)
getDatabaseOutput() - Method in class weka.clusterers.OPTICS: Returns the file to save the database to - if directory, database is not saved.
getDatabaseSize() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the database's size
getDatabaseURL() - Method in class weka.experiment.DatabaseUtils: Get the value of DatabaseURL.
getDataDictionary() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Get the data dictionary.
getDataFileName() - Method in class weka.classifiers.BVDecompose: Get the name of the data file used for the decomposition
getDataFileName() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the name of the data file used for the decomposition
getDataObject(String) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Select a dataObject from the database
getDataObject(String) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Select a dataObject from the database
getDataObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement: Returns this dataObject
getDataPoint() - Method in class weka.gui.beans.ChartEvent: Get the data point
getDataSeqID() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the attribute representing the data sequence ID.
getDataset() - Method in class weka.classifiers.CheckSource: Gets the dataset to use for testing, can be null.
getDataSet() - Method in class weka.core.converters.AbstractLoader
getDataSet() - Method in class weka.core.converters.ArffLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.C45Loader: Return the full data set.
getDataSet() - Method in class weka.core.converters.ConverterUtils.DataSource: returns the full dataset, can be null in case of an error.
getDataSet(int) - Method in class weka.core.converters.ConverterUtils.DataSource: returns the full dataset with the specified class index set, can be null in case of an error.
getDataSet() - Method in class weka.core.converters.CSVLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.DatabaseLoader: Return the full data set in batch mode (header and all intances at once).
getDataSet() - Method in class weka.core.converters.LibSVMLoader: Return the full data set.
getDataSet() - Method in interface weka.core.converters.Loader: Return the full data set.
getDataSet() - Method in class weka.core.converters.SerializedInstancesLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.SVMLightLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.TextDirectoryLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.XRFFLoader: Return the full data set.
getDataset() - Method in class weka.filters.CheckSource: Gets the dataset to use for testing, can be null.
getDataSet() - Method in class weka.gui.beans.DataSetEvent: Return the instances of the data set
getDataSet() - Method in class weka.gui.beans.ThresholdDataEvent: Return the instances of the data set
getDataSet() - Method in class weka.gui.beans.VisualizableErrorEvent: Return the instances of the data set
getDatasetFormat() - Method in class weka.datagenerators.DataGenerator: Gets the format of the dataset that is to be generated.
getDatasetKeyColumns() - Method in class weka.experiment.PairedTTester: Get the value of DatasetKeyColumns.
getDatasetKeyColumns() - Method in interface weka.experiment.Tester: Get the value of DatasetKeyColumns.
getDatasets() - Method in class weka.experiment.Experiment: Gets the datasets in the experiment.
getDatasetsFirst() - Static method in class weka.gui.experiment.ExperimenterDefaults: whether datasets or algorithms are iterated first
getDataType() - Method in class weka.gui.beans.xml.XMLBeans: returns the type of data that is to be read/written
getDateAttributes() - Method in class weka.core.converters.CSVLoader: Returns the current attribute range to be forced to type date.
getDateFormat() - Method in class weka.core.Attribute: Returns the Date format pattern in case this attribute is of type DATE, otherwise an empty string.
getDateFormat() - Method in class weka.core.converters.CSVLoader: Get the format to use for parsing date values.
getDateFormat() - Method in class weka.filters.unsupervised.attribute.Add: Get the date format, complying to ISO-8601.
getDateFormat() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Get the date format used in output.
getDbUtils() - Method in class weka.gui.sql.event.ConnectionEvent: returns the DbUtils instance that is responsible for the connect/disconnect.
getDbUtils() - Method in class weka.gui.sql.event.QueryExecuteEvent: returns the DbUtils instance that was executed the query
getDebug() - Method in class weka.associations.GeneralizedSequentialPatterns: Get whether debugging is turned on.
getDebug() - Method in class weka.attributeSelection.RaceSearch: Get whether output is to be verbose
getDebug() - Method in class weka.attributeSelection.ScatterSearchV1: Get whether output is to be verbose
getDebug() - Method in class weka.classifiers.BVDecompose: Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.Classifier: Get whether debugging is turned on.
getDebug() - Method in class weka.classifiers.functions.LeastMedSq: Returns whether or not debugging output shouild be printed
getDebug() - Method in class weka.classifiers.functions.LinearRegression: Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.functions.Logistic: Gets whether debugging output will be printed.
getDebug() - Method in class weka.classifiers.functions.PaceRegression: Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.functions.supportVector.Kernel: Gets whether debugging output is turned on or not.
getDebug() - Method in class weka.classifiers.meta.MultiScheme: Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.rules.JRip: Gets whether debug information is output to the console
getDebug() - Method in class weka.clusterers.EM: Get debug mode
getDebug() - Method in class weka.clusterers.HierarchicalClusterer: Get whether debugging is turned on.
getDebug() - Method in class weka.clusterers.sIB: Get debug mode
getDebug() - Method in class weka.core.Check: Get whether debugging is turned on
getDebug() - Method in class weka.core.converters.TextDirectoryLoader: Gets whether additional debug information is printed.
getDebug() - Method in class weka.core.Debug.Random: returns whether to print the generated random values or not
getDebug() - Method in class weka.datagenerators.DataGenerator: Gets the debug flag.
getDebug() - Method in class weka.estimators.CheckEstimator: Get whether debugging is turned on
getDebug() - Method in class weka.estimators.Estimator: Get whether debugging is turned on.
getDebug() - Method in class weka.experiment.DatabaseUtils: Gets whether there should be printed some debugging output to stderr or not.
getDebug() - Method in class weka.filters.SimpleFilter: Returns the current debugging mode state.
getDebug() - Method in class weka.filters.unsupervised.attribute.AddExpression: Gets whether debug is set
getDebug() - Method in class weka.gui.DatabaseConnectionDialog: Returns the debug flag
getDebug() - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: returns whether debug mode is on.
getDebug() - Method in class weka.gui.streams.InstanceCounter
getDebug() - Method in class weka.gui.streams.InstanceJoiner
getDebug() - Method in class weka.gui.streams.InstanceLoader
getDebug() - Method in class weka.gui.streams.InstanceSavePanel
getDebug() - Method in class weka.gui.streams.InstanceTable
getDebug() - Method in class weka.gui.streams.InstanceViewer
getDebugLevel() - Method in class weka.clusterers.XMeans: Gets the debug level.
getDebugVectorsFile() - Method in class weka.clusterers.XMeans: Gets the file name for a file that has the random vectors stored.
getDecay() - Method in class weka.classifiers.functions.MultilayerPerceptron
getDecimals() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Get the number of decimals to round to.
getDefault() - Method in class weka.core.Tee: returns the default printstrean, can be NULL.
getDefaultValue() - Method in class weka.core.pmml.TargetMetaInfo: Get the default value (numeric target)
getDefaultWeight() - Method in class weka.classifiers.functions.Winnow: Get the value of defaultWeight.
getDegree() - Method in class weka.classifiers.functions.LibSVM: Gets the degree of the kernel
getDegreesOfFreedom() - Method in class weka.experiment.PairedStats: Gets the degrees of freedom.
getDeleteEmptyBins() - Method in class weka.classifiers.meta.RegressionByDiscretization: Gets the number of bins numeric attributes will be divided into
getDelimiters() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer: Get the value of delimiters (not backquoted).
getDelta() - Method in class weka.associations.Apriori: Get the value of delta.
getDelta() - Method in class weka.associations.FPGrowth: Get the value of delta.
getDelta() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
getDelta() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
getDensityBasedClusterer() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Get the clusterer used by this filter
getDerivedFields() - Method in class weka.core.pmml.MiningSchema
getDerivedValue(double[]) - Method in class weka.core.pmml.DerivedFieldMetaInfo: Get the derived field value for the given incoming vector of values.
getDescendantPopulationSize() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getDescendantPopulationSize() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getDescription() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter: The description of this filter.
getDescription() - Method in class weka.gui.ExtensionFileFilter: Gets the description of accepted files.
getDescription() - Method in class weka.gui.visualize.BMPWriter: returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.JComponentWriter: returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.JPEGWriter: returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.PNGWriter: returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.PostscriptWriter: returns the name of the writer, to display in the FileChooser.
getDesignatedClass() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the method to determine which class value to optimize.
getDesignVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin: Get the specific version of Weka the class is designed for.
getDesignVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin: Get the specific version of Weka the class is designed for.
getDesignVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin: Get the specific version of Weka the class is designed for.
getDesignVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin: Get the specific version of Weka the class is designed for.
getDesiredSize() - Method in class weka.classifiers.meta.Decorate: Gets the desired size of the committee.
getDesiredWeightOfInstancesPerInterval() - Method in class weka.filters.unsupervised.attribute.Discretize: Get the DesiredWeightOfInstancesPerInterval value.
getDestination() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default destination
getDetectionPerAttribute() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Gets whether an Outlier/ExtremeValue attribute pair is generated for each numeric attribute ("true") or just one pair for all numeric attributes together ("false").
getDeviceConfiguration() - Method in class weka.gui.visualize.PostscriptGraphics
getDir() - Method in class weka.core.Javadoc: returns the current dir containing the class to update.
getDir() - Method in class weka.gui.Loader: returns the dir prefix
getDirection() - Method in class weka.attributeSelection.BestFirst: Get the search direction
getDirectory() - Method in class weka.core.converters.TextDirectoryLoader: get the Dir specified as the source
getDirectory() - Method in class weka.gui.beans.SerializedModelSaver: Get the directory that the model(s) will be saved into
getDiscretizeBin() - Method in class weka.classifiers.mi.MIBoost: Get the number of bins in discretization
getDiscretizer() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Return the discretizer used at this node
getDisplay() - Method in enum weka.core.TechnicalInformation.Field: returns the display string
getDisplay() - Method in enum weka.core.TechnicalInformation.Type: returns the display string
getDisplayCol(int) - Method in class weka.experiment.ResultMatrix: returns the displayed index of the given col, depending on the order of columns, returns -1 if index out of bounds
getDisplayedResultsets() - Method in class weka.experiment.PairedTTester: Gets the indices of the the datasets that are displayed (if null then all are displayed).
getDisplayedResultsets() - Method in interface weka.experiment.Tester: Gets the indices of the the datasets that are displayed (if null then all are displayed).
getDisplayModelInOldFormat() - Method in class weka.classifiers.bayes.NaiveBayes: Get whether to display model output in the old, original format.
getDisplayModelInOldFormat() - Method in class weka.clusterers.EM: Get whether to display model output in the old, original format.
getDisplayName() - Method in class weka.experiment.PairedCorrectedTTester: returns the name of the tester
getDisplayName() - Method in class weka.experiment.PairedTTester: returns the name of the tester
getDisplayName() - Method in class weka.experiment.ResultMatrix: returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixCSV: returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixGnuPlot: returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixHTML: returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixLatex: returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixPlainText: returns the name of the output format
getDisplayName() - Method in class weka.experiment.ResultMatrixSignificance: returns the name of the output format
getDisplayName() - Method in interface weka.experiment.Tester: returns the name of the testing algorithm
getDisplayRow(int) - Method in class weka.experiment.ResultMatrix: returns the displayed index of the given row, depending on the order of rows, returns -1 if index out of bounds
getDisplayRules() - Method in class weka.classifiers.rules.DecisionTable: Gets whether rules are being printed
getDisplayStdDevs() - Method in class weka.clusterers.SimpleKMeans: Gets whether standard deviations and nominal count Should be displayed in the clustering output
getDisplayValue() - Method in class weka.core.pmml.FieldMetaInfo.Value
getDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement: Returns the distance that was calulcated for this dataObject (The distance between this dataObject and the dataObject for which an epsilon-range-query was performed.)
getDistanceF() - Method in class weka.clusterers.XMeans: Gets the distance function.
getDistanceFunction() - Method in class weka.clusterers.HierarchicalClusterer
getDistanceFunction() - Method in class weka.clusterers.SimpleKMeans: returns the distance function currently in use.
getDistanceFunction() - Method in class weka.core.neighboursearch.KDTree: returns the distance function currently in use.
getDistanceFunction() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: returns the distance function currently in use.
getDistanceIsBranchLength() - Method in class weka.clusterers.HierarchicalClusterer
getDistances() - Method in class weka.core.neighboursearch.BallTree: Returns the distances of the k nearest neighbours.
getDistances() - Method in class weka.core.neighboursearch.CoverTree: Returns the distances of the (k)-NN(s) found earlier by kNearestNeighbours()/nearestNeighbour().
getDistances() - Method in class weka.core.neighboursearch.KDTree: Returns the distances to the kNearest or 1 nearest neighbour currently found with either the kNearestNeighbours or the nearestNeighbour method.
getDistances() - Method in class weka.core.neighboursearch.LinearNNSearch: Returns the distances of the k nearest neighbours.
getDistances() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns the distances of the k nearest neighbours.
getDistanceWeighting() - Method in class weka.classifiers.lazy.IBk: Gets the distance weighting method used.
getDistMult() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the distance multiplier.
getDistribution(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: returns distribution of a node in matrix form with matrix representing distribution with P[i][j] = P(node = j | parent configuration = i)
getDistribution(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: returns distribution of a node in matrix form with matrix representing distribution with P[i][j] = P(node = j | parent configuration = i)
getDistribution() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the current distribution that'll be used for calculating the random matrix
getDistributions() - Method in class weka.classifiers.bayes.BayesNet: Get full set of estimators.
getDistributions(int) - Method in class weka.classifiers.rules.RuleStats: Get the class distribution predicted by the rule in given position
getDistributionSpread() - Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the value for the distribution spread
getDocType() - Method in class weka.core.xml.XMLDocument: returns the current DOCTYPE, can be null.
getDocument() - Method in class weka.core.xml.XMLDocument: returns the parsed DOM document.
getDocument() - Method in class weka.core.xml.XMLOptions: returns the parsed DOM document.
getDoNotOperateOnPerClassBasis() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the DoNotOperateOnPerClassBasis value.
getDoNotReplaceMissingValues() - Method in class weka.classifiers.functions.LibLINEAR: Gets whether automatic replacement of missing values is disabled.
getDoNotReplaceMissingValues() - Method in class weka.classifiers.functions.LibSVM: Gets whether automatic replacement of missing values is disabled.
getDoNotWeightBags() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Gets whether the bags are weighted
getDontFilterAfterFirstBatch() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get whether to apply the filter to instances that arrive once the first (training) batch has been seen.
getDontNormalize() - Method in class weka.classifiers.functions.SPegasos: Get whether normalization has been turned off.
getDontNormalize() - Method in class weka.core.NormalizableDistance: Gets whether if the attribute values are to be normazlied in distance calculation.
getDontReplaceMissing() - Method in class weka.classifiers.functions.SPegasos: Get whether global replacement of missing values has been disabled.
getDontReplaceMissingValues() - Method in class weka.clusterers.SimpleKMeans: Gets whether missing values are to be replaced
getDoublePivot() - Method in class weka.core.matrix.LUDecomposition: Return pivot permutation vector as a one-dimensional double array
getEditor() - Method in class weka.gui.PropertyDialog: Gets the current property editor.
getEditorActive() - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Returns true if the editor is currently in an active status---that is the array is active and able to be edited.
getElapsedTime() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the elapsed-time
getElement(int, int) - Method in class weka.classifiers.CostMatrix: Return the value of a cell as a double (for legacy code)
getElement(int, int, Instance) - Method in class weka.classifiers.CostMatrix: Return the value of a cell as a double.
getElement(int) - Method in class weka.core.AlgVector: Returns the value of a cell in the matrix.
getElement(int, int) - Method in class weka.core.Matrix: Deprecated.

Returns the value of a cell in the matrix.
getElementAt(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Returns the component at the specified index.
getElements() - Method in class weka.core.AlgVector: Gets the elements of the vector and returns them as double array.
getEliminateColinearAttributes() - Method in class weka.classifiers.functions.LinearRegression: Get the value of EliminateColinearAttributes.
getEnabled() - Method in class weka.core.Debug: returns whether the logging is enabled
getEnclosureCharacters() - Method in class weka.core.converters.CSVLoader: Get the character(s) to use/recognize as string enclosures
getEntropicAutoBlend() - Method in class weka.classifiers.lazy.KStar: Get whether entropic blending being used
getEntry(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Returns the table entry to which the specified key is mapped in this hashtable.
getEnumerateColNames() - Method in class weka.experiment.ResultMatrix: returns whether column names or numbers instead are enumerateed
getEnumerateRowNames() - Method in class weka.experiment.ResultMatrix: returns whether row names or numbers instead are enumerateed
getEnvironment() - Method in class weka.gui.beans.FlowRunner: Get the environment variables that are in use.
getEpochs() - Method in class weka.classifiers.functions.SPegasos: Get current number of epochs
getEps() - Method in class weka.classifiers.functions.LibLINEAR: Gets tolerance of termination criterion
getEps() - Method in class weka.classifiers.functions.LibSVM: Gets tolerance of termination criterion
getEpsilon() - Method in class weka.classifiers.functions.SMO: Get the value of epsilon.
getEpsilon() - Method in class weka.classifiers.functions.supportVector.RegSMO: Get the value of epsilon.
getEpsilon() - Method in class weka.classifiers.mi.MISMO: Get the value of epsilon.
getEpsilon() - Method in class weka.clusterers.DBSCAN: Returns the value of epsilon
getEpsilon() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the value of epsilon
getEpsilon() - Method in class weka.clusterers.OPTICS: Returns the value of epsilon
getEpsilonParameter() - Method in class weka.attributeSelection.SVMAttributeEval: Get the value of P used with SMO
getEpsilonParameter() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Get the value of epsilon parameter of the epsilon insensitive loss function.
getError() - Method in class weka.classifiers.BVDecompose: Get the calculated error rate
getError() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated error rate
getErrorOnProbabilities() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of errorOnProbabilities.
getErrorOnProbabilities() - Method in class weka.classifiers.trees.FT: Get the value of errorOnProbabilities.
getErrorOnProbabilities() - Method in class weka.classifiers.trees.LMT: Get the value of errorOnProbabilities.
getErrors() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Return the errors made by the naive bayes model at this node
getErrors() - Method in class weka.classifiers.trees.j48.NBTreeSplit: Return the errors made by the naive bayes models arising from this split.
getEstimatedErrorsForLeaf() - Method in class weka.classifiers.rules.part.C45PruneableDecList: Computes estimated errors for leaf.
getEstimator() - Method in class weka.classifiers.bayes.BayesNet: Get the BayesNetEstimator used for calculating the CPTs
getEstimator() - Method in class weka.classifiers.functions.PaceRegression: Gets the estimator
getEstimator() - Method in class weka.estimators.CheckEstimator: Get the estimator used as the estimator
getEstimator(double) - Method in interface weka.estimators.ConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DDConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DKConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DNConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.KDConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.KKConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NDConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NNConditionalEstimator: Get a probability estimator for a value
getEvaluation() - Method in class weka.classifiers.meta.GridSearch: Gets the criterion used for evaluating the classifier performance.
getEvaluationMeasure() - Method in class weka.classifiers.rules.DecisionTable: Gets the currently set performance evaluation measure used for selecting attributes for the decision table
getEvaluationMode() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the evaluation mode used.
getEvaluator() - Method in class weka.attributeSelection.CheckAttributeSelection: Get the current evaluator
getEvaluator() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Get the evaluator used as the base evaluator.
getEvaluator() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the attribute evaluator used
getEvaluator() - Method in class weka.filters.supervised.attribute.AttributeSelection: Get the name of the attribute/subset evaluator
getEvalUsingTrainingData() - Method in class weka.attributeSelection.OneRAttributeEval: Returns true if the training data is to be used for evaluation
getEventName() - Method in class weka.gui.beans.BeanConnection: Returns the name of the event for this conncetion
getEvents() - Method in class weka.associations.gsp.Element: Returns the events Array of an Element.
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSinkBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSourceBeanInfo: Get the event set descriptors pertinent to data sources
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTestSetProducerBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainingSetProducerBeanInfo: Returns event set descriptors for this type of bean
getEventSetDescriptors() - Method in class weka.gui.beans.AssociatorBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.AttributeSummarizerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo: Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.ClassValuePickerBeanInfo: Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ClustererBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.ClustererPerformanceEvaluatorBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.CostBenefitAnalysisBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.DataVisualizerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.FilterBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.GraphViewerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.InstanceStreamToBatchMakerBeanInfo: Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ModelPerformanceChartBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.PredictionAppenderBeanInfo: Get the event set descriptors pertinent to data sources
getEventSetDescriptors() - Method in class weka.gui.beans.ScatterPlotMatrixBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.SerializedModelSaverBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.TextViewerBeanInfo: Get the event set descriptors for this bean
getEvidence(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: get evidence state of a node.
getException() - Method in class weka.gui.sql.event.ConnectionEvent: returns the stored exception, if any (can be NULL)
getException() - Method in class weka.gui.sql.event.QueryExecuteEvent: returns the exception, if one happened, otherwise NULL
getExclusive() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns whether exclusive expressions for nominal attributes splits are considered
getExecutionSlots() - Method in class weka.gui.beans.Classifier: Get the number of execution slots (threads) used to train models.
getExecutionStatus() - Method in class weka.experiment.TaskStatusInfo: Get the execution status of this Task.
getExitIfNoWindowsOpen() - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Gets whether System.exit gets called after the last window gets closed
getExitOnClose() - Method in class weka.gui.arffviewer.ArffViewer: returns TRUE if a System.exit(0) is done on a close
getExitOnClose() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns TRUE if a System.exit(0) is done on a close
getExpectedFrequency() - Method in class weka.associations.tertius.Rule: Get the expected frequency of counter-instances of this rule.
getExpectedNumber() - Method in class weka.associations.tertius.Rule
getExpectedResultsPerAverage() - Method in class weka.experiment.AveragingResultProducer: Get the value of ExpectedResultsPerAverage.
getExperiment() - Method in class weka.experiment.RemoteExperimentSubTask: Get the experiment for this sub task
getExperiment() - Method in class weka.gui.experiment.SetupModePanel: Gets the currently configured experiment.
getExperiment() - Method in class weka.gui.experiment.SetupPanel: Gets the currently configured experiment.
getExperiment() - Method in class weka.gui.experiment.SimpleSetupPanel: Gets the currently configured experiment.
getExperimentType() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default experiment type
getExplicitPropsFile() - Method in class weka.gui.GenericPropertiesCreator: returns TRUE, if a file is loaded and not the Utils class used for locating the props file.
getExplorer() - Method in class weka.gui.explorer.AssociationsPanel: returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.AttributeSelectionPanel: returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.ClassifierPanel: returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.ClustererPanel: returns the parent Explorer frame
getExplorer() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel: returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.PreprocessPanel: returns the parent Explorer frame
getExplorer() - Method in class weka.gui.explorer.VisualizePanel: returns the parent Explorer frame
getExponent() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Gets the exponent value.
getExponent() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of exponent.
getExpression(Node, FieldMetaInfo.Optype, ArrayList<Attribute>, TransformationDictionary) - Static method in class weka.core.pmml.Expression: Static factory method that returns a subclass of Expression that encapsulates the type of expression contained in the Element supplied.
getExpression(String, Node, FieldMetaInfo.Optype, ArrayList<Attribute>, TransformationDictionary) - Static method in class weka.core.pmml.Expression: Static factory method that returns a subclass of Expression that encapsulates the type of expression supplied as an argument.
getExpression() - Method in class weka.datagenerators.classifiers.regression.Expression: Gets the mathematical expression for generating y out of x
getExpression() - Method in class weka.filters.unsupervised.attribute.AddExpression: Get the expression
getExpression() - Method in class weka.filters.unsupervised.attribute.MathExpression: Get the expression
getExpression() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Returns the expression used for filtering.
getExtension() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default experiment extension
getExtension() - Method in class weka.gui.visualize.BMPWriter: returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.JComponentWriter: returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.JPEGWriter: returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.PNGWriter: returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.PostscriptWriter: returns the extension (incl.
getExtensions() - Method in class weka.gui.ExtensionFileFilter: Returns a copy of the acceptable extensions.
getExtremeValuesAsOutliers() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Get whether extreme values are also tagged as outliers.
getExtremeValuesFactor() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Gets the factor for determining the thresholds for extreme values.
getFactory() - Method in class weka.core.xml.XMLDocument: returns the DocumentBuilderFactory.
getFailReason() - Method in class weka.core.Capabilities: returns the reason why the tests failed, is null if tests succeeded
getFallout() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the fallout.
getFalseNegative() - Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of positive instances predicted as negative
getFalsePositive() - Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of negative instances predicted as positive
getFalsePositiveRate() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the false positive rate.
getFastRegression() - Method in class weka.classifiers.trees.LMT: Get the value of fastRegression.
getFieldAsAttribute() - Method in class weka.core.pmml.DerivedFieldMetaInfo: Get this derived field as an Attribute.
getFieldAsAttribute() - Method in class weka.core.pmml.FieldMetaInfo: Return this field as an Attribute.
getFieldAsAttribute() - Method in class weka.core.pmml.MiningFieldMetaInfo: Return this mining field as an Attribute.
getFieldAsAttribute() - Method in class weka.core.pmml.TargetMetaInfo: Return this field as an Attribute.
getFieldDef(String) - Method in class weka.core.pmml.Expression: Return the named attribute from the list of reference fields.
getFieldDefIndex(String) - Method in class weka.core.pmml.Expression
getFieldName() - Method in class weka.core.pmml.FieldMetaInfo: Get the name of this field.
getFieldsAsInstances() - Method in class weka.core.pmml.MiningSchema: Get the all the fields (both mining schema and derived) as Instances.
getFieldsMappingString() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Get a textual description of the mapping between mining schema fields and incoming data fields.
getFieldsMappingString() - Method in class weka.core.pmml.MappingInfo: Get a textual description of them mapping between mining schema fields and incoming data fields.
getFile() - Method in class weka.gui.visualize.JComponentWriter: returns the file being used for storing the output
getFileDescription() - Method in class weka.core.converters.AbstractFileSaver: to be pverridden
getFileDescription() - Method in class weka.core.converters.ArffLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.ArffSaver: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.C45Loader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.C45Saver: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.CSVLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.CSVSaver: Returns a description of the file type.
getFileDescription() - Method in interface weka.core.converters.FileSourcedConverter: Get a one line description of the type of file
getFileDescription() - Method in class weka.core.converters.LibSVMLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.LibSVMSaver: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SerializedInstancesLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SerializedInstancesSaver: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SVMLightLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SVMLightSaver: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.TextDirectoryLoader: Returns a description of the file type, actually it's directories.
getFileDescription() - Method in class weka.core.converters.XRFFLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.XRFFSaver: Returns a description of the file type.
getFileExtension() - Method in class weka.core.converters.AbstractFileSaver: Gets ihe file extension.
getFileExtension() - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
getFileExtension() - Method in class weka.core.converters.ArffLoader: Get the file extension used for arff files
getFileExtension() - Method in class weka.core.converters.C45Loader: Get the file extension used for arff files
getFileExtension() - Method in class weka.core.converters.CSVLoader: Get the file extension used for arff files.
getFileExtension() - Method in interface weka.core.converters.FileSourcedConverter: Get the file extension used for this type of file
getFileExtension() - Method in class weka.core.converters.LibSVMLoader: Get the file extension used for libsvm files.
getFileExtension() - Method in interface weka.core.converters.Saver: Gets the file extension
getFileExtension() - Method in class weka.core.converters.SerializedInstancesLoader: Get the file extension used for arff files
getFileExtension() - Method in class weka.core.converters.SVMLightLoader: Get the file extension used for svm light files.
getFileExtension() - Method in class weka.core.converters.XRFFLoader: Get the file extension used for libsvm files
getFileExtensions() - Method in class weka.core.converters.AbstractFileSaver: Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.ArffLoader: Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.ArffSaver: Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.C45Loader: Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.CSVLoader: Gets all the file extensions used for this type of file.
getFileExtensions() - Method in interface weka.core.converters.FileSourcedConverter: Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.LibSVMLoader: Gets all the file extensions used for this type of file.
getFileExtensions() - Method in class weka.core.converters.SerializedInstancesLoader: Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.SVMLightLoader: Gets all the file extensions used for this type of file.
getFileExtensions() - Method in class weka.core.converters.XRFFLoader: Gets all the file extensions used for this type of file
getFileExtensions() - Method in class weka.core.converters.XRFFSaver: Gets all the file extensions used for this type of file
getFileFormat() - Method in class weka.gui.beans.SerializedModelSaver: Get the file format to use for saving.
getFileLoaders() - Static method in class weka.core.converters.ConverterUtils: returns a vector with the classnames of all the file loaders.
getFileMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: returns all the file/dir matches with the partial search string.
getFileMustExist() - Method in class weka.gui.ConverterFileChooser: Returns whether the selected file must exist (only open dialog).
getFileName() - Method in class weka.classifiers.bayes.net.BIFReader: returns the current filename
getFilename() - Method in class weka.core.Debug.Log: returns the filename of the log, can be null
getFilename() - Method in class weka.core.Debug.SimpleLog: returns the filename of the log, can be null
getFilename() - Method in class weka.core.FindWithCapabilities: returns the current filename for the dataset to base the capabilities on.
getFilename() - Method in class weka.gui.arffviewer.ArffPanel: returns the filename
getFilename(int) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the filename of the specified panel
getFileSavers() - Static method in class weka.core.converters.ConverterUtils: returns a vector with the classnames of all the file savers.
getFillWithMissing() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Gets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
getFilter() - Method in class weka.associations.FilteredAssociator: Gets the filter used.
getFilter() - Method in class weka.attributeSelection.FilteredAttributeEval: Get the filter to use
getFilter() - Method in class weka.attributeSelection.FilteredSubsetEval: Get the filter to use
getFilter() - Method in class weka.classifiers.functions.PLSClassifier: Get the PLS filter.
getFilter() - Method in class weka.classifiers.meta.FilteredClassifier: Gets the filter used.
getFilter() - Method in class weka.classifiers.meta.GridSearch: Get the kernel filter.
getFilter() - Method in class weka.clusterers.FilteredClusterer: Gets the filter used.
getFilter() - Method in class weka.filters.CheckSource: Gets the filter being used for the tests, can be null.
getFilter(int) - Method in class weka.filters.MultiFilter: Gets a single filter from the set of available filters.
getFilter(int) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Gets a single filter from the set of available filters.
getFilter() - Method in class weka.filters.unsupervised.attribute.Wavelet: Get the preprocessing filter.
getFilter() - Method in class weka.gui.beans.Filter
getFilter() - Method in class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent: returns the associated Capabilities filter
getFilter() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the default filter (fully configured) for the preprocess panel.
getFilterAfterFirstBatch() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Get whether to apply the filter to instances that arrive once the first (training) batch has been seen.
getFilterAttributes() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the String containing the attributes which are used for output filtering.
getFiltered(int) - Method in class weka.classifiers.rules.RuleStats: Get the data after filtering the given rule
getFilters() - Method in class weka.filters.MultiFilter: Gets the list of possible filters to choose from.
getFilters() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Gets the list of possible filters to choose from.
getFilterType() - Method in class weka.attributeSelection.SVMAttributeEval: Get the filtering mode passed to SMO
getFilterType() - Method in class weka.classifiers.functions.GaussianProcesses: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.functions.SMO: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.functions.SMOreg: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MDD: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MIDD: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MIEMDD: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MIOptimalBall: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MISMO: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.mi.MISVM: Gets how the training data will be transformed.
getFindAllRulesForSupportLevel() - Method in class weka.associations.FPGrowth: Get whether all rules meeting the lower bound on min support and the minimum metric threshold are to be found.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.Discretize: Get the value of FindNumBins.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Get the value of FindNumBins.
getFirst() - Method in class weka.associations.tertius.SimpleLinkedList
getFirstToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils: Gets token, skipping empty lines.
getFirstValueIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Get the index of the first value used.
getFirstValueIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the first value used.
getFlag(char, String[]) - Static method in class weka.core.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class weka.core.Utils: Checks if the given array contains the flag "-String".
getFlow() - Method in class weka.gui.beans.KnowledgeFlowApp: Gets the current flow being edited.
getFlows() - Method in class weka.gui.beans.FlowRunner: Get the vector holding the flow(s)
getFMeasure() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the F-Measure.
getFold() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the fold which is selected.
getFold() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the fold which is selected.
getFoldColumn() - Method in class weka.experiment.PairedTTester: Get the value of FoldColumn.
getFoldColumn() - Method in interface weka.experiment.Tester: Get the value of FoldColumn.
getFolds() - Method in class weka.attributeSelection.OneRAttributeEval: Get the number of folds used for cross validation
getFolds() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the number of folds used for accuracy estimation
getFolds() - Method in class weka.classifiers.rules.ConjunctiveRule: returns the current number of folds
getFolds() - Method in class weka.classifiers.rules.JRip: Gets the number of folds
getFolds() - Method in class weka.classifiers.rules.Ridor
getFolds() - Method in class weka.gui.beans.CrossValidationFoldMaker: Get the currently set number of folds
getFolds() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the number of folds used for cross-validation
getFoldsType() - Method in class weka.attributeSelection.RaceSearch: Get the xfold type
getFont() - Method in class weka.gui.visualize.PostscriptGraphics: Get current font.
getFontMetrics(Font) - Method in class weka.gui.visualize.PostscriptGraphics: Get Font metrics
getFontRenderContext() - Method in class weka.gui.visualize.PostscriptGraphics: START overridden Graphics2D methods
getFormat() - Method in class weka.core.Debug.Timestamp: returns the current timestamp format
getForwardSelectionMethod() - Method in class weka.attributeSelection.LinearForwardSelection: Get the search direction
getFPRate() - Method in class weka.associations.tertius.Rule: Get the rate of False Positive instances of this rule.
getFrameLocation() - Method in class weka.gui.MemoryUsagePanel: Returns the default position for the dialog.
getFrameTitle() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the title (incl.
getFrequency() - Method in class weka.associations.FPGrowth.BinaryItem: Get the frequency of this item.
getFrequencyLimit() - Method in class weka.classifiers.bayes.AODE: Gets the frequency limit.
getFrequencyLimit() - Method in class weka.classifiers.bayes.AODEsr: Gets the frequency limit.
getFrequencyThreshold() - Method in class weka.associations.Tertius: Get the value of frequencyThreshold.
getFreshCardinalityOfParents(Instances) - Method in class weka.classifiers.bayes.net.ParentSet: returns cardinality of parents after recalculation
getFromYear() - Static method in class weka.core.Copyright: returns the start year of the copyright
getFunction(String) - Static method in class weka.core.pmml.Function: Get a built-in PMML Function.
getFunction(String, TransformationDictionary) - Static method in class weka.core.pmml.Function: Get either a function.
getFunction() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Gets the function for generating the data.
getFunctionValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Gets a particular function value
getGamma() - Method in class weka.classifiers.functions.LibSVM: Gets gamma
getGamma() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Gets the gamma value.
getGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the number of visible groups of siblings there are.
getGenerateRanking() - Method in class weka.attributeSelection.GreedyStepwise: Gets whether ranking has been requested.
getGenerateRanking() - Method in class weka.attributeSelection.RaceSearch: Gets whether ranking has been requested.
getGenerateRanking() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets whether the user has opted to see a ranked list of attributes rather than the normal result of the search
getGenerateRanking() - Method in class weka.attributeSelection.Ranker: This is a dummy method.
getGenerator() - Method in class weka.gui.explorer.DataGeneratorPanel: returns the currently selected DataGenerator
getGeneratorSamplesBase() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the base used for computing the number of samples to obtain from each generator
getGlobalBlend() - Method in class weka.classifiers.lazy.KStar: Get the value of the global blend parameter
getGlobalInfo(Object, boolean) - Static method in class weka.core.Utils: Utility method for grabbing the global info help (if it exists) from an arbitrary object.
getGlobalInfo(Object) - Static method in class weka.gui.beans.KnowledgeFlowApp: Utility method for grabbing the global info help (if it exists) from an arbitrary object
getGlobalModel() - Method in class weka.classifiers.trees.j48.NBTreeSplit: Return the global naive bayes model for this node
getGraphString() - Method in class weka.gui.beans.GraphEvent: Return the dot string for the graph
getGraphTitle() - Method in class weka.gui.beans.GraphEvent: Return the graph title
getGraphType() - Method in class weka.gui.beans.GraphEvent: Return the graph type
getGridExtensionsPerformed() - Method in class weka.classifiers.meta.GridSearch: returns the number of grid extensions that took place during the search (only applicable if the grid was extendable).
getGridIsExtendable() - Method in class weka.classifiers.meta.GridSearch: Get whether the grid can be extended dynamically.
getGridWidth() - Method in class weka.gui.beans.AttributeSummarizer: Get the width of the grid of plots
getGroupIdentifier() - Method in class weka.gui.beans.BatchClassifierEvent
getGUI() - Method in class weka.classifiers.functions.MultilayerPerceptron
getGUIType() - Method in class weka.gui.Main: Gets the currently set type of GUI to display.
getH() - Method in class weka.core.matrix.QRDecomposition: Return the Householder vectors
getHandler() - Method in class weka.core.FindWithCapabilities: returns the current set CapabilitiesHandler to generate the dataset for, can be null.
getHandler() - Method in class weka.core.TestInstances: returns the current set CapabilitiesHandler to generate the dataset for, can be null
getHashtable(FastVector, int) - Static method in class weka.associations.ItemSet: Return a hashtable filled with the given item sets.
getHashtable(FastVector, int) - Static method in class weka.associations.LabeledItemSet: Return a hashtable filled with the given item sets.
getHDRank() - Method in class weka.classifiers.mi.CitationKNN: Returns the rank associated to the Hausdorff distance
getHeader(String) - Method in class weka.experiment.ResultMatrix: returns the value associated with the given key, null if if cannot be found
getHeight() - Method in class weka.gui.beans.BeanInstance: Gets the height of this bean
getHeight(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the number of visible levels there are.
getHeuristic() - Method in class weka.classifiers.trees.BFTree: Get if use heuristic search for nominal attributes in multi-class problems.
getHeuristic() - Method in class weka.classifiers.trees.SimpleCart: Get if use heuristic search for nominal attributes in multi-class problems.
getHeuristicStop() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of heuristicStop.
getHiddenLayers() - Method in class weka.classifiers.functions.MultilayerPerceptron
getHistory() - Method in class weka.gui.sql.ConnectionPanel: returns the history.
getHistory() - Method in class weka.gui.sql.event.HistoryChangedEvent: returns the history model
getHistory() - Method in class weka.gui.sql.QueryPanel: returns the history.
getHistoryName() - Method in class weka.gui.sql.event.HistoryChangedEvent: returns the name of the history
getHoldOutFile() - Method in class weka.attributeSelection.ClassifierSubsetEval: Gets the file that holds hold out/test instances.
getHomeDir() - Static method in class weka.core.Debug: returns the home directory of the user
getHornClauses() - Method in class weka.associations.Tertius: Get the value of hornClauses.
getHyperparameterRange() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Get the range of hyperparameter values to consider during CV-based selection.
getHyperparameterSelection() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Get the method used to select the hyperparameter
getHyperparameterValue() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Get the hyperparameter value.
getIconPath() - Method in class weka.gui.beans.BeanVisual: returns the path for the icon
getId() - Method in class weka.classifiers.functions.neural.NeuralConnection
getID() - Method in class weka.core.Debug.Random: returns the unique ID of this number generator
getID() - Method in class weka.core.Tag: Gets the numeric ID of the Tag.
getID() - Method in class weka.core.TechnicalInformation: returns the unique ID (either the one used in creating this instance or the automatically generated one)
getID() - Method in class weka.gui.streams.InstanceEvent: Get the event type
getID() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
getIDFTransform() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
getIDIndex() - Method in class weka.filters.unsupervised.attribute.AddID: Get the index of the attribute used.
getIDStr() - Method in class weka.core.Tag: Gets the string ID of the Tag.
getIgnoreClass() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Gets the IgnoreClass value.
getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AddCluster: Gets ranges of attributes to be ignored.
getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Gets ranges of attributes to be ignored.
getIgnoredProperties() - Method in class weka.core.CheckGOE: Get the ignored properties used in checkToolTips() as comma-separated list (sorted).
getIgnoreRange() - Method in class weka.filters.unsupervised.attribute.MathExpression: Get the current range selection.
getImage(String, String) - Static method in class weka.gui.ComponentHelper: returns the Image for a given directory and filename, NULL if not successful
getImage(String) - Static method in class weka.gui.ComponentHelper: returns the Image for a given filename, NULL if not successful
getImageIcon(String, String) - Static method in class weka.gui.ComponentHelper: returns the ImageIcon for a given filename and directory, NULL if not successful
getImageIcon(String) - Static method in class weka.gui.ComponentHelper: returns the ImageIcon for a given filename, NULL if not successful
getImagEigenvalues() - Method in class weka.core.matrix.EigenvalueDecomposition: Return the imaginary parts of the eigenvalues
getIncludeClass() - Method in class weka.core.InstanceComparator: returns TRUE if the class is included in the comparison
getIncludeClass() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Gets whether the class is included in the cleaning process or always skipped.
getIndex() - Method in class weka.associations.tertius.Predicate
getIndex() - Method in class weka.core.PropertyPath.PathElement: returns the index of the property, -1 if the property is not an index-based one
getIndex() - Method in class weka.core.SingleIndex: Gets the selected index
getIndex() - Method in class weka.gui.SortedTableModel.SortContainer: Returns the original index of the item.
getIndexofBiggest(List<Integer>) - Method in class weka.attributeSelection.ScatterSearchV1: get the index in a List where this have the biggest number
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Gets whether to init as naive bayes
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.global.K2: Gets whether to init as naive bayes
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Gets whether to init as naive bayes
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.local.K2: Gets whether to init as naive bayes
getInitFile() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Gets the file to initialize the filter with, can be null.
getInitFileClassIndex() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Gets the class index of the file to initialize the filter with.
getInitGenericObjectEditorFilter() - Static method in class weka.gui.explorer.ExplorerDefaults: returns if the GOEs in the Explorer will be initialized based on the data that is loaded into the Explorer.
getInitial() - Method in class weka.core.Memory: returns the initial size of the JVM heap, obtains a fresh MemoryUsage object to do so.
getInitialDatasetsDirectory() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the initial directory for the datasets (if empty, it returns the user's home directory)
getInitialDirectory() - Static method in class weka.gui.explorer.ExplorerDefaults: Returns the initial directory for the file chooser used for opening datasets.
getInputCenterFile() - Method in class weka.clusterers.XMeans: Gets the file to read the list of centers from.
getInputFilename() - Method in class weka.gui.GenericPropertiesCreator: returns the name of the input file
getInputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the input numbers.
getInputOrder() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the input order.
getInputProperties() - Method in class weka.gui.GenericPropertiesCreator: returns the input properties object (template containing the packages)
getInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the inputs.
getInputs() - Method in class weka.gui.beans.MetaBean
getInputStream(String, String) - Static method in class weka.gui.Loader: returns an InputStream for the given dir and filename, can be NULL if it fails
getInputStream(String) - Method in class weka.gui.Loader: returns an InputStream for the given filename, can be NULL if it fails
getInstalledLookAndFeels() - Static method in class weka.gui.LookAndFeel: returns an array with the classnames of all the installed LnFs
getInstance() - Static method in class weka.associations.gsp.Messages: getInstance.
getInstance() - Static method in class weka.associations.Messages: getInstance.
getInstance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Returns the original instance
getInstance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Returns the original instance
getInstance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Returns the original instance
getInstance() - Static method in class weka.gui.arffviewer.Messages: getInstance.
getInstance() - Method in class weka.gui.beans.InstanceEvent: Get the instance
getInstance() - Static method in class weka.gui.beans.Messages: getInstance.
getInstance() - Static method in class weka.gui.beans.xml.Messages: getInstance.
getInstance() - Static method in class weka.gui.boundaryvisualizer.Messages: getInstance.
getInstance() - Static method in class weka.gui.experiment.Messages: getInstance.
getInstance() - Static method in class weka.gui.explorer.Messages: getInstance.
getInstance() - Static method in class weka.gui.graphvisualizer.Messages: getInstance.
getInstance() - Static method in class weka.gui.hierarchyvisualizer.Messages: getInstance.
getInstance() - Static method in class weka.gui.Messages: getInstance.
getInstance() - Static method in class weka.gui.sql.event.Messages: getInstance.
getInstance() - Static method in class weka.gui.sql.Messages: getInstance.
getInstance() - Static method in class weka.gui.streams.Messages: getInstance.
getInstance() - Static method in class weka.gui.treevisualizer.Messages: getInstance.
getInstance() - Static method in class weka.gui.visualize.Messages: getInstance.
getInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Instance Index array
getInstanceRange() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Gets the number of instances forward to translate values between.
getInstances() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Returns the original instances delivered from WEKA
getInstances() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Returns the original instances delivered from WEKA
getInstances() - Method in class weka.core.converters.AbstractSaver: Gets instances that should be stored.
getInstances() - Method in interface weka.core.DistanceFunction: returns the instances currently set.
getInstances() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: returns the instances currently set.
getInstances() - Method in class weka.core.NormalizableDistance: returns the instances currently set.
getInstances() - Method in class weka.core.xml.XMLInstances: returns the current instances, either the ones that were set or the ones that were generated from the XML structure.
getInstances() - Method in class weka.experiment.PairedTTester: Get the value of Instances.
getInstances() - Method in interface weka.experiment.Tester: Get the value of Instances.
getInstances() - Method in class weka.gui.arffviewer.ArffPanel: returns the instances of the panel, if none then NULL
getInstances() - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns the data
getInstances() - Method in class weka.gui.arffviewer.ArffTableModel: returns the data
getInstances() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Get the training instances
getInstances() - Method in class weka.gui.explorer.DataGeneratorPanel: returns the generated instances, null if the process was cancelled.
getInstances() - Method in class weka.gui.explorer.PreprocessPanel: Gets the working set of instances.
getInstances() - Method in class weka.gui.SetInstancesPanel: Gets the set of instances currently held by the panel
getInstances() - Method in class weka.gui.treevisualizer.Node: This will return the Instances object related to this node.
getInstances() - Method in class weka.gui.ViewerDialog: returns the currently displayed instances
getInstances() - Method in class weka.gui.visualize.VisualizePanel: Get the master plot's instances
getInstances1() - Method in class weka.gui.visualize.VisualizePanelEvent
getInstances2() - Method in class weka.gui.visualize.VisualizePanelEvent
getInstancesFromClass(Instances, int, int, double, Instances) - Static method in class weka.estimators.EstimatorUtils: Returns a dataset that contains all instances of a certain class value.
getInstancesFromClass(Instances, int, double) - Static method in class weka.estimators.EstimatorUtils: Returns a dataset that contains of all instances of a certain class value.
getInstancesFromValue(Instances, int, double) - Static method in class weka.estimators.EstimatorUtils: Returns a dataset that contains of all instances of a certain value for the given attribute.
getInstancesIndices() - Method in class weka.filters.unsupervised.instance.RemoveRange: Gets ranges of instances selected.
getInstancesNoClass() - Method in class weka.associations.Apriori: Gets the instances without the class atrribute.
getInstancesNoClass() - Method in interface weka.associations.CARuleMiner: Gets the instances without the class attribute
getInstancesNoClass() - Method in class weka.associations.PredictiveApriori: Gets the instances without the class attribute
getInstancesOnlyClass() - Method in class weka.associations.Apriori: Gets only the class attribute of the instances.
getInstancesOnlyClass() - Method in interface weka.associations.CARuleMiner: Gets the class attribute and its values for all instances
getInstancesOnlyClass() - Method in class weka.associations.PredictiveApriori: Gets the class attribute of all instances
getInstancesValueAt(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns the double value of the underlying Instances object at the given position, -1 if out of bounds
getInstancesValueAt(int, int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the double value of the underlying Instances object at the given position, -1 if out of bounds
getIntercept() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns the intercept of the function.
getInternalCacheSize() - Method in class weka.classifiers.functions.supportVector.StringKernel: Gets the size of the internal cache
getInternals() - Method in class weka.classifiers.bayes.WAODE: Gets whether more internals of the classifier are printed.
getInterpreter() - Method in class weka.core.Jython: returns the currently used Python Interpreter
getInvert() - Method in class weka.core.Range: Gets whether the range sense is inverted, i.e.
getInvert() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Get whether selection is inverted.
getInvertSelection() - Method in interface weka.core.DistanceFunction: Gets whether the matching sense of attribute indices is inverted or not.
getInvertSelection() - Method in class weka.core.NormalizableDistance: Gets whether the matching sense of attribute indices is inverted or not.
getInvertSelection() - Method in class weka.filters.supervised.attribute.Discretize: Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.supervised.instance.Resample: Gets whether selection is inverted (only if instances are drawn WIHTOUT replacement).
getInvertSelection() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Copy: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.MathExpression: Get whether the supplied columns are to be select or unselect
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Gets whether the selection of the columns is inverted
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Gets whether the supplied columns are to be worked on or the others.
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get whether the supplied columns are to be transformed or not
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Gets whether the supplied columns are to be processed or skipped
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Remove: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.RemoveType: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether the supplied columns are to be processed or skipped.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveRange: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.instance.Resample: Gets whether selection is inverted (only if instances are drawn WIHTOUT replacement).
getJavaInitializationString() - Method in class weka.gui.CostMatrixEditor: Returns the Java code that generates an object the same as the one being edited.
getJavaInitializationString() - Method in class weka.gui.FileEditor: Returns a representation of the current property value as java source.
getJavaInitializationString() - Method in class weka.gui.GenericArrayEditor: Supposedly returns an initialization string to create a classifier identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString() - Method in class weka.gui.GenericObjectEditor: Supposedly returns an initialization string to create a Object identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString() - Method in class weka.gui.SelectedTagEditor: Returns a description of the property value as java source.
getJavaInitializationString() - Method in class weka.gui.SimpleDateFormatEditor: Returns the Java code that generates an object the same as the one being edited.
getJTable() - Method in class weka.gui.JTableHelper: returns the JTable
getKDTree() - Method in class weka.clusterers.XMeans: Gets the KDTree class.
getKernel() - Method in class weka.classifiers.functions.GaussianProcesses: Gets the kernel to use.
getKernel() - Method in class weka.classifiers.functions.SMO.BinarySMO: Returns the kernel to use
getKernel() - Method in class weka.classifiers.functions.SMO: Returns the kernel to use
getKernel() - Method in class weka.classifiers.functions.SMOreg: Returns the kernel to use
getKernel() - Method in class weka.classifiers.functions.supportVector.CheckKernel: Get the kernel being tested
getKernel() - Method in class weka.classifiers.mi.MISMO: Gets the kernel to use.
getKernel() - Method in class weka.classifiers.mi.MISVM: Gets the kernel to use.
getKernel() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Gets the kernel to use.
getKernelBandwidth() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Get the kernel bandwidth
getKernelEvaluations() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: returns the number of kernel evaluations
getKernelFactorExpression() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Gets the expression for the kernel.
getKernelMatrixFile() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Gets the file containing the kernel matrix.
getKernelType() - Method in class weka.classifiers.functions.LibSVM: Gets type of kernel function
getKey() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Returns the key for this DataObject
getKey() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Returns the key for this DataObject
getKey() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Returns the key for this DataObject
getKey() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the key describing the current SplitEvaluator.
getKey() - Method in interface weka.experiment.SplitEvaluator: Gets the key describing the current SplitEvaluator.
getKeyFieldName() - Method in class weka.experiment.AveragingResultProducer: Get the value of KeyFieldName.
getKeyNames() - Method in class weka.experiment.AveragingResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.CrossValidationResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DatabaseResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.LearningRateResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.RandomSplitResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in interface weka.experiment.ResultProducer: Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in interface weka.experiment.SplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeys() - Method in class weka.core.converters.DatabaseLoader: Gets the key columns' name
getKeyTypes() - Method in class weka.experiment.AveragingResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.CrossValidationResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DatabaseResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.LearningRateResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.RandomSplitResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in interface weka.experiment.ResultProducer: Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in interface weka.experiment.SplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKeywords() - Method in class weka.experiment.DatabaseUtils: Returns the currently stored keywords (as comma-separated list).
getKeywordsMaskChar() - Method in class weka.experiment.DatabaseUtils: Returns the currently set mask character.
getKNN() - Method in class weka.classifiers.lazy.IBk: Gets the number of neighbours the learner will use.
getKNN() - Method in class weka.classifiers.lazy.LWL: Gets the number of neighbours used for kernel bandwidth setting.
getKValue() - Method in class weka.classifiers.trees.RandomTree: Get the value of K.
getKWBias() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated bias squared according to the Kohavi and Wolpert definition
getKWSigma() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated sigma according to the Kohavi and Wolpert definition
getKWVariance() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated variance according to the Kohavi and Wolpert definition
getL() - Method in class weka.core.matrix.CholeskyDecomposition: Return triangular factor.
getL() - Method in class weka.core.Matrix: Deprecated.

Returns the L part of the matrix.
getL() - Method in class weka.core.matrix.LUDecomposition: Return lower triangular factor
getLabel() - Method in class weka.gui.treevisualizer.Edge: Get the value of label.
getLabel() - Method in class weka.gui.treevisualizer.Node: Get the value of label.
getLabels() - Method in class weka.filters.unsupervised.attribute.AddValues: Get the comma-separated list of labels that are added.
getLambda() - Method in class weka.classifiers.functions.SPegasos: Get the current value of lambda
getLambda() - Method in class weka.classifiers.functions.supportVector.StringKernel: Gets the lambda constant used in the string kernel
getLast() - Method in class weka.associations.tertius.SimpleLinkedList
getLastLiteral() - Method in class weka.associations.tertius.LiteralSet: Give the last literal added to this set.
getLeaf() - Method in class weka.classifiers.trees.j48.GraftSplit
getLearningRate() - Method in class weka.classifiers.functions.MultilayerPerceptron
getLegendText() - Method in class weka.gui.beans.ChartEvent: Get the legend text vector
getLevel() - Method in class weka.gui.HierarchyPropertyParser: Get the level of current node.
getLikelihoodThreshold() - Method in class weka.classifiers.meta.LogitBoost: Get the value of Precision.
getLine(int) - Method in class weka.gui.treevisualizer.Edge: Returns line number n
getLine(int) - Method in class weka.gui.treevisualizer.Node: Returns the text String for the specfied line.
getLineNo() - Method in class weka.core.converters.ArffLoader.ArffReader: returns the current line number
getLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2: returns the element (Link) at a specific index from the list.
getLinkAt(int) - Method in class weka.attributeSelection.LFSMethods.LinkedList2: returns the element (Link) at a specific index from the list.
getLinkType() - Method in class weka.clusterers.HierarchicalClusterer
getList() - Method in class weka.gui.ResultHistoryPanel: Gets the JList used by the results list
getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.CheckBoxList.CheckBoxListRenderer: Return a component that has been configured to display the specified value.
getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer: Return a component that has been configured to display the specified value.
getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.sql.InfoPanelCellRenderer: Return a component that has been configured to display the specified value.
getLiteral(int) - Method in class weka.associations.tertius.Predicate
getLNorm() - Method in class weka.filters.unsupervised.instance.Normalize: Get the L Norm used.
getLoader() - Method in class weka.core.converters.ConverterUtils.DataSource: returns the determined loader, null if the DataSource was initialized with data alone and not a file/URL.
getLoader() - Method in class weka.gui.beans.Loader: Get the loader
getLoader() - Method in class weka.gui.ConverterFileChooser: returns the loader that was chosen by the user, can be null in case the user aborted the dialog or the save dialog was shown
getLoader() - Method in class weka.gui.SetInstancesPanel: Gets the currently used Loader
getLoaderForExtension(String) - Static method in class weka.core.converters.ConverterUtils: tries to determine the loader to use for this kind of extension, returns null if none can be found.
getLoaderForFile(String) - Static method in class weka.core.converters.ConverterUtils: tries to determine the loader to use for this kind of file, returns null if none can be found.
getLoaderForFile(File) - Static method in class weka.core.converters.ConverterUtils: tries to determine the loader to use for this kind of file, returns null if none can be found.
getLocallyPredictive() - Method in class weka.attributeSelection.CfsSubsetEval: Return true if including locally predictive attributes
getLocator(int) - Method in class weka.core.AttributeLocator: Returns the AttributeLocator at the given index.
getLocatorIndices() - Method in class weka.core.AttributeLocator: Returns the indices of the AttributeLocator objects.
getLog() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Get the logger.
getLog() - Method in class weka.core.Debug.Random: the currently used log, if null then stdout is used for outputting the debugging information
getLog() - Method in interface weka.core.pmml.PMMLModel: Get the logger.
getLogFile() - Method in class weka.classifiers.meta.GridSearch: Gets current log file.
getLoglikeliHood(double[], Instances) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
getLoglikelihood() - Method in class weka.classifiers.bayes.blr.Prior
getLogLikelihood() - Method in class weka.clusterers.ClusterEvaluation: Return the log likelihood corresponding to the most recent set of instances clustered.
getLogPosterior() - Method in class weka.classifiers.bayes.blr.Prior
getLogProbForTargetClass(Instance) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary: Calculates the class membership probabilities for the given test instance.
getLookupCacheSize() - Method in class weka.attributeSelection.BestFirst: Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.
getLookupCacheSize() - Method in class weka.attributeSelection.LinearForwardSelection: Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.
getLookupCacheSize() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.
getLoss() - Method in class weka.classifiers.functions.LibSVM: Gets the epsilon in loss function of epsilon-SVR
getLossFunction() - Method in class weka.classifiers.functions.SPegasos: Get the current loss function.
getLower() - Method in class weka.gui.experiment.RunNumberPanel: Gets the current lower run number.
getLowerBoundMinSupport() - Method in class weka.associations.Apriori: Get the value of lowerBoundMinSupport.
getLowerBoundMinSupport() - Method in class weka.associations.FPGrowth: Get the value of lowerBoundMinSupport.
getLowerCaseTokens() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the tokens are to be downcased or not.
getLowerNumericBound() - Method in class weka.core.Attribute: Returns the lower bound of a numeric attribute.
getLowerSize() - Method in class weka.experiment.LearningRateResultProducer: Get the value of LowerSize.
getM5RootNode() - Method in class weka.classifiers.trees.m5.M5Base
getM5RootNode() - Method in class weka.classifiers.trees.m5.Rule
getMainPanel() - Method in class weka.gui.arffviewer.ArffViewer: returns the main panel
getMajorityClass() - Method in class weka.classifiers.rules.Ridor
getMakeBinary() - Method in class weka.filters.supervised.attribute.Discretize: Gets whether binary attributes should be made for discretized ones.
getMakeBinary() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets whether binary attributes should be made for discretized ones.
getManualThresholdValue() - Method in class weka.classifiers.meta.ThresholdSelector: Returns the value of the manual threshold.
getMargin(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: return marginal distibution for a node
getMargin(int) - Method in class weka.classifiers.bayes.net.MarginCalculator
getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
getMasterPlot() - Method in class weka.gui.visualize.Plot2D: Get the master plot
getMatches() - Method in class weka.core.FindWithCapabilities: returns the matches from the last find call.
getMatches(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: returns all the matches with the partial search string, files or classes.
getMatchMissingValues() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Gets whether missing values are counted as a match.
getMatrix(int, int, int, int) - Method in class weka.core.matrix.Matrix: Get a submatrix.
getMatrix(int[], int[]) - Method in class weka.core.matrix.Matrix: Get a submatrix.
getMatrix(int, int, int[]) - Method in class weka.core.matrix.Matrix: Get a submatrix.
getMatrix(int[], int, int) - Method in class weka.core.matrix.Matrix: Get a submatrix.
getMax() - Method in class weka.core.Memory: returns the maximum size of the JVM heap, obtains a fresh MemoryUsage object to do so.
getMax() - Method in class weka.gui.beans.ChartEvent: Get the max y value
getMaxArray() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns the calculated maximum values for the attributes in the data.
getMaxBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of maxBoostingIterations.
getMaxC() - Method in class weka.gui.visualize.Plot2D: Return the current max value of the colouring attribute
getMaxCardinality() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: returns the max cardinality
getMaxCardinality() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Gets the maximum number of values allowed for nominal attributes, before they're skipped.
getMaxChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the maximum chunk size
getMaxCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the maximum of coords per point.
getMaxCost(int) - Method in class weka.classifiers.CostMatrix: Gets the maximum cost for a particular class value.
getMaxCost(int, Instance) - Method in class weka.classifiers.CostMatrix: Gets the maximum cost for a particular class value.
getMaxCount() - Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the value for the max count
getMaxDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Get the maximum default.
getMaxDepth() - Method in class weka.classifiers.trees.RandomForest: Get the maximum depth of trh tree, 0 for unlimited.
getMaxDepth() - Method in class weka.classifiers.trees.RandomTree: Get the maximum depth of trh tree, 0 for unlimited.
getMaxDepth() - Method in class weka.classifiers.trees.REPTree: Get the value of MaxDepth.
getMaxDepth() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the depth of the built tree.
getMaxGenerations() - Method in class weka.attributeSelection.GeneticSearch: get the number of generations
getMaxGridExtensions() - Method in class weka.classifiers.meta.GridSearch: Gets the maximum number of grid extensions, -1 for unlimited.
getMaxGroup() - Method in class weka.classifiers.meta.RotationForest: Gets the maximum size of a group.
getMaximumAttributeNames() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Gets maximum number of attributes to include in transformed attribute names.
getMaximumAttributeNames() - Method in class weka.attributeSelection.PrincipalComponents: Gets maximum number of attributes to include in transformed attribute names.
getMaximumAttributeNames() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Gets maximum number of attributes to include in transformed attribute names.
getMaximumAttributes() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Gets maximum number of PC attributes to retain.
getMaximumVariancePercentageAllowed() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Gets the maximum variance attributes are allowed to have before they are deleted by the filter.
getMaxInfoGain() - Method in class weka.classifiers.rules.JRip.Antd
getMaxInstancesInLeaf() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the maximum number of instances allowed in a leaf.
getMaxInstInLeaf() - Method in class weka.core.neighboursearch.KDTree: Get the maximum number of instances in a leaf.
getMaxInstNum() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the upper boundary for instances per cluster.
getMaxInstNum() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Gets the upper boundary for instances per cluster.
getMaxIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: returns the maximum of internal nodes visited.
getMaxIterations() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Get the maximum number of iterations to perform
getMaxIterations() - Method in class weka.classifiers.mi.MIBoost: Get the maximum number of boost iterations
getMaxIterations() - Method in class weka.classifiers.mi.MISVM: Gets the maximum number of iterations.
getMaxIterations() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns the maxIterations parameter.
getMaxIterations() - Method in class weka.clusterers.EM: Get the maximum number of iterations
getMaxIterations() - Method in class weka.clusterers.sIB: Get the max number of iterations
getMaxIterations() - Method in class weka.clusterers.SimpleKMeans: gets the number of maximum iterations to be executed
getMaxIterations() - Method in class weka.clusterers.XMeans: Gets the maximum number of iterations.
getMaxIterations() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the maximum number of cleansing iterations performed
getMaxIts() - Method in class weka.classifiers.functions.Logistic: Get the value of MaxIts.
getMaxIts() - Method in class weka.classifiers.functions.RBFNetwork: Get the value of MaxIts.
getMaxK() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of maxK.
getMaxKMeans() - Method in class weka.clusterers.XMeans: Gets the maximum number of iterations in KMeans.
getMaxKMeansForChildren() - Method in class weka.clusterers.XMeans: Gets the maximum number of iterations in KMeans.
getMaxLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the maximum number of leaves visited.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Gets the max number of parents.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.global.K2: Gets the max number of parents.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Gets the max number of parents.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.K2: Gets the max number of parents.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Gets the max number of parents.
getMaxNumberOfItems() - Method in class weka.associations.FPGrowth: Gets the maximum number of items to be included in large item sets.
getMaxNumClusters() - Method in class weka.clusterers.XMeans: Gets the maximum number of clusters to generate.
getMaxPlots() - Method in class weka.gui.beans.AttributeSummarizer: Get the number of plots to display
getMaxPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the maximum of points visited.
getMaxRadius() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the upper boundary for the radiuses of the clusters.
getMaxRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Gets the upper boundary for the range of x
getMaxRelativeLeafRadius() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the maximum relative radius of a leaf node.
getMaxRows() - Method in class weka.gui.sql.event.QueryExecuteEvent: returns the maximum number of rows to retrieve.
getMaxRows() - Method in class weka.gui.sql.QueryPanel: returns the current value for the maximum number of rows.
getMaxRows() - Method in class weka.gui.sql.ResultSetHelper: the maximum number of rows to retrieve, less than 1 means unlimited.
getMaxRuleSize() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the maximum number of tests in rules.
getMaxRunNumber() - Method in class weka.gui.beans.BatchClassifierEvent: Get the maximum run number
getMaxRunNumber() - Method in class weka.gui.beans.TestSetEvent: Get the maximum number of runs.
getMaxRunNumber() - Method in class weka.gui.beans.TrainingSetEvent: Get the maximum number of runs.
getMaxSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent: Get the maximum set number (ie the total number of training and testing sets in the series).
getMaxSetNumber() - Method in class weka.gui.beans.BatchClustererEvent: Get the maximum set number (ie the total number of training and testing sets in the series).
getMaxSetNumber() - Method in class weka.gui.beans.TestSetEvent: Get the maximum set number
getMaxSetNumber() - Method in class weka.gui.beans.TrainingSetEvent: Get the maximum set number
getMaxSubsequenceLength() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the maximum length of the subsequence
getMaxThreshold() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Get the maximum threshold.
getMaxValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
getMaxVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin: Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin: Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin: Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin: Get the maximum version of Weka, exclusive, the class is designed to work with.
getMaxX() - Method in class weka.gui.visualize.Plot2D: Return the current max value of the attribute plotted on the x axis
getMaxXBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Gets the maximum x-coordinate bound, in training-instance units (not mouse coordinates).
getMaxY() - Method in class weka.gui.visualize.Plot2D: Return the current max value of the attribute plotted on the y axis
getMaxYBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Gets the maximum x-coordinate bound, in training-instance units (not mouse coordinates).
getMean() - Method in class weka.estimators.NormalEstimator: Return the value of the mean of this normal estimator.
getMean(int, int) - Method in class weka.experiment.ResultMatrix: returns the mean at the given position, if the position is valid, otherwise 0
getMeanCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the mean of coords per point.
getMeanIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the mean of internal nodes visited.
getMeanLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the mean of number of leaves visited.
getMeanPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the mean of points visited.
getMeanPrec() - Method in class weka.experiment.ResultMatrix: returns the current precision for the means
getMeanPrec() - Method in class weka.gui.experiment.OutputFormatDialog: Gets the precision used for printing the mean.
getMeanPrecision() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default precision for the mean
getMeans() - Method in class weka.estimators.KernelEstimator: Return the means of the kernels.
getMeanSquared() - Method in class weka.classifiers.lazy.IBk: Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
getMeanStddev() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: returns the current mean/stddev setup
getMeanValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
getMeanWidth() - Method in class weka.experiment.ResultMatrix: returns the current width for the mean
getMeasure(String) - Method in class weka.classifiers.bayes.BayesNet: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.functions.SimpleLogistic: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.functions.SMOreg: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.lazy.IBk: Returns the value of the named measure from the neighbour search algorithm, plus the chosen K in case cross-validation is enabled.
getMeasure(String) - Method in class weka.classifiers.lazy.LWL: Returns the value of the named measure from the neighbour search algorithm.
getMeasure(String) - Method in class weka.classifiers.meta.AdditiveRegression: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.meta.Bagging: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.meta.GridSearch: Returns the value of the named measure
getMeasure() - Method in class weka.classifiers.meta.ThresholdSelector: get measure used for determining threshold
getMeasure(String) - Method in class weka.classifiers.rules.DecisionTable: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.DTNB: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.JRip: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.PART: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.Ridor: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.ADTree: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.BFTree: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.FT: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.J48: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.J48graft: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.LADTree: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.LMT: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.m5.M5Base: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.NBTree: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.RandomForest: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.REPTree: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.SimpleCart: Returns the value of the named measure.
getMeasure(String) - Method in interface weka.core.AdditionalMeasureProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.core.neighboursearch.BallTree: Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.CoverTree: Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.KDTree: Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.PerformanceStats: Returns the value of the named measure.
getMeasure(String) - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the value of the named measure.
getMeasure(String) - Method in class weka.experiment.AveragingResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.ClassifierSplitEvaluator: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.CrossValidationResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DatabaseResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.LearningRateResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RandomSplitResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RegressionSplitEvaluator: Returns the value of the named measure
getMeasurePerformance() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Gets whether performance statistics are being calculated or not.
getMembershipValues(Instance) - Method in class weka.classifiers.trees.RandomTree: Computes array that indicates node membership.
getMenu() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the menu bar to be added in a frame
getMenuBar() - Method in class weka.classifiers.bayes.net.GUI: Get the menu bar for this application.
getMenuTitle() - Method in interface weka.gui.MainMenuExtension: Returns the name of the menu item.
getMestWeight() - Method in class weka.classifiers.bayes.AODEsr: Gets the weight used in m-estimate
getMetaClassifier() - Method in class weka.classifiers.meta.Stacking: Gets the meta classifier.
getMetadata() - Method in class weka.core.Attribute: Returns the properties supplied for this attribute.
getMetaData() - Method in class weka.core.converters.DatabaseConnection: Gets meta data for the database connection object.
getMethod() - Method in class weka.classifiers.functions.neural.NeuralNode
getMethod() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets the method used.
getMethod() - Method in class weka.classifiers.mi.MIWrapper: Get the method used in testing.
getMethodName() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the transformation method.
getMetricType() - Method in class weka.associations.Apriori: Get the metric type
getMetricType() - Method in class weka.associations.FPGrowth.AssociationRule: Get the metric type of this rule (e.g.
getMetricType() - Method in class weka.associations.FPGrowth: Get the metric type to use.
getMetricValue() - Method in class weka.associations.FPGrowth.AssociationRule: Get the value of the metric for this rule.
getMiddle(double[]) - Method in class weka.core.EuclideanDistance: Returns value in the middle of the two parameter values.
getMidPoints() - Method in class weka.associations.PriorEstimation: returns an ordered array of all mid points
getMin() - Method in class weka.gui.beans.ChartEvent: Get the min y value
getMinArray() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns the calculated minimum values for the attributes in the data.
getMinBoxRelWidth() - Method in class weka.core.neighboursearch.KDTree: Gets the minimum relative box width.
getMinBucketSize() - Method in class weka.classifiers.rules.OneR: Get the value of minBucketSize.
getMinC() - Method in class weka.gui.visualize.Plot2D: Return the current min value of the colouring attribute
getMinChange() - Method in class weka.clusterers.sIB: get the minimum number of changes
getMinChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the minimum chunk size
getMinCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the minimum of coords per point.
getMinDefault() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Get the minimum default.
getMinFunction() - Method in class weka.core.Optimization: Get the minimal function value
getMinGroup() - Method in class weka.classifiers.meta.RotationForest: Gets the minimum size of a group.
getMinimax() - Method in class weka.classifiers.mi.MISMO: Check if the MIMinimax feature space is to be used.
getMinimizeExpectedCost() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the value of MinimizeExpectedCost.
getMinimumBucketSize() - Method in class weka.attributeSelection.OneRAttributeEval: Get the minimum bucket size used by oneR
getMinimumNumberInstances() - Method in class weka.core.Capabilities: returns the minimum number of instances that have to be in the dataset
getMiningFields() - Method in class weka.core.pmml.MiningSchema
getMiningSchema() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Get the mining schema for this model.
getMiningSchema() - Method in interface weka.core.pmml.PMMLModel: Get the mining schema.
getMiningSchemaAsInstances() - Method in class weka.core.pmml.MiningSchema: Get the mining schema fields as an Instances object.
getMinInstNum() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the lower boundary for instances per cluster.
getMinInstNum() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Gets the lower boundary for instances per cluster.
getMinIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the minimum of internal nodes visited.
getMinLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the minimum number of leaves visited.
getMinLevel() - Method in class weka.core.logging.Logger: Returns the minimum level log messages must have in order to appear in the log.
getMinMax(Instances, int, double[]) - Static method in class weka.estimators.CheckEstimator: Find the minimum and the maximum of the attribute and return it in the last parameter..
getMinMax(Instances, int, double[]) - Static method in class weka.estimators.EstimatorUtils: Find the minimum and the maximum of the attribute and return it in the last parameter..
getMinMetric() - Method in class weka.associations.Apriori: Get the value of minConfidence.
getMinMetric() - Method in class weka.associations.FPGrowth: Get the value of minConfidence.
getMinNo() - Method in class weka.classifiers.rules.ConjunctiveRule: Gets the minimum total weight of the instances in a rule
getMinNo() - Method in class weka.classifiers.rules.JRip: Gets the minimum total weight of the instances in a rule
getMinNo() - Method in class weka.classifiers.rules.Ridor
getMinNum() - Method in class weka.classifiers.trees.RandomTree: Get the value of MinNum.
getMinNum() - Method in class weka.classifiers.trees.REPTree: Get the value of MinNum.
getMinNumClusters() - Method in class weka.clusterers.XMeans: Gets the minimum number of clusters to generate.
getMinNumInstances() - Method in class weka.classifiers.trees.FT: Get the value of minNumInstances.
getMinNumInstances() - Method in class weka.classifiers.trees.LMT: Get the value of minNumInstances.
getMinNumInstances() - Method in class weka.classifiers.trees.m5.M5Base: Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.Rule: Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode: Get the minimum number of instances to allow at a leaf node
getMinNumObj() - Method in class weka.classifiers.rules.PART: Get the value of minNumObj.
getMinNumObj() - Method in class weka.classifiers.trees.BFTree: Get minimal number of instances at the terminal nodes.
getMinNumObj() - Method in class weka.classifiers.trees.J48: Get the value of minNumObj.
getMinNumObj() - Method in class weka.classifiers.trees.J48graft: Get the value of minNumObj.
getMinNumObj() - Method in class weka.classifiers.trees.SimpleCart: Get minimal number of instances at the terminal nodes.
getMinPoints() - Method in class weka.clusterers.DBSCAN: Returns the value of minPoints
getMinPoints() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the number of minPoints
getMinPoints() - Method in class weka.clusterers.OPTICS: Returns the value of minPoints
getMinPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the minimum of points visited.
getMinRadius() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the lower boundary for the radiuses of the clusters.
getMinRange() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Gets the lower boundary for the range of x
getMinRuleSize() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the minimum number of tests in rules.
getMinStdDev() - Method in class weka.classifiers.functions.RBFNetwork: Get the MinStdDev value.
getMinStdDev() - Method in class weka.clusterers.EM: Get the minimum allowable standard deviation.
getMinStdDev() - Method in class weka.clusterers.MakeDensityBasedClusterer: Get the minimum allowable standard deviation.
getMinSupport() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the minimum support threshold.
getMinTermFreq() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the MinTermFreq value.
getMinThreshold() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Get the minimum threshold.
getMinValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
getMinVarianceProp() - Method in class weka.classifiers.trees.REPTree: Get the value of MinVarianceProp.
getMinVersion() - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin: Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinVersion() - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin: Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinVersion() - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin: Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinVersion() - Method in interface weka.gui.visualize.plugins.VisualizePlugin: Get the minimum version of Weka, inclusive, the class is designed to work with.
getMinX() - Method in class weka.gui.visualize.Plot2D: Return the current min value of the attribute plotted on the x axis
getMinXBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Gets the minimum x-coordinate bound, in training-instance units (not mouse coordinates).
getMinY() - Method in class weka.gui.visualize.Plot2D: Return the current min value of the attribute plotted on the y axis
getMinYBound() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Gets the minimum y-coordinate bound, in training-instance units (not mouse coordinates).
getMisses() - Method in class weka.core.FindWithCapabilities: returns the misses from the last find call.
getMissingMerge() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.GainRatioAttributeEval: get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.InfoGainAttributeEval: get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: get whether missing values are being distributed or not
getMissingMode() - Method in class weka.classifiers.lazy.KStar: Gets the method to use for handling missing values.
getMissingSeparate() - Method in class weka.attributeSelection.CfsSubsetEval: Return true is missing is treated as a separate value
getMissingValue() - Method in class weka.core.converters.CSVLoader: Returns the current placeholder for missing values.
getMissingValues() - Method in class weka.associations.Tertius: Get the value of missingValues.
getMissingValueTreatmentMethod() - Method in class weka.core.pmml.MiningFieldMetaInfo: Get the missing value treatment method for this field.
getMixingDistribution() - Method in class weka.classifiers.functions.pace.MixtureDistribution: Gets the mixing distribution
getModel() - Method in class weka.classifiers.functions.LibLINEAR
getModel() - Method in class weka.classifiers.trees.m5.RuleNode: Get the linear model at this node
getModel() - Method in class weka.gui.SortedTableModel: returns the current model, can be null
getModelFile() - Method in class weka.classifiers.misc.SerializedClassifier: Gets the file containing the serialized model.
getModelParameters() - Method in class weka.classifiers.trees.ft.FTtree: Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
getModelParameters() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
getModelType() - Method in class weka.classifiers.trees.FT: Get the type of functional tree model being used.
getModelValueAt(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns the value at the given position
getModifyHeader() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Gets whether the header will be modified when selecting on nominal attributes.
getModifyHeader() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Gets whether the header will be modified when selecting on nominal attributes.
getMomentum() - Method in class weka.classifiers.functions.MultilayerPerceptron
getMultiInstance() - Method in class weka.core.TestInstances: Gets whether multi-instance data (with a fixed structure) is generated
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.CitationKNN: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MDD: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIBoost: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIDD: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIEMDD: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MILR: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MINND: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIOptimalBall: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MISMO: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MISVM: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.MIWrapper: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.SimpleMI: Returns the capabilities of this multi-instance classifier for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel: Returns the capabilities of this multi-instance kernel for the relational data.
getMultiInstanceCapabilities() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel: Returns the capabilities of this multi-instance kernel for the relational data.
getMultiInstanceCapabilities() - Method in interface weka.core.MultiInstanceCapabilitiesHandler: Returns the capabilities of this multi-instance classifier for the relational data (i.e., the bags).
getMultiInstanceCapabilities() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Returns the capabilities of this multi-instance filter for the relational data (i.e., the bags).
getMultinomialWord() - Method in class weka.classifiers.bayes.DMNBtext: Gets whether use binary text representation
getMutationProb() - Method in class weka.attributeSelection.GeneticSearch: get the probability of mutation
getNaiveBayesModel() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Get the naive bayes model at this node
getName() - Method in class weka.classifiers.bayes.BayesNet: get name of the Bayes network
getName() - Method in class weka.core.pmml.Function
getName() - Method in class weka.core.pmml.MiningFieldMetaInfo: Get the name of this field.
getName() - Method in class weka.core.PropertyPath.PathElement: returns the name of the property
getName() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the name of the new attribute
getName() - Method in class weka.gui.visualize.VisualizePanel: Returns the name associated with this plot.
getNameAtIndex(int) - Method in class weka.gui.ResultHistoryPanel: Gets the name of theitem in the list at the specified index
getNamedBuffer(String) - Method in class weka.gui.ResultHistoryPanel: Gets the named buffer
getNamedObject(String) - Method in class weka.gui.ResultHistoryPanel: Get the named object from the list
getNearestNeighbors() - Method in class weka.filters.supervised.instance.SMOTE: Gets the number of nearest neighbors to use.
getNearestNeighbourSearchAlgorithm() - Method in class weka.classifiers.lazy.IBk: Returns the current nearestNeighbourSearch algorithm in use.
getNearestNeighbourSearchAlgorithm() - Method in class weka.classifiers.lazy.LWL: Returns the current nearestNeighbourSearch algorithm in use.
getNegation() - Method in class weka.associations.Tertius: Get the value of negation.
getNegation() - Method in class weka.associations.tertius.Literal
getNext(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Gets the next element in the set.
getNextDebugVectorsInstance(Instances) - Method in class weka.clusterers.XMeans: Read an instance from debug vectors file.
getNextInstance(Instances) - Method in class weka.core.converters.AbstractLoader
getNextInstance(Instances) - Method in class weka.core.converters.ArffLoader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.C45Loader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.CSVLoader: CSVLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in class weka.core.converters.DatabaseLoader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.LibSVMLoader: LibSVmLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in interface weka.core.converters.Loader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.SerializedInstancesLoader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.SVMLightLoader: SVMLightLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in class weka.core.converters.TextDirectoryLoader: TextDirectoryLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in class weka.core.converters.XRFFLoader: XRFFLoader is unable to process a data set incrementally.
getNGramMaxSize() - Method in class weka.core.tokenizers.NGramTokenizer: Gets the max N of the NGram.
getNGramMinSize() - Method in class weka.core.tokenizers.NGramTokenizer: Gets the min N of the NGram.
getNoClass() - Method in class weka.core.TestInstances: whether no class attribute is generated
getNode(String) - Method in class weka.classifiers.bayes.net.BIFReader: getNode finds the index of the node with name sNodeName and throws an exception if no such node can be found.
getNode(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: returns index of node with given name.
getNode(String) - Method in class weka.classifiers.bayes.net.MarginCalculator
getNode(String) - Method in class weka.core.xml.XMLDocument: Returns the node represented by the XPath expression.
getNode2(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: returns index of node with given name, or -1 if no such node exists
getNodeName(int) - Method in class weka.classifiers.bayes.BayesNet: get name of a node in the Bayes network
getNodes() - Method in class weka.classifiers.trees.ft.FTtree: Return a list of all inner nodes in the tree
getNodes(Vector) - Method in class weka.classifiers.trees.ft.FTtree: Fills a list with all inner nodes in the tree
getNodes() - Method in class weka.classifiers.trees.lmt.LMTNode: Return a list of all inner nodes in the tree
getNodes(Vector) - Method in class weka.classifiers.trees.lmt.LMTNode: Fills a list with all inner nodes in the tree
getNodes() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: give access to set of graph nodes
getNodes() - Method in interface weka.gui.graphvisualizer.LayoutEngine: give access to set of graph nodes
getNodeSplitter() - Method in class weka.core.neighboursearch.KDTree: Returns the splitting method currently in use to split the nodes of the KDTree.
getNodeValue(int, int) - Method in class weka.classifiers.bayes.BayesNet: get name of a particular value of a node
getNoise() - Method in class weka.classifiers.functions.GaussianProcesses: Get the value of noise.
getNoisePercent() - Method in class weka.datagenerators.classifiers.classification.LED24: Gets the noise percentage.
getNoiseRate() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Gets the gaussian noise rate.
getNoiseRate() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the percentage of noise set.
getNoiseRate() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Gets the percentage of noise set.
getNoiseThreshold() - Method in class weka.associations.Tertius: Get the value of noiseThreshold.
getNoiseVariance() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Gets the noise variance
getNominalAttributes() - Method in class weka.core.converters.CSVLoader: Returns the current attribute range to be forced to type nominal.
getNominalCols() - Method in class weka.datagenerators.ClusterGenerator: returns the range of nominal attributes
getNominalIndices() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the set of nominal value indices that will be used for selection
getNominalLabels() - Method in class weka.filters.unsupervised.attribute.Add: Get the list of labels for nominal attribute creation.
getNominalToBinaryFilter() - Method in class weka.classifiers.functions.MultilayerPerceptron
getNoPruning() - Method in class weka.classifiers.trees.REPTree: Get the value of NoPruning.
getNoReplacement() - Method in class weka.filters.supervised.instance.Resample: Gets whether instances are drawn with or without replacement.
getNoReplacement() - Method in class weka.filters.unsupervised.instance.Resample: Gets whether instances are drawn with or without replacement.
getNorm() - Method in class weka.filters.unsupervised.instance.Normalize: Get the instance's Norm.
getNormalize() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Gets whether or not input data is to be normalized
getNormalize() - Method in class weka.classifiers.functions.LibLINEAR: whether to normalize input data
getNormalize() - Method in class weka.classifiers.functions.LibSVM: whether to normalize input data
getNormalizeAttributes() - Method in class weka.classifiers.functions.MultilayerPerceptron
getNormalizeDimWidths() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Whether we are normalizing the widths(ranges) of the dimensions (attributes) or not.
getNormalizeDocLength() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the word frequencies for a document (instance) should be normalized or not.
getNormalizeNodeWidth() - Method in class weka.core.neighboursearch.KDTree: Gets the normalize flag.
getNormalizeNumericClass() - Method in class weka.classifiers.functions.MultilayerPerceptron
getNormalizeWordWeights() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns true if the word weights for each class are to be normalized
getNot() - Method in class weka.datagenerators.Test: Negates the test.
getNotCapabilities() - Method in class weka.core.FindWithCapabilities: The "not to have" capabilities to search for.
getNotes() - Method in class weka.experiment.Experiment: Get the user notes.
getNotUnifyNorm() - Method in class weka.clusterers.sIB: Get whether to normalize instances to unify prior probability before building the clusterer
getNPointPrecision(Instances, int) - Static method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the n point precision result, which is the precision averaged over n evenly spaced (w.r.t recall) samples of the curve.
getNrOfGoodOperations() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Gets the number of "good operations"
getNrOfLookAheadSteps() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Gets the number of look-ahead steps
getNrOfNodes() - Method in class weka.classifiers.bayes.BayesNet: get number of nodes in the Bayes network
getNrOfParents(int) - Method in class weka.classifiers.bayes.BayesNet: get number of parents of a node in the network structure
getNrOfParents() - Method in class weka.classifiers.bayes.net.ParentSet: returns number of parents
getNu() - Method in class weka.classifiers.functions.LibSVM: Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
getNumAntds() - Method in class weka.classifiers.rules.ConjunctiveRule: Gets the number of antecedants
getNumArcs() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Gets the number of arcs for the bayesian net
getNumAttemptsOfGeneOption() - Method in class weka.classifiers.rules.NNge: Gets the number of attempts for generalisation.
getNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes in the dataset
getNumAttributes() - Method in class weka.core.TestInstances: returns the overall number of attributes (incl.
getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.ClusterGenerator: Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Get the number of attributes (< 1 percentage, >= 1 absolute number).
getNumAttributesSet() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes "in use"
getNumberLiterals() - Method in class weka.associations.Tertius: Get the value of numberLiterals.
getNumberOfAttributes() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the number of Attributes of the specified database
getNumberOfAttributes() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the current number of attributes (dimensionality) to which the data will be reduced to.
getNumberOfGeneratedClusters() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the number of generated clusters
getNumberOfGroups() - Method in class weka.classifiers.meta.RotationForest: Get whether minGroup and maxGroup refer to the number of groups or their size
getNumBins() - Method in class weka.classifiers.meta.RegressionByDiscretization: Gets the number of bins numeric attributes will be divided into
getNumBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of numBoostingIterations.
getNumBoostingIterations() - Method in class weka.classifiers.trees.FT: Get the value of numBoostingIterations.
getNumBoostingIterations() - Method in class weka.classifiers.trees.LMT: Get the value of numBoostingIterations.
getNumCentroids() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Gets the number of centroids.
getNumCiters() - Method in class weka.classifiers.mi.CitationKNN: Returns the number of citers considered to estimate the class prediction of tests bags
getNumClasses() - Method in class weka.core.TestInstances: returns the current number of classes
getNumClasses() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Gets the number of classes the dataset should have.
getNumClasses() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the number of classes the dataset should have.
getNumClusters() - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator: Return the number of clusters used by the subset evaluator
getNumClusters() - Method in class weka.classifiers.functions.RBFNetwork: Return the number of clusters to generate.
getNumClusters() - Method in class weka.clusterers.ClusterEvaluation: Return the number of clusters found for the most recent call to evaluateClusterer
getNumClusters() - Method in class weka.clusterers.EM: Get the number of clusters
getNumClusters() - Method in class weka.clusterers.FarthestFirst: gets the number of clusters to generate
getNumClusters() - Method in class weka.clusterers.HierarchicalClusterer
getNumClusters() - Method in class weka.clusterers.sIB: Get the number of clusters
getNumClusters() - Method in class weka.clusterers.SimpleKMeans: gets the number of clusters to generate
getNumClusters() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the number of clusters the dataset should have.
getNumComponents() - Method in class weka.filters.supervised.attribute.PLSFilter: returns the maximum number of attributes to use.
getNumCycles() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the number of cycles.
getNumDatasets() - Method in class weka.experiment.PairedTTester: Gets the number of datasets in the resultsets
getNumDatasets() - Method in interface weka.experiment.Tester: Gets the number of datasets in the resultsets
getNumDate() - Method in class weka.core.CheckScheme: returns the current number of date attributes
getNumDate() - Method in class weka.core.TestInstances: returns the current number of date attributes
getNumeric() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Check if new attribute is to be numeric.
getNumericColumns() - Method in class weka.gui.sql.ResultSetHelper: returns an array that indicates whether a column is numeric or nor.
getNumEvalsCached() - Method in class weka.attributeSelection.LFSMethods
getNumEvalsTotal() - Method in class weka.attributeSelection.LFSMethods
getNumExamples() - Method in class weka.datagenerators.ClassificationGenerator: Gets the number of examples, given by option.
getNumExamples() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Gets the number of examples, given by option.
getNumExamples() - Method in class weka.datagenerators.RegressionGenerator: Gets the number of examples, given by option.
getNumExamplesAct() - Method in class weka.datagenerators.DataGenerator: Gets the number of examples the dataset should have.
getNumFeatures() - Method in class weka.classifiers.trees.RandomForest: Get the number of features used in random selection.
getNumFiles() - Method in class weka.core.Debug.Log: returns the number of files being used
getNumFoldersMIOption() - Method in class weka.classifiers.rules.NNge: Gets the number of folder for mutual information.
getNumFolds() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Return the number of folds for CV-based hyperparameter selection
getNumFolds() - Method in class weka.classifiers.functions.SMO: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.meta.CVParameterSelection: Gets the number of folds for the cross-validation.
getNumFolds() - Method in class weka.classifiers.meta.Dagging: Gets the number of folds to use for splitting the training set.
getNumFolds() - Method in class weka.classifiers.meta.LogitBoost: Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.meta.MultiScheme: Gets the number of folds for cross-validation.
getNumFolds() - Method in class weka.classifiers.meta.Stacking: Gets the number of folds for the cross-validation.
getNumFolds() - Method in class weka.classifiers.mi.MISMO: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.rules.PART: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.J48: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.RandomTree: Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.trees.REPTree: Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.CrossValidationResultProducer: Get the value of NumFolds.
getNumFolds() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the number of folds in which dataset is to be split into.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the number of folds in which dataset is to be split into.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the number of cross-validation folds used by the filter.
getNumFoldsPruning() - Method in class weka.classifiers.trees.BFTree: Set number of folds in internal cross-validation.
getNumFoldsPruning() - Method in class weka.classifiers.trees.SimpleCart: Set number of folds in internal cross-validation.
getNumGeneratingModels() - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Returns the number of generating models used by this DataGenerator
getNumGeneratingModels() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Return the number of kernels (there is one per training instance)
getNumInnerNodes() - Method in class weka.classifiers.trees.ft.FTtree: Method to count the number of inner nodes in the tree
getNumInnerNodes() - Method in class weka.classifiers.trees.lmt.LMTNode: Method to count the number of inner nodes in the tree
getNumInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
getNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of instances in the dataset
getNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode: Return the number of instances that reach this node.
getNumInstances() - Method in class weka.core.CheckScheme: Gets the current number of instances to use for the datasets.
getNumInstances() - Method in class weka.core.TestInstances: returns the current number of instances to produce
getNumInstances() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
getNumInstances() - Method in class weka.estimators.CheckEstimator: Gets the current number of instances to use for the datasets.
getNumInstancesRelational() - Method in class weka.core.CheckScheme: returns the current number of instances in relational/bag attributes to produce
getNumInstancesRelational() - Method in class weka.core.TestInstances: returns the current number of instances in relational/bag attributes to produce
getNumInstancesSet() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of instances "in use"
getNumIrrelevant() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the number of irrelevant attributes.
getNumIterations() - Method in class weka.classifiers.bayes.DMNBtext: Gets the number of iterations to be performed
getNumIterations() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of NumIterations.
getNumIterations() - Method in class weka.classifiers.functions.Winnow: Get the value of numIterations.
getNumIterations() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Gets the number of bagging iterations
getNumIterations() - Method in class weka.classifiers.meta.MetaCost: Gets the number of bagging iterations
getNumKernels() - Method in class weka.estimators.KernelEstimator: Return the number of kernels in this kernel estimator
getNumLeaves() - Method in class weka.classifiers.trees.ft.FTtree: Returns the number of leaves in the tree.
getNumLeaves() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the number of leaves in the tree.
getNumLeaves() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the number of leaves in the built tree.
getNumNeighbours() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the number of nearest neighbours
getNumNeighbours() - Method in class weka.classifiers.mi.MINND: Returns the number of nearest neighbours to estimate the class prediction of tests bags
getNumNodes() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the number of nodes (internal + leaf) in the built tree.
getNumNominal() - Method in class weka.core.CheckScheme: returns the current number of nominal attributes
getNumNominal() - Method in class weka.core.TestInstances: returns the current number of nominal attributes
getNumNominalValues() - Method in class weka.core.TestInstances: returns the current number of values for nominal attributes
getNumNumeric() - Method in class weka.core.CheckScheme: returns the current number of numeric attributes
getNumNumeric() - Method in class weka.core.TestInstances: returns the current number of numeric attributes
getNumNumeric() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the number of numerical attributes.
getNumOfBoostingIterations() - Method in class weka.classifiers.trees.ADTree: Gets the number of boosting iterations.
getNumOfBoostingIterations() - Method in class weka.classifiers.trees.LADTree: Gets the number of boosting iterations.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.Splitter: Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the number of branches of the split.
getNumOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
getNumQueries() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the number of queries.
getNumReferences() - Method in class weka.classifiers.mi.CitationKNN: Returns the number of references considered to estimate the class prediction of tests bags
getNumRegressions() - Method in class weka.classifiers.functions.SimpleLogistic: Get the number of LogitBoost iterations performed (= the number of regression functions fit by LogitBoost).
getNumRegressions() - Method in class weka.classifiers.trees.lmt.LogisticBase: The number of LogitBoost iterations performed (= the number of simple regression functions fit).
getNumRelational() - Method in class weka.core.CheckScheme: returns the current number of relational attributes
getNumRelational() - Method in class weka.core.TestInstances: returns the current number of relational attributes
getNumRelationalDate() - Method in class weka.core.TestInstances: returns the current number of date attributes in a relational attribute
getNumRelationalNominal() - Method in class weka.core.TestInstances: returns the current number of nominal attributes in a relational attribute
getNumRelationalNominalValues() - Method in class weka.core.TestInstances: returns the current number of values for nominal attributes in a relational attribute
getNumRelationalNumeric() - Method in class weka.core.TestInstances: returns the current number of numeric attributes in a relational attribute
getNumRelationalString() - Method in class weka.core.TestInstances: returns the current number of string attributes in a relational attribute
getNumRestarts() - Method in class weka.clusterers.sIB: Get the number of restarts
getNumResultsets() - Method in class weka.experiment.PairedTTester: Gets the number of resultsets in the data.
getNumResultsets() - Method in interface weka.experiment.Tester: Gets the number of resultsets in the data.
getNumRules() - Method in class weka.associations.Apriori: Get the value of numRules.
getNumRules() - Method in class weka.associations.PredictiveApriori: Get the value of the number of required rules.
getNumRulesToFind() - Method in class weka.associations.FPGrowth: Get the number of rules to find.
getNumRuns() - Method in class weka.classifiers.meta.LogitBoost: Get the value of NumRuns.
getNumSamplesPerRegion() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the number of points to sample from a region (fixed dimensions).
getNumString() - Method in class weka.core.CheckScheme: returns the current number of string attributes
getNumString() - Method in class weka.core.TestInstances: returns the current number of string attributes
getNumSubCmtys() - Method in class weka.classifiers.meta.MultiBoostAB: Get the number of sub committees to use
getNumSubsetSizeCVFolds() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Get the number of cross validation folds for subset size determination (default = 5).
getNumSymbols() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Gets the number of symbols this estimator operates with
getNumSymbols() - Method in class weka.estimators.DiscreteEstimator: Gets the number of symbols this estimator operates with
getNumTestingNoises() - Method in class weka.classifiers.mi.MINND: Returns The number of nearest neighbour instances in the selection of noises in the test data
getNumToSelect() - Method in class weka.attributeSelection.GreedyStepwise: Gets the number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.RaceSearch: Gets the number of attributes to be retained.
getNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets the user specified number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.Ranker: Gets the number of attributes to be retained.
getNumTraining() - Method in class weka.classifiers.lazy.IBk: Get the number of training instances the classifier is currently using.
getNumTrainingNoises() - Method in class weka.classifiers.mi.MINND: Returns the number of nearest neighbour instances in the selection of noises in the training data
getNumTrees() - Method in class weka.classifiers.trees.RandomForest: Get the value of numTrees.
getNumUsedAttributes() - Method in class weka.attributeSelection.LinearForwardSelection: Get the number of top-ranked attributes that taken into account by the search process.
getNumUsedAttributes() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Get the number of top-ranked attributes that taken into account by the search process.
getNumValues() - Method in class weka.datagenerators.clusterers.SubspaceCluster: returns array that stores the number of values for a nominal attribute.
getNumValues() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Gets how many values are retained
getNumXValFolds() - Method in class weka.classifiers.meta.ThresholdSelector: Get the number of folds used for cross-validation.
getObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement: Returns the object
getObject() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement: Returns the object
getObject() - Method in class weka.core.CheckGOE: Get the object used in the tests.
getObject() - Method in class weka.core.SerializedObject: Returns a serialized object.
getObjectKey() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement: Returns the key
getObservedFrequency() - Method in class weka.associations.tertius.Rule: Get the observed frequency of counter-instances of this rule in the dataset.
getObservedNumber() - Method in class weka.associations.tertius.Rule: Get the observed number of counter-instances of this rule in the dataset.
getOmega() - Method in class weka.classifiers.functions.supportVector.Puk: Gets the omega value.
getOnDemandDirectory() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.classifiers.meta.MetaCost: Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns the directory that will be searched for cost files when loading on demand.
getOneElements(Instances) - Static method in class weka.associations.gsp.Element: Returns all events of the given data set as Elements containing a single event.
getOptimistic() - Method in class weka.associations.tertius.Rule: Get the optimistic estimate of the confirmation obtained by refining this rule.
getOptimizations() - Method in class weka.classifiers.rules.JRip: Gets the the number of optimization runs
getOption(char, String[]) - Static method in class weka.core.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class weka.core.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOptionHandler() - Method in class weka.core.CheckOptionHandler: Get the OptionHandler used in the tests.
getOptionPos(char, String[]) - Static method in class weka.core.Utils: Gets the index of an option or flag indicated by a flag "-Char" from the given array of strings.
getOptionPos(String, String[]) - Static method in class weka.core.Utils: Gets the index of an option or flag indicated by a flag "-String" from the given array of strings.
getOptions() - Method in class weka.associations.Apriori: Gets the current settings of the Apriori object.
getOptions() - Method in class weka.associations.CheckAssociator: Gets the current settings of the CheckAssociator.
getOptions() - Method in class weka.associations.FilteredAssociator: Gets the current settings of the Associator.
getOptions() - Method in class weka.associations.FPGrowth: Gets the current settings of the classifier.
getOptions() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns an Array containing the current options settings.
getOptions() - Method in class weka.associations.PredictiveApriori: Gets the current settings of the PredictiveApriori object.
getOptions() - Method in class weka.associations.SingleAssociatorEnhancer: Gets the current settings of the associator.
getOptions() - Method in class weka.associations.Tertius: Gets the current settings of the Tertius object.
getOptions() - Method in class weka.attributeSelection.BestFirst: Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.CfsSubsetEval: Gets the current settings of CfsSubsetEval
getOptions() - Method in class weka.attributeSelection.CheckAttributeSelection: Gets the current settings of the CheckAttributeSelection.
getOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Gets the current settings.
getOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval: Gets the current settings of ClassifierSubsetEval
getOptions() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Gets the current settings of the subset evaluator.
getOptions() - Method in class weka.attributeSelection.ExhaustiveSearch: Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.FilteredAttributeEval: Gets the current settings of the subset evaluator.
getOptions() - Method in class weka.attributeSelection.FilteredSubsetEval: Gets the current settings of the subset evaluator.
getOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.GeneticSearch: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.GreedyStepwise: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Gets the current settings of LatentSemanticAnalysis
getOptions() - Method in class weka.attributeSelection.LinearForwardSelection: Gets the current settings of LinearForwardSelection.
getOptions() - Method in class weka.attributeSelection.OneRAttributeEval: returns the current setup.
getOptions() - Method in class weka.attributeSelection.PrincipalComponents: Gets the current settings of PrincipalComponents
getOptions() - Method in class weka.attributeSelection.RaceSearch: Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.RandomSearch: Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.Ranker: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.RankSearch: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.ScatterSearchV1: Gets the current settings of ScatterSearchV1.
getOptions() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Gets the current settings of LinearForwardSelection.
getOptions() - Method in class weka.attributeSelection.SVMAttributeEval: Gets the current settings of SVMAttributeEval
getOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.WrapperSubsetEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.classifiers.bayes.AODE: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.AODEsr: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
getOptions() - Method in class weka.classifiers.bayes.BayesNet: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.DMNBtext: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.NaiveBayes: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.BayesNetGenerator: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.K2: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.TAN: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.K2: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.TAN: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.WAODE: Gets the current settings of the filter.
getOptions() - Method in class weka.classifiers.BVDecompose: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.CheckClassifier: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.CheckSource: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.Classifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.functions.GaussianProcesses: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.LeastMedSq: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.functions.LibLINEAR: Returns the current options
getOptions() - Method in class weka.classifiers.functions.LibSVM: Returns the current options
getOptions() - Method in class weka.classifiers.functions.LinearRegression: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.Logistic: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.MultilayerPerceptron: Gets the current settings of NeuralNet.
getOptions() - Method in class weka.classifiers.functions.PaceRegression: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.PLSClassifier: returns the options of the current setup
getOptions() - Method in class weka.classifiers.functions.RBFNetwork: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SimpleLogistic: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.functions.SMO: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SMOreg: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SPegasos: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.supportVector.CachedKernel: Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.CheckKernel: Gets the current settings of the CheckKernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.Kernel: Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.Puk: Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.supportVector.RegSMO: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Gets the current settings of the object.
getOptions() - Method in class weka.classifiers.functions.supportVector.StringKernel: Gets the current settings of the Kernel.
getOptions() - Method in class weka.classifiers.functions.VotedPerceptron: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.Winnow: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.lazy.IBk: Gets the current settings of IBk.
getOptions() - Method in class weka.classifiers.lazy.KStar: Gets the current settings of K*.
getOptions() - Method in class weka.classifiers.lazy.LWL: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.meta.AdaBoostM1: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AdditiveRegression: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Bagging: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.ClassificationViaClustering: returns the options of the current setup
getOptions() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.CVParameterSelection: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Dagging: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Decorate: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.FilteredClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.GridSearch: returns the options of the current setup
getOptions() - Method in class weka.classifiers.meta.LogitBoost: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MetaCost: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiBoostAB: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiScheme: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RandomSubSpace: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RegressionByDiscretization: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RotationForest: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Stacking: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Vote: Gets the current settings of Vote.
getOptions() - Method in class weka.classifiers.mi.CitationKNN: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.mi.MDD: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MIBoost: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MIDD: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MIEMDD: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MILR: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MINND: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.mi.MIOptimalBall: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MISMO: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MISVM: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.mi.MIWrapper: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.mi.SimpleMI: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.misc.SerializedClassifier: returns the options of the current setup
getOptions() - Method in class weka.classifiers.misc.VFI: Gets the current settings of VFI
getOptions() - Method in class weka.classifiers.MultipleClassifiersCombiner: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.RandomizableClassifier: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.ConjunctiveRule: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.DecisionTable: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.DTNB: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.JRip: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.NNge: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.rules.OneR: Gets the current settings of the OneR classifier.
getOptions() - Method in class weka.classifiers.rules.PART: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.Ridor: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.SingleClassifierEnhancer: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.ADTree: Gets the current settings of ADTree.
getOptions() - Method in class weka.classifiers.trees.BFTree: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.FT: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.J48: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.J48graft: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.LADTree: Gets the current settings of ADTree.
getOptions() - Method in class weka.classifiers.trees.LMT: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.m5.M5Base: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.M5P: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.RandomForest: Gets the current settings of the forest.
getOptions() - Method in class weka.classifiers.trees.RandomTree: Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.REPTree: Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.SimpleCart: Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.CheckClusterer: Gets the current settings of the CheckClusterer.
getOptions() - Method in class weka.clusterers.CLOPE: Gets the current settings of CLOPE
getOptions() - Method in class weka.clusterers.Cobweb: Gets the current settings of Cobweb.
getOptions() - Method in class weka.clusterers.DBSCAN: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.clusterers.EM: Gets the current settings of EM.
getOptions() - Method in class weka.clusterers.FarthestFirst: Gets the current settings of FarthestFirst
getOptions() - Method in class weka.clusterers.FilteredClusterer: Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.HierarchicalClusterer: Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.MakeDensityBasedClusterer: Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.OPTICS: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.clusterers.RandomizableClusterer: Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.RandomizableDensityBasedClusterer: Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer: Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.sIB: Gets the current settings.
getOptions() - Method in class weka.clusterers.SimpleKMeans: Gets the current settings of SimpleKMeans
getOptions() - Method in class weka.clusterers.SingleClustererEnhancer: Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.XMeans: Gets the current settings of SimpleKMeans.
getOptions() - Method in class weka.core.Check: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.core.CheckGOE: Gets the current settings of the object.
getOptions() - Method in class weka.core.CheckOptionHandler: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.core.CheckScheme: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.core.converters.AbstractFileSaver: Gets the current settings of the Saver object.
getOptions() - Method in class weka.core.converters.ArffSaver: returns the options of the current setup
getOptions() - Method in class weka.core.converters.C45Saver: Gets the current settings of the C45Saver object.
getOptions() - Method in class weka.core.converters.CSVLoader: Gets the current settings of the Classifier.
getOptions() - Method in class weka.core.converters.DatabaseLoader: Gets the setting
getOptions() - Method in class weka.core.converters.DatabaseSaver: Gets the setting.
getOptions() - Method in class weka.core.converters.LibSVMSaver: returns the options of the current setup
getOptions() - Method in class weka.core.converters.SVMLightSaver: returns the options of the current setup.
getOptions() - Method in class weka.core.converters.TextDirectoryLoader: Gets the setting
getOptions() - Method in class weka.core.converters.XRFFSaver: returns the options of the current setup
getOptions() - Method in class weka.core.FindWithCapabilities: Gets the current settings of this object.
getOptions() - Method in class weka.core.Javadoc: Gets the current settings of this object.
getOptions() - Method in class weka.core.ListOptions: Gets the current settings of this object.
getOptions() - Method in class weka.core.neighboursearch.BallTree: Gets the current settings of KDtree.
getOptions() - Method in class weka.core.neighboursearch.balltrees.BallSplitter: Gets the current settings of the object.
getOptions() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Gets the current settings.
getOptions() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Gets the current settings of the object.
getOptions() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Gets the current settings.
getOptions() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Gets the current settings of this BallTree MiddleOutConstructor.
getOptions() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Gets the current settings of KDtree.
getOptions() - Method in class weka.core.neighboursearch.CoverTree: Gets the current settings of KDtree.
getOptions() - Method in class weka.core.neighboursearch.KDTree: Gets the current settings of KDtree.
getOptions() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Gets the current settings of the object.
getOptions() - Method in class weka.core.neighboursearch.LinearNNSearch: Gets the current settings.
getOptions() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Gets the current settings.
getOptions() - Method in class weka.core.NormalizableDistance: Gets the current settings.
getOptions() - Method in interface weka.core.OptionHandler: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.core.OptionHandlerJavadoc: Gets the current settings of this object.
getOptions() - Method in class weka.core.stemmers.SnowballStemmer: Gets the current settings of the classifier.
getOptions() - Method in class weka.core.TechnicalInformationHandlerJavadoc: Gets the current settings of this object.
getOptions() - Method in class weka.core.TestInstances: Gets the current settings of this object.
getOptions() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.core.tokenizers.NGramTokenizer: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.core.tokenizers.Tokenizer: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.datagenerators.ClassificationGenerator: Gets the current settings of the classifier.
getOptions() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.classifiers.classification.LED24: Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the current settings of the datagenerator RDG1.
getOptions() - Method in class weka.datagenerators.classifiers.regression.Expression: Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.ClusterDefinition: Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Gets the current settings of the datagenerator.
getOptions() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.ClusterGenerator: Gets the current settings of the classifier.
getOptions() - Method in class weka.datagenerators.DataGenerator: Gets the current settings of the datagenerator RDG1.
getOptions() - Method in class weka.datagenerators.RegressionGenerator: Gets the current settings of the classifier.
getOptions() - Method in class weka.estimators.CheckEstimator: Gets the current settings of the CheckEstimator.
getOptions() - Method in class weka.estimators.Estimator: Gets the current settings of the Estimator.
getOptions() - Method in class weka.experiment.AveragingResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CrossValidationResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.CSVResultListener: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.DatabaseResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.Experiment: Gets the current settings of the experiment iterator.
getOptions() - Method in class weka.experiment.InstanceQuery: Gets the current settings of InstanceQuery
getOptions() - Method in class weka.experiment.LearningRateResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.PairedTTester: Gets current settings of the PairedTTester.
getOptions() - Method in class weka.experiment.RandomSplitResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.filters.CheckSource: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.MultiFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.SimpleFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.AddClassification: Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection: Gets the current settings for the attribute selection (search, evaluator) etc.
getOptions() - Method in class weka.filters.supervised.attribute.ClassOrder: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.Discretize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.PLSFilter: returns the options of the current setup
getOptions() - Method in class weka.filters.supervised.instance.Resample: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.SMOTE: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Add: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddID: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddValues: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.ClassAssigner: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Copy: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MathExpression: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NominalToString: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Normalize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Gets the current settings of the classifier.
getOptions() - Method in class weka.filters.unsupervised.attribute.Remove: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Reorder: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Wavelet: returns the options of the current setup
getOptions() - Method in class weka.filters.unsupervised.instance.Normalize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Randomize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Resample: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Gets the current settings of the filter.
getOptions() - Method in class weka.gui.Main: returns the options of the current setup.
getOptype() - Method in class weka.core.pmml.Expression: Get the optype of the result of applying this Expression.
getOptype() - Method in class weka.core.pmml.FieldMetaInfo: Get the optype.
getOrder() - Method in enum weka.core.logging.Logger.Level: Returns the order of this level.
getOrderedFlag() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the ordered flag (option O).
getOriginalCoords() - Method in class weka.gui.beans.MetaBean: returns the vector containing the original coordinates (instances of class Point) for the inputs
getOtherCapabilities() - Method in class weka.core.Capabilities: returns all other capabilities, besides class and attribute related ones
getOtherLeaf() - Method in class weka.classifiers.trees.j48.GraftSplit
getOutlierFactor() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Gets the factor for determining the thresholds for outliers.
getOutlierTreatmentMethod() - Method in class weka.core.pmml.MiningFieldMetaInfo: Get the outlier treatment method used for this field.
getOutput() - Method in class weka.datagenerators.DataGenerator: Gets the print writer.
getOutput() - Method in class weka.gui.explorer.DataGeneratorPanel: returns the generated output as text
getOutputCenterFile() - Method in class weka.clusterers.XMeans: Gets the file to write the list of centers to.
getOutputClassification() - Method in class weka.filters.supervised.attribute.AddClassification: Get whether the classifiction of the classifier is output.
getOutputDef() - Method in class weka.core.pmml.BuiltInArithmetic: Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.BuiltInMath: Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.BuiltInString: Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.DefineFunction: Get the structure of the result produced by this function.
getOutputDef() - Method in class weka.core.pmml.FieldRef: Return the structure of the result of applying this Expression as an Attribute.
getOutputDef() - Method in class weka.core.pmml.Function: Get the structure of the result produced by this function.
getOutputDistribution() - Method in class weka.filters.supervised.attribute.AddClassification: Get whether the classifiction of the classifier is output.
getOutputErrorFlag() - Method in class weka.filters.supervised.attribute.AddClassification: Get whether the classifiction of the classifier is output.
getOutputFile() - Method in class weka.experiment.CrossValidationResultProducer: Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.CSVResultListener: Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.RandomSplitResultProducer: Get the value of OutputFile.
getOutputFilename() - Method in class weka.core.converters.TextDirectoryLoader: Gets whether the filename will be stored as an extra attribute.
getOutputFilename() - Method in class weka.gui.GenericPropertiesCreator: returns the name of the output file
getOutputFormat() - Method in class weka.core.Debug.Clock: returns the output format
getOutputFormat() - Method in class weka.filters.Filter: Gets the format of the output instances.
getOutputFormat() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Gets the format of the output instances.
getOutputFormat() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the classname of the ResultMatrix class, responsible for the output format
getOutputItemSets() - Method in class weka.associations.Apriori: Gets whether itemsets are output as well
getOutputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the output numbers.
getOutputOffsetMultiplier() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Gets whether an additional attribute "Offset" is generated per Outlier/ExtremeValue attribute pair that lists the multiplier the value is off the median: value = median + 'multiplier' * IQR.
getOutputPerClassInfoRetrievalStats() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Get whether per-class information retrieval stats are to be output.
getOutputProperties() - Method in class weka.gui.GenericPropertiesCreator: returns the output properties object (structure like the template, but filled with classes instead of packages)
getOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the outputs.
getOutputs() - Method in class weka.gui.beans.MetaBean
getOutputTypes() - Method in class weka.core.Debug.DBO: Gets the current output type selection
getOutputWordCounts() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether output instances contain 0 or 1 indicating word presence, or word counts.
getOverwriteWarning() - Method in class weka.gui.ConverterFileChooser: Returns whether a popup appears with a warning that the file already exists (only save dialog).
getOwner() - Method in class weka.core.Capabilities: returns the owner of this capabilities object
getOwner() - Static method in class weka.core.Copyright: returns the entity owning the copyright
getP() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the proportion of instances that are common between two training sets.
getPackage(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: returns the packages part of the partial classname.
getPadding() - Method in class weka.filters.unsupervised.attribute.Wavelet: Gets the type of Padding to use
getPaint() - Method in class weka.gui.visualize.PostscriptGraphics
getPanel(int) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the specified panel, null if index is out of bounds
getPanelCount() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the number of panels currently open
getPanels() - Method in class weka.gui.explorer.Explorer: returns all the panels, apart from the PreprocessPanel
getParameterNames() - Method in class weka.core.pmml.BuiltInArithmetic: Returns an array of the names of the parameters expected as input by this function
getParameterNames() - Method in class weka.core.pmml.BuiltInMath: Returns an array of the names of the parameters expected as input by this function.
getParameterNames() - Method in class weka.core.pmml.BuiltInString: Returns an array of the names of the parameters expected as input by this function.
getParameterNames() - Method in class weka.core.pmml.DefineFunction: Returns an array of the names of the parameters expected as input by this function.
getParameterNames() - Method in class weka.core.pmml.Function: Returns an array of the names of the parameters expected as input by this function.
getParent(int, int) - Method in class weka.classifiers.bayes.BayesNet: get node index of a parent of a node in the network structure
getParent(int) - Method in class weka.classifiers.bayes.net.ParentSet: returns index parent of parent specified by index
getParent() - Method in class weka.datagenerators.ClusterDefinition: returns the parent datagenerator this cluster belongs to
getParent(int) - Method in class weka.gui.treevisualizer.Node: Get the parent edge.
getParentCardinality(int) - Method in class weka.classifiers.bayes.BayesNet: get number of values the collection of parents of a node can take
getParentDialog(Container) - Static method in class weka.gui.PropertyDialog: Tries to determine the dialog this panel is part of.
getParentFrame() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the parent frame, if it's a JFrame, otherwise null
getParentFrame() - Method in class weka.gui.GUIChooser.ChildFrameSDI: returns the parent frame, can be null.
getParentFrame() - Method in class weka.gui.Main.ChildFrameMDI: returns the parent frame, can be null.
getParentFrame() - Method in class weka.gui.Main.ChildFrameSDI: returns the parent frame, can be null.
getParentFrame(Container) - Static method in class weka.gui.PropertyDialog: Tries to determine the frame this panel is part of.
getParentFrame() - Method in class weka.gui.SetInstancesPanel: Returns the current frame the panel knows of, that it resides in.
getParentInternalFrame() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the parent frame, if it's a JInternalFrame, otherwise null
getParents() - Method in class weka.classifiers.bayes.net.ParentSet
getParentSet(int) - Method in class weka.classifiers.bayes.BayesNet: get the parent set of a node
getParentSets() - Method in class weka.classifiers.bayes.BayesNet: Get full set of parent sets.
getParts() - Method in class weka.associations.tertius.IndividualInstance
getPassword() - Method in class weka.core.converters.DatabaseLoader: Returns the database password
getPassword() - Method in class weka.core.converters.DatabaseSaver: Returns the database password.
getPassword() - Method in class weka.experiment.DatabaseUtils: Get the database password.
getPassword() - Method in class weka.gui.DatabaseConnectionDialog: Returns password from dialog
getPassword() - Method in class weka.gui.sql.ConnectionPanel: returns the current Password.
getPassword() - Method in class weka.gui.sql.event.ResultChangedEvent: returns the password that produced the table model
getPassword() - Method in class weka.gui.sql.ResultSetTable: returns the password that produced the table model
getPassword() - Method in class weka.gui.sql.SqlViewer: returns the password from the currently active tab in the ResultPanel, otherwise an empty string.
getPassword() - Method in class weka.gui.sql.SqlViewerDialog: returns the chosen password, if any
getPath() - Method in class weka.gui.PropertySelectorDialog: Gets the path of property nodes to the selected property.
getPattern() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the pattern type.
getPenalty() - Method in class weka.classifiers.bayes.blr.Prior
getPercent() - Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the size of noise data as a percentage of the original set.
getPercent() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the percent the attributes (dimensions) of the data will be reduced to
getPercentage() - Method in class weka.filters.supervised.instance.SMOTE: Gets the percentage of SMOTE instances to create.
getPercentage() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets the percentage of instances to select.
getPercentCompleted() - Method in class weka.gui.boundaryvisualizer.RemoteResult: Return the progress for this row
getPercentThreshold() - Method in class weka.attributeSelection.SVMAttributeEval: Get the threshold below which percentage elimination reverts to constant elimination.
getPercentToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval: Get the percentage rate of attribute elimination per iteration
getPerformanceStats() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Gets the class object that contains the performance statistics of the search method.
getPerformPrediction() - Method in class weka.filters.supervised.attribute.PLSFilter: Gets whether the class attribute is updated with the predicted value.
getPerformRanking() - Method in class weka.attributeSelection.LinearForwardSelection: Get boolean if initial ranking should be performed to select the top-ranked attributes
getPerformRanking() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Get boolean if initial ranking should be performed to select the top-ranked attributes
getPeriodicPruning() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets the rate at which the dictionary is periodically pruned, as a percentage of the dataset size.
getPerturbationFraction() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Gets the perturbation fraction.
getPivot() - Method in class weka.core.matrix.LUDecomposition: Return pivot permutation vector
getPivot() - Method in class weka.core.neighboursearch.balltrees.BallNode: Returns the pivot/centre of the node's ball.
getPlainColumnName(int) - Method in class weka.gui.arffviewer.ArffTable: returns the basically the attribute name of the column and not the HTML column name via getColumnName(int)
getPlotInstances() - Method in class weka.gui.visualize.PlotData2D: Returns the instances for this plot
getPlotName() - Method in class weka.gui.visualize.PlotData2D: Get the name of this plot
getPlotNameHTML() - Method in class weka.gui.visualize.PlotData2D: Get the name of the plot for use in a tool tip text.
getPlotPanel() - Method in class weka.gui.visualize.VisualizePanel: Returns the underlying plot panel.
getPlots() - Method in class weka.gui.visualize.Plot2D: Return the list of plots
getPlotTrainingData() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Returns true if training data is to be superimposed
getPMMLModel(String) - Static method in class weka.core.pmml.PMMLFactory: Read and return a PMML model.
getPMMLModel(File) - Static method in class weka.core.pmml.PMMLFactory: Read and return a PMML model.
getPMMLModel(InputStream) - Static method in class weka.core.pmml.PMMLFactory: Read and return a PMML model.
getPMMLModel(String, Logger) - Static method in class weka.core.pmml.PMMLFactory: Read and return a PMML model.
getPMMLModel(File, Logger) - Static method in class weka.core.pmml.PMMLFactory: Read and return a PMML model.
getPMMLModel(InputStream, Logger) - Static method in class weka.core.pmml.PMMLFactory: Read and return a PMML model.
getPMMLVersion() - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Get the PMML version used for this model.
getPMMLVersion() - Method in interface weka.core.pmml.PMMLModel: Get the version of PMML used to encode this model.
getPointValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Gets a particular point value
getPopulationSize() - Method in class weka.attributeSelection.GeneticSearch: get the size of the population
getPopulationSize() - Method in class weka.attributeSelection.ScatterSearchV1: Get the population size
getPopulationSize() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getPopulationSize() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getPopup() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog: returns the currently set JPopupMenu.
getPositionX(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: get x position of a node
getPositionY(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: get y position of a node
getPositiveIndex() - Method in class weka.associations.FPGrowth: Get the index of the attribute value to consider as positive for binary attributes in normal dense instances.
getPostFixExpression() - Method in class weka.core.AttributeExpression: Return the postfix expression
getPostProcessor() - Method in class weka.core.CheckScheme: returns the current PostProcessor, can be null
getPostProcessor() - Method in class weka.estimators.CheckEstimator: returns the current PostProcessor, can be null
getPrecision() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the precision.
getPrecision() - Method in class weka.estimators.KernelEstimator: Return the precision of this kernel estimator.
getPrecision() - Method in class weka.estimators.NormalEstimator: Return the value of the precision of this normal estimator.
getPredicate() - Method in class weka.associations.tertius.Literal
getPrediction(Classifier, Instance) - Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a single prediction for a test instance given the pre-trained classifier.
getPredTargetColumn() - Method in class weka.experiment.ClassifierSplitEvaluator
getPreferredScrollableViewportSize() - Method in class weka.gui.AttributeSelectionPanel
getPrefix() - Method in class weka.gui.beans.SerializedModelSaver: Get the prefix to prepend to the model file names.
getPremise() - Method in class weka.associations.FPGrowth.AssociationRule: Get the premise of this rule.
getPremiseSupport() - Method in class weka.associations.FPGrowth.AssociationRule: Get the support for the premise.
getPreprocessing() - Method in class weka.filters.supervised.attribute.PLSFilter: Gets the type of preprocessing to use
getPreprocessing() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Gets the filter used for preprocessing
getPreprocessPanel() - Method in class weka.gui.explorer.Explorer: returns the instance of the PreprocessPanel being used in this instance of the Explorer
getPreserveInstancesOrder() - Method in class weka.clusterers.SimpleKMeans: Gets whether order of instances must be preserved
getPrintColNames() - Method in class weka.experiment.ResultMatrix: returns whether column names or numbers instead are printed
getPrintNewick() - Method in class weka.clusterers.HierarchicalClusterer
getPrintRowNames() - Method in class weka.experiment.ResultMatrix: returns whether row names or numbers instead are printed
getPriorClass() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Get the type of prior to use.
getPriority(int) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue: Returns the priority for the object at the specified index
getPriority() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement: Returns the priority for this object
getPriority(int) - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue: Returns the priority for the object at the specified index
getPriority() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement: Returns the priority for this object
getPriorProbability(String) - Method in class weka.core.pmml.TargetMetaInfo: Get the prior probability for the supplied value.
getProbabilities() - Method in class weka.gui.boundaryvisualizer.RemoteResult: Return the probability distributions for this row in the visualization
getProbability(int, int, int) - Method in class weka.classifiers.bayes.BayesNet: get particular probability of the conditional probability distribtion of a node given its parents.
getProbability(double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Get a probability estimate for a value
getProbability(double, double) - Method in interface weka.estimators.ConditionalEstimator: Get a probability for a value conditional on another value
getProbability(double, double) - Method in class weka.estimators.DDConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.DiscreteEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.DKConditionalEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.DNConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.Estimator: Get a probability estimate for a value.
getProbability(double, double) - Method in class weka.estimators.KDConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.KernelEstimator: Get a probability estimate for a value.
getProbability(double, double) - Method in class weka.estimators.KKConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.MahalanobisEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NDConditionalEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NNConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.NormalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.PoissonEstimator: Get a probability estimate for a value
getProbabilityEstimates() - Method in class weka.classifiers.functions.LibLINEAR: Sets whether to generate probability estimates instead of -1/+1 for classification problems.
getProbabilityEstimates() - Method in class weka.classifiers.functions.LibSVM: Sets whether to generate probability estimates instead of -1/+1 for classification problems.
getProgressBar() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Returns a handle to the progressBar of this LayoutEngine.
getProgressBar() - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method returns the progress bar for the LayoutEngine, which shows the progress of the layout process, if it takes a while to layout the graph
getProjectionFilter() - Method in class weka.classifiers.meta.RotationForest: Gets the filter used to project the data.
getProlog() - Method in class weka.core.OptionHandlerJavadoc: whether "Valid options are..." prolog is included in the Javadoc
getProlog() - Method in class weka.core.TechnicalInformationHandlerJavadoc: whether "Valid options are..." prolog is included in the Javadoc
getProperties() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the associated properties file
getProperties() - Static method in class weka.gui.explorer.ExplorerDefaults: returns the associated properties file.
getProperty() - Method in class weka.core.pmml.FieldMetaInfo.Value
getPropertyArray() - Method in class weka.experiment.Experiment: Gets the array of values to set the custom property to.
getPropertyArrayLength() - Method in class weka.experiment.Experiment: Gets the number of custom iterator values that have been defined for the experiment.
getPropertyArrayValue(int) - Method in class weka.experiment.Experiment: Gets a specified value from the custom property iterator array.
getPropertyDescriptor(Object, PropertyPath.Path) - Static method in class weka.core.PropertyPath: returns the property associated with the given path, null if a problem occurred.
getPropertyDescriptor(Object, String) - Static method in class weka.core.PropertyPath: returns the property associated with the given path
getPropertyDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo: Returns the property descriptors
getPropertyDescriptors() - Method in class weka.gui.beans.ClassValuePickerBeanInfo: Returns the property descriptors
getPropertyDescriptors() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo: Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo: Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.PredictionAppenderBeanInfo: Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo: Get the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo: Get the property descriptors for this bean
getPropertyPath() - Method in class weka.experiment.Experiment: Gets the path of properties taken to get to the custom property to iterate over.
getPruningMethod() - Method in class weka.classifiers.functions.supportVector.StringKernel: Gets the method used for pruning.
getPruningStrategy() - Method in class weka.classifiers.trees.BFTree: Gets the pruning strategy.
getPruningType() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the pruning type
getQ() - Method in class weka.core.matrix.QRDecomposition: Generate and return the (economy-sized) orthogonal factor
getQuality() - Method in class weka.gui.visualize.JPEGWriter: returns the quality the JPEG will be stored in.
getQuery() - Method in class weka.core.converters.DatabaseLoader: Gets the query to execute against the database
getQuery() - Method in class weka.experiment.InstanceQuery: Get the query to execute against the database
getQuery() - Method in class weka.gui.sql.event.QueryExecuteEvent: returns the query that was executed
getQuery() - Method in class weka.gui.sql.event.ResultChangedEvent: returns the query that was executed
getQuery() - Method in class weka.gui.sql.QueryPanel: returns the currently displayed query.
getQuery() - Method in class weka.gui.sql.ResultSetTable: returns the query that produced the table model
getQuery() - Method in class weka.gui.sql.SqlViewer: returns the query from the currently active tab in the ResultPanel, otherwise an empty string.
getQuery() - Method in class weka.gui.sql.SqlViewerDialog: returns the chosen query, if any
getQueryPanel() - Method in class weka.gui.sql.ResultPanel: returns the currently set QueryPanel, can be NULL
getR() - Method in class weka.core.matrix.QRDecomposition: Return the upper triangular factor
getRaceType() - Method in class weka.attributeSelection.RaceSearch: Get the race type
getRadius() - Method in class weka.core.neighboursearch.balltrees.BallNode: Returns the radius of the node's ball.
getRandom() - Method in class weka.datagenerators.DataGenerator: Gets the random generator.
getRandomize() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Gets whether the order of the generated is randomized
getRandomizeData() - Method in class weka.experiment.RandomSplitResultProducer: Get if dataset is to be randomized
getRandomNumberGenerator(long) - Method in class weka.core.Instances: Returns a random number generator.
getRandomOrder() - Method in class weka.classifiers.bayes.net.search.global.K2: Get random order flag
getRandomOrder() - Method in class weka.classifiers.bayes.net.search.local.K2: Get random order flag
getRandomSeed() - Method in class weka.classifiers.functions.LeastMedSq: get the seed for the random number generator
getRandomSeed() - Method in class weka.classifiers.functions.SMO: Get the value of randomSeed.
getRandomSeed() - Method in class weka.classifiers.mi.MISMO: Get the value of randomSeed.
getRandomSeed() - Method in class weka.classifiers.trees.ADTree: Gets random seed for a random walk.
getRandomSeed() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Returns the seed value of random number generator.
getRandomSeed() - Method in class weka.filters.supervised.instance.Resample: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.supervised.instance.SMOTE: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the random seed of the random number generator
getRandomSeed() - Method in class weka.filters.unsupervised.instance.Randomize: Get the random number generator seed value.
getRandomSeed() - Method in class weka.filters.unsupervised.instance.Resample: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Gets the random number seed.
getRandomWidthFactor() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets the multiplier when generating random codes.
getRange(int) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Gets a single Range from the set of available Ranges.
getRangeCorrection() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the confidence range correction mode used.
getRanges() - Method in class weka.core.NormalizableDistance: Method to get the ranges.
getRanges() - Method in class weka.core.Range: Gets the string representing the selected range of values
getRanges() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Gets the list of possible Ranges to choose from.
getRank() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Gets the desired matrix rank (or coverage proportion) for feature-space reduction
getRawOutput() - Method in class weka.experiment.CrossValidationResultProducer: Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.RandomSplitResultProducer: Get if raw split evaluator output is to be saved
getRawResultOutput() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the raw output from the classifier
getRawResultOutput() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Gets the raw output from the classifier
getRawResultOutput() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the raw output from the classifier
getRawResultOutput() - Method in interface weka.experiment.SplitEvaluator: Returns the raw output for the most recent call to getResult.
getReachabilityDistance() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Returns the reachabilityDistance for this dataObject
getReachabilityDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Returns the reachabilityDistance for this dataObject
getReachabilityDistance() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Returns the reachabilityDistance for this dataObject
getReadable() - Method in class weka.core.Tag: Gets the string description of the Tag.
getReader(String, String) - Static method in class weka.gui.Loader: returns a Reader for the given filename and dir, can be NULL if it fails
getReader(String) - Method in class weka.gui.Loader: returns a Reader for the given filename, can be NULL if it fails
getReadIncrementally() - Method in class weka.gui.SetInstancesPanel: Gets whether instances are to be read incrementally or not
getRealEigenvalues() - Method in class weka.core.matrix.EigenvalueDecomposition: Return the real parts of the eigenvalues
getRecall() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the recall.
getReducedErrorPruning() - Method in class weka.classifiers.rules.PART: Get the value of reducedErrorPruning.
getReducedErrorPruning() - Method in class weka.classifiers.trees.J48: Get the value of reducedErrorPruning.
getRefer() - Method in class weka.gui.treevisualizer.Node: Get the value of refer.
getRefreshFreq() - Method in class weka.gui.beans.StripChart: Get the refresh frequency
getRegOptimizer() - Method in class weka.classifiers.functions.SMOreg: returns the learning algorithm
getRegressionTree() - Method in class weka.classifiers.trees.m5.Rule: Get the value of regressionTree.
getRegressionTree() - Method in class weka.classifiers.trees.m5.RuleNode: Get the value of regressionTree.
getRelabel() - Method in class weka.classifiers.trees.J48graft: Get the value of relabelling
getRelation() - Method in class weka.core.TestInstances: returns the current name of the relation
getRelationalClassFormat() - Method in class weka.core.TestInstances: returns the current strcuture of the relational class attribute, can be null
getRelationalFormat(int) - Method in class weka.core.TestInstances: returns the format for the specified relational attribute, can be null
getRelationForTableName() - Method in class weka.core.converters.DatabaseSaver: Gets whether or not the relation name is used as name of the table.
getRelationName() - Method in class weka.datagenerators.DataGenerator: Gets the relation name the dataset should have.
getRelationNameForFilename() - Method in class weka.gui.beans.Saver: Get whether the relation name is the primary part of the filename.
getRemoteHosts() - Method in class weka.experiment.RemoteExperiment: Get the list of remote host names
getRemoveAllMissingCols() - Method in class weka.associations.Apriori: Returns whether columns containing all missing values are to be removed
getRemoveClassColumn() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Get whether the class column is to be removed.
getRemovedPercentage() - Method in class weka.classifiers.meta.RotationForest: Gets the percentage of instances to be removed
getRemoveFilterClassnames() - Static method in class weka.gui.experiment.ExperimenterDefaults: whether the filter classnames in the dataset names are removed by default
getRemoveFilterName() - Method in class weka.experiment.ResultMatrix: returns whether the filter classname is removed from the dataset name
getRemoveFilterName() - Method in class weka.gui.experiment.OutputFormatDialog: returns whether the filter classname is removed from the dataset name.
getRemoveOldClass() - Method in class weka.filters.supervised.attribute.AddClassification: Get whether the old class attribute is removed.
getRemoveUnused() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Gets whether unused attributes (ones that are not covered by any of the ranges) are removed from the output.
getRenderingHint(RenderingHints.Key) - Method in class weka.gui.visualize.PostscriptGraphics
getRenderingHints() - Method in class weka.gui.visualize.PostscriptGraphics
getRepeatLiterals() - Method in class weka.associations.Tertius: Get the value of repeatLiterals.
getRepetitions() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the number of repetitions to use
getReplaceMissing() - Method in class weka.filters.supervised.attribute.PLSFilter: Gets whether missing values are replace.
getReplaceMissingValues() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the current setting for using ReplaceMissingValues filter
getReportFrequency() - Method in class weka.attributeSelection.GeneticSearch: get how often repports are generated
getRepulsion() - Method in class weka.clusterers.CLOPE: gets the repulsion
getReset() - Method in class weka.classifiers.functions.MultilayerPerceptron
getReset() - Method in class weka.gui.beans.ChartEvent: get the value of the reset flag
getResult() - Method in class weka.core.mathematicalexpression.Parser: Returns the result of the evaluation.
getResult(double[]) - Method in class weka.core.pmml.BuiltInArithmetic: Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.BuiltInMath: Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.BuiltInString: Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.Constant: Get the result of evaluating the expression.
getResult(double[]) - Method in class weka.core.pmml.DefineFunction: Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.Discretize: Get the result of evaluating the expression.
getResult(double[]) - Method in class weka.core.pmml.Expression: Get the result of evaluating the expression.
getResult(double[]) - Method in class weka.core.pmml.FieldRef
getResult(double[]) - Method in class weka.core.pmml.Function: Get the result of applying this function.
getResult(double[]) - Method in class weka.core.pmml.NormContinuous: Get the result of evaluating the expression.
getResult(double[]) - Method in class weka.core.pmml.NormDiscrete: Get the result of evaluating the expression.
getResult(Instances, Instances) - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.RegressionSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in interface weka.experiment.SplitEvaluator: Gets the results for the supplied train and test datasets.
getResult() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Returns the result of the evaluation.
getResult() - Method in class weka.gui.experiment.OutputFormatDialog: the result from the last display of the dialog, the same is returned from showDialog.
getResultCategorical(double[]) - Method in class weka.core.pmml.Constant: Gets the result of evaluating the expression when the optype is categorical or ordinal as the actual String value.
getResultCategorical(double[]) - Method in class weka.core.pmml.Discretize: Gets the result of evaluating the expression when the optype is categorical or ordinal as the actual String value.
getResultCategorical(double[]) - Method in class weka.core.pmml.Expression: Gets the result of evaluating the expression when the optype is categorical or ordinal as the actual String value.
getResultCategorical(double[]) - Method in class weka.core.pmml.FieldRef
getResultCategorical(double[]) - Method in class weka.core.pmml.NormContinuous: Always throws an Exception since the result of NormContinuous must be continuous.
getResultCategorical(double[]) - Method in class weka.core.pmml.NormDiscrete: Always throws an Exception since the result of NormDiscrete must be continuous.
getResultContinuous(double[]) - Method in class weka.core.pmml.Expression: Get the result of evaluating the expression for continuous optype.
getResultFromTable(String, ResultProducer, Object[]) - Method in class weka.experiment.DatabaseUtils: Executes a database query to extract a result for the supplied key from the database.
getResultInverse(double[]) - Method in class weka.core.pmml.NormContinuous: Compute the inverse of the normalization (i.e.
getResultListener() - Method in class weka.experiment.Experiment: Gets the result listener where results will be sent.
getResultMatrix() - Method in class weka.experiment.PairedTTester: Gets the instance that produces the output.
getResultMatrix() - Method in interface weka.experiment.Tester: Gets the instance that produces the output.
getResultMatrix() - Method in class weka.gui.experiment.OutputFormatDialog: Gets the currently selected output format result matrix.
getResultNames() - Method in class weka.experiment.AveragingResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.CrossValidationResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.DatabaseResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.LearningRateResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.RandomSplitResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in interface weka.experiment.ResultProducer: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in interface weka.experiment.SplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultProducer() - Method in class weka.experiment.AveragingResultProducer: Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.DatabaseResultProducer: Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.Experiment: Get the result producer used for the current experiment.
getResultProducer() - Method in class weka.experiment.LearningRateResultProducer: Get the ResultProducer.
getResults() - Method in class weka.associations.Tertius: returns the results
getResultSet() - Method in class weka.experiment.DatabaseUtils: Gets the results generated by a previous query.
getResultSet() - Method in class weka.gui.sql.event.QueryExecuteEvent: returns the resultset that was produced, can be null in case the query failed
getResultSet() - Method in class weka.gui.sql.ResultSetHelper: the underlying resultset.
getResultsetKeyColumns() - Method in class weka.experiment.PairedTTester: Get the value of ResultsetKeyColumns.
getResultsetKeyColumns() - Method in interface weka.experiment.Tester: Get the value of ResultsetKeyColumns.
getResultsetName(int) - Method in class weka.experiment.PairedTTester: Gets a string descriptive of the specified resultset.
getResultsetName(int) - Method in interface weka.experiment.Tester: Gets a string descriptive of the specified resultset.
getResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseUtils: Gets the name of the experiment table that stores results from a particular ResultProducer.
getResultTypes() - Method in class weka.experiment.AveragingResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.CrossValidationResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DatabaseResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.LearningRateResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.RandomSplitResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in interface weka.experiment.ResultProducer: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in interface weka.experiment.SplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultVector() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the resultVector
getResultVector() - Method in class weka.clusterers.OPTICS: Returns the resultVector
getReturnValue() - Method in class weka.gui.DatabaseConnectionDialog: Returns which of OK or cancel was clicked from dialog
getReturnValue() - Method in class weka.gui.sql.SqlViewerDialog: returns whether the user clicked OK (JOptionPane.OK_OPTION) or whether he cancelled the dialog (JOptionPane.CANCEL_OPTION)
getRevision() - Method in class weka.associations.AbstractAssociator: Returns the revision string.
getRevision() - Method in class weka.associations.Apriori: Returns the revision string.
getRevision() - Method in class weka.associations.AprioriItemSet: Returns the revision string.
getRevision() - Method in class weka.associations.AssociatorEvaluation: Returns the revision string.
getRevision() - Method in class weka.associations.CaRuleGeneration: Returns the revision string.
getRevision() - Method in class weka.associations.CheckAssociator: Returns the revision string.
getRevision() - Method in class weka.associations.FilteredAssociator: Returns the revision string.
getRevision() - Method in class weka.associations.FPGrowth: Returns the revision string.
getRevision() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the revision string.
getRevision() - Method in class weka.associations.gsp.Element: Returns the revision string.
getRevision() - Method in class weka.associations.gsp.Sequence: Returns the revision string.
getRevision() - Method in class weka.associations.ItemSet: Returns the revision string.
getRevision() - Method in class weka.associations.LabeledItemSet: Returns the revision string.
getRevision() - Method in class weka.associations.PredictiveApriori: Returns the revision string.
getRevision() - Method in class weka.associations.PriorEstimation: Returns the revision string.
getRevision() - Method in class weka.associations.RuleGeneration: Returns the revision string.
getRevision() - Method in class weka.associations.RuleItem: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.AttributeValueLiteral: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.Body: Returns the revision string.
getRevision() - Method in class weka.associations.Tertius: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.Head: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.IndividualInstance: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.IndividualInstances: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.IndividualLiteral: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.Predicate: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.Rule: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.SimpleLinkedList: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator: Returns the revision string.
getRevision() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ASEvaluation: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ASSearch: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.AttributeSelection: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.BestFirst: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.BestFirst.Link2: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.BestFirst.LinkedList2: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CfsSubsetEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CheckAttributeSelection: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ConsistencySubsetEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CostSensitiveAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.CostSensitiveSubsetEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.FilteredAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.FilteredSubsetEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.GeneticSearch: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.GreedyStepwise: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LFSMethods: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LFSMethods.Link2: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LFSMethods.LinkedList2: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.OneRAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.PrincipalComponents: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.RaceSearch: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.RandomSearch: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.Ranker: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.RankSearch: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.ScatterSearchV1: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.SVMAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns the revision string.
getRevision() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.AODE: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.AODEsr: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.BayesNet: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.DMNBtext: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.HNB: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayes: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayesSimple: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.ADNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.BayesNetGenerator: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.BIFReader: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.EditableBayesNet: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.ParentSet: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.fixed.NaiveBayes: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.K2: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.global.TAN: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.K2: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.local.TAN: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.net.VaryNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.bayes.WAODE: Returns the revision string.
getRevision() - Method in class weka.classifiers.BVDecompose: Returns the revision string.
getRevision() - Method in class weka.classifiers.BVDecomposeSegCVSub: Returns the revision string.
getRevision() - Method in class weka.classifiers.CheckClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.CheckSource: Returns the revision string.
getRevision() - Method in class weka.classifiers.Classifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.CostMatrix: Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.CostCurve: Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.EvaluationUtils: Returns the revision string.
getRevision() - Method in class weka.classifiers.Evaluation: Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.MarginCurve: Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.NominalPrediction: Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.NumericPrediction: Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.ThresholdCurve: Returns the revision string.
getRevision() - Method in class weka.classifiers.evaluation.TwoClassStats: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.GaussianProcesses: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.IsotonicRegression: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.LeastMedSq: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.LibLINEAR: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.LibSVM: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.LinearRegression: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.Logistic: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.MultilayerPerceptron: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.neural.LinearUnit: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.neural.NeuralNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.neural.SigmoidUnit: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.pace.NormalMixture: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.PaceRegression: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.PLSClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.RBFNetwork: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SMO.BinarySMO: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SMO: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SMOreg: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.SPegasos: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.CheckKernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.Puk: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.RegSMO: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.SMOset: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the revision string.
getRevision() - Method in class weka.classifiers.functions.Winnow: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.IB1: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.IBk: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.KStar: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.kstar.KStarWrapper: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.LBR: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the revision string.
getRevision() - Method in class weka.classifiers.lazy.LWL: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.AdaBoostM1: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.AdditiveRegression: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Bagging: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.ClassificationViaClustering: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.ClassificationViaRegression: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.CVParameterSelection: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Dagging: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Decorate: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.END: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.FilteredClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Grading: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.GridSearch: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.LogitBoost: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.MetaCost: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.MultiBoostAB: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.MultiClassClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.MultiScheme: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.nestedDichotomies.ND: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RandomCommittee: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RandomSubSpace: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.RotationForest: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Stacking: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.StackingC: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.ThresholdSelector: Returns the revision string.
getRevision() - Method in class weka.classifiers.meta.Vote: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.CitationKNN: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MDD: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIBoost: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIDD: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIEMDD: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MILR: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MINND: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIOptimalBall: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MISMO: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MISVM: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.MIWrapper: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.SimpleMI: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.supportVector.MIPolyKernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.mi.supportVector.MIRBFKernel: Returns the revision string.
getRevision() - Method in class weka.classifiers.misc.HyperPipes: Returns the revision string.
getRevision() - Method in class weka.classifiers.misc.SerializedClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.misc.VFI: Returns the revision string.
getRevision() - Method in class weka.classifiers.pmml.consumer.GeneralRegression
getRevision() - Method in class weka.classifiers.pmml.consumer.NeuralNetwork
getRevision() - Method in class weka.classifiers.pmml.consumer.Regression
getRevision() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.DecisionTable: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.DecisionTableHashKey: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.DTNB: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip.Antd: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip.NominalAntd: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip.NumericAntd: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.JRip.RipperRule: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.M5Rules: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.NNge: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.OneR: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.part.C45PruneableDecList: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.part.ClassifierDecList: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.PART: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.part.MakeDecList: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.part.PruneableDecList: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.Prism: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.Ridor: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.RuleStats: Returns the revision string.
getRevision() - Method in class weka.classifiers.rules.ZeroR: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ADTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.adtree.PredictionNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.adtree.ReferenceInstances: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.BFTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.DecisionStump: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTInnerNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTLeavesNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTtree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.FT: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.Id3: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.BinC45ModelSelection: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.BinC45Split: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.C45ModelSelection: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.C45Split: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.Distribution: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.EntropySplitCrit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.J48: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.GraftSplit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NBTreeModelSelection: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NBTreeSplit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.NoSplit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.j48.Stats: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.J48graft: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.LADTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.LMT: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.lmt.ResidualSplit: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.Impurity: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.Rule: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.RuleNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.Values: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.M5P: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.NBTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.RandomForest: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.REPTree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.SimpleCart: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.UserClassifier: Returns the revision string.
getRevision() - Method in class weka.classifiers.xml.XMLClassifier: Returns the revision string.
getRevision() - Method in class weka.clusterers.AbstractClusterer: Returns the revision string.
getRevision() - Method in class weka.clusterers.CheckClusterer: Returns the revision string.
getRevision() - Method in class weka.clusterers.CLOPE: Returns the revision string.
getRevision() - Method in class weka.clusterers.ClusterEvaluation: Returns the revision string.
getRevision() - Method in class weka.clusterers.Cobweb: Returns the revision string.
getRevision() - Method in class weka.clusterers.DBSCAN: Returns the revision string.
getRevision() - Method in class weka.clusterers.EM: Returns the revision string.
getRevision() - Method in class weka.clusterers.FarthestFirst: Returns the revision string.
getRevision() - Method in class weka.clusterers.FilteredClusterer: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.EpsilonRange_ListElement: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue: Returns the revision string.
getRevision() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement: Returns the revision string.
getRevision() - Method in class weka.clusterers.HierarchicalClusterer: Returns the revision string.
getRevision() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns the revision string.
getRevision() - Method in class weka.clusterers.OPTICS: Returns the revision string.
getRevision() - Method in class weka.clusterers.sIB: Returns the revision string.
getRevision() - Method in class weka.clusterers.SimpleKMeans: Returns the revision string.
getRevision() - Method in class weka.clusterers.XMeans: Returns the revision string.
getRevision() - Method in class weka.core.AlgVector: Returns the revision string.
getRevision() - Method in class weka.core.AllJavadoc: Returns the revision string.
getRevision() - Method in class weka.core.Attribute: Returns the revision string.
getRevision() - Method in class weka.core.AttributeExpression: Returns the revision string.
getRevision() - Method in class weka.core.AttributeLocator: Returns the revision string.
getRevision() - Method in class weka.core.AttributeStats: Returns the revision string.
getRevision() - Method in class weka.core.BinarySparseInstance: Returns the revision string.
getRevision() - Method in class weka.core.Capabilities: Returns the revision string.
getRevision() - Method in class weka.core.ChebyshevDistance: Returns the revision string.
getRevision() - Method in class weka.core.CheckGOE: Returns the revision string.
getRevision() - Method in class weka.core.CheckOptionHandler: Returns the revision string.
getRevision() - Method in class weka.core.CheckScheme.PostProcessor: Returns the revision string.
getRevision() - Method in class weka.core.ClassDiscovery: Returns the revision string.
getRevision() - Method in class weka.core.ClassDiscovery.StringCompare: Returns the revision string.
getRevision() - Method in class weka.core.ClassloaderUtil: Returns the revision string.
getRevision() - Method in class weka.core.ContingencyTables: Returns the revision string.
getRevision() - Method in class weka.core.converters.ArffLoader.ArffReader: Returns the revision string.
getRevision() - Method in class weka.core.converters.ArffLoader: Returns the revision string.
getRevision() - Method in class weka.core.converters.ArffSaver: Returns the revision string.
getRevision() - Method in class weka.core.converters.C45Loader: Returns the revision string.
getRevision() - Method in class weka.core.converters.C45Saver: Returns the revision string.
getRevision() - Method in class weka.core.converters.ConverterUtils.DataSink: Returns the revision string.
getRevision() - Method in class weka.core.converters.ConverterUtils.DataSource: Returns the revision string.
getRevision() - Method in class weka.core.converters.ConverterUtils: Returns the revision string.
getRevision() - Method in class weka.core.converters.CSVLoader: Returns the revision string.
getRevision() - Method in class weka.core.converters.CSVSaver: Returns the revision string.
getRevision() - Method in class weka.core.converters.DatabaseConnection: Returns the revision string.
getRevision() - Method in class weka.core.converters.DatabaseLoader: Returns the revision string.
getRevision() - Method in class weka.core.converters.DatabaseSaver: Returns the revision string.
getRevision() - Method in class weka.core.converters.LibSVMLoader: Returns the revision string.
getRevision() - Method in class weka.core.converters.LibSVMSaver: Returns the revision string.
getRevision() - Method in class weka.core.converters.SerializedInstancesLoader: Returns the revision string.
getRevision() - Method in class weka.core.converters.SerializedInstancesSaver: Returns the revision string.
getRevision() - Method in class weka.core.converters.SVMLightLoader: Returns the revision string.
getRevision() - Method in class weka.core.converters.SVMLightSaver: Returns the revision string.
getRevision() - Method in class weka.core.converters.TextDirectoryLoader: Returns the revision string.
getRevision() - Method in class weka.core.converters.XRFFLoader: Returns the revision string.
getRevision() - Method in class weka.core.converters.XRFFSaver: Returns the revision string.
getRevision() - Method in class weka.core.Debug.Clock: Returns the revision string.
getRevision() - Method in class weka.core.Debug.DBO: Returns the revision string.
getRevision() - Method in class weka.core.Debug: Returns the revision string.
getRevision() - Method in class weka.core.Debug.Log: Returns the revision string.
getRevision() - Method in class weka.core.Debug.Random: Returns the revision string.
getRevision() - Method in class weka.core.Debug.SimpleLog: Returns the revision string.
getRevision() - Method in class weka.core.Debug.Timestamp: Returns the revision string.
getRevision() - Method in class weka.core.EditDistance: Returns the revision string.
getRevision() - Method in class weka.core.Environment: Returns the revision string.
getRevision() - Method in class weka.core.EuclideanDistance: Returns the revision string.
getRevision() - Method in class weka.core.FastVector.FastVectorEnumeration: Returns the revision string.
getRevision() - Method in class weka.core.FastVector: Returns the revision string.
getRevision() - Method in class weka.core.FindWithCapabilities: Returns the revision string.
getRevision() - Method in class weka.core.GlobalInfoJavadoc: Returns the revision string.
getRevision() - Method in class weka.core.Instance: Returns the revision string.
getRevision() - Method in class weka.core.InstanceComparator: Returns the revision string.
getRevision() - Method in class weka.core.Instances: Returns the revision string.
getRevision() - Method in class weka.core.Jython: Returns the revision string.
getRevision() - Method in class weka.core.ListOptions: Returns the revision string.
getRevision() - Method in class weka.core.logging.ConsoleLogger: Returns the revision string.
getRevision() - Method in class weka.core.logging.FileLogger: Returns the revision string.
getRevision() - Method in class weka.core.logging.OutputLogger: Returns the revision string.
getRevision() - Method in class weka.core.ManhattanDistance: Returns the revision string.
getRevision() - Method in class weka.core.MathematicalExpression: Returns the revision string.
getRevision() - Method in class weka.core.matrix.CholeskyDecomposition: Returns the revision string.
getRevision() - Method in class weka.core.matrix.DoubleVector: Returns the revision string.
getRevision() - Method in class weka.core.matrix.EigenvalueDecomposition: Returns the revision string.
getRevision() - Method in class weka.core.matrix.ExponentialFormat: Returns the revision string.
getRevision() - Method in class weka.core.matrix.FlexibleDecimalFormat: Returns the revision string.
getRevision() - Method in class weka.core.matrix.FloatingPointFormat: Returns the revision string.
getRevision() - Method in class weka.core.Matrix: Deprecated.

Returns the revision string.
getRevision() - Method in class weka.core.matrix.IntVector: Returns the revision string.
getRevision() - Method in class weka.core.matrix.LinearRegression: Returns the revision string.
getRevision() - Method in class weka.core.matrix.LUDecomposition: Returns the revision string.
getRevision() - Method in class weka.core.matrix.Maths: Returns the revision string.
getRevision() - Method in class weka.core.matrix.Matrix: Returns the revision string.
getRevision() - Method in class weka.core.matrix.QRDecomposition: Returns the revision string.
getRevision() - Method in class weka.core.matrix.SingularValueDecomposition: Returns the revision string.
getRevision() - Method in class weka.core.Memory: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.BallTree: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BallNode: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BallSplitter: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.CoverTree.CoverTreeNode: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.CoverTree: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.covertrees.Stack: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.KDTree: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.LinearNNSearch: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the revision string.
getRevision() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the revision string.
getRevision() - Method in class weka.core.Option: Returns the revision string.
getRevision() - Method in class weka.core.OptionHandlerJavadoc: Returns the revision string.
getRevision() - Method in class weka.core.PropertyPath: Returns the revision string.
getRevision() - Method in class weka.core.PropertyPath.Path: Returns the revision string.
getRevision() - Method in class weka.core.PropertyPath.PathElement: Returns the revision string.
getRevision() - Method in class weka.core.ProtectedProperties: Returns the revision string.
getRevision() - Method in class weka.core.Queue: Returns the revision string.
getRevision() - Method in class weka.core.RandomVariates: Returns the revision string.
getRevision() - Method in class weka.core.Range: Returns the revision string.
getRevision() - Method in class weka.core.RelationalLocator: Returns the revision string.
getRevision() - Method in interface weka.core.RevisionHandler: Returns the revision string.
getRevision() - Method in class weka.core.SelectedTag: Returns the revision string.
getRevision() - Method in class weka.core.SerializationHelper: Returns the revision string.
getRevision() - Method in class weka.core.SerializedObject: Returns the revision string.
getRevision() - Method in class weka.core.SingleIndex: Returns the revision string.
getRevision() - Method in class weka.core.SparseInstance: Returns the revision string.
getRevision() - Method in class weka.core.SpecialFunctions: Returns the revision string.
getRevision() - Method in class weka.core.Statistics: Returns the revision string.
getRevision() - Method in class weka.core.stemmers.IteratedLovinsStemmer: Returns the revision string.
getRevision() - Method in class weka.core.stemmers.LovinsStemmer: Returns the revision string.
getRevision() - Method in class weka.core.stemmers.NullStemmer: Returns the revision string.
getRevision() - Method in class weka.core.stemmers.SnowballStemmer: Returns the revision string.
getRevision() - Method in class weka.core.stemmers.Stemming: Returns the revision string.
getRevision() - Method in class weka.core.Stopwords: Returns the revision string.
getRevision() - Method in class weka.core.StringLocator: Returns the revision string.
getRevision() - Method in class weka.core.SystemInfo: Returns the revision string.
getRevision() - Method in class weka.core.Tag: Returns the revision string.
getRevision() - Method in class weka.core.TechnicalInformation: Returns the revision string.
getRevision() - Method in class weka.core.TechnicalInformationHandlerJavadoc: Returns the revision string.
getRevision() - Method in class weka.core.Tee: Returns the revision string.
getRevision() - Method in class weka.core.TestInstances: Returns the revision string.
getRevision() - Method in class weka.core.tokenizers.AlphabeticTokenizer: Returns the revision string.
getRevision() - Method in class weka.core.tokenizers.NGramTokenizer: Returns the revision string.
getRevision() - Method in class weka.core.tokenizers.WordTokenizer: Returns the revision string.
getRevision() - Method in class weka.core.Trie: Returns the revision string.
getRevision() - Method in class weka.core.Trie.TrieIterator: Returns the revision string.
getRevision() - Method in class weka.core.Trie.TrieNode: Returns the revision string.
getRevision() - Method in class weka.core.Utils: Returns the revision string.
getRevision() - Method in class weka.core.Version: Returns the revision string.
getRevision() - Method in class weka.core.xml.KOML: Returns the revision string.
getRevision() - Method in class weka.core.xml.MethodHandler: Returns the revision string.
getRevision() - Method in class weka.core.xml.PropertyHandler: Returns the revision string.
getRevision() - Method in class weka.core.xml.SerialUIDChanger: Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLBasicSerialization: Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLDocument: Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLInstances: Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLOptions: Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLSerialization: Returns the revision string.
getRevision() - Method in class weka.core.xml.XMLSerializationMethodHandler: Returns the revision string.
getRevision() - Method in class weka.core.xml.XStream: Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.LED24: Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.regression.Expression: Returns the revision string.
getRevision() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Returns the revision string.
getRevision() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the revision string.
getRevision() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Returns the revision string.
getRevision() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns the revision string.
getRevision() - Method in class weka.datagenerators.Test: Returns the revision string.
getRevision() - Method in class weka.estimators.CheckEstimator.AttrTypes: Returns the revision string.
getRevision() - Method in class weka.estimators.CheckEstimator.EstTypes: Returns the revision string.
getRevision() - Method in class weka.estimators.CheckEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.CheckEstimator.PostProcessor: Returns the revision string.
getRevision() - Method in class weka.estimators.DDConditionalEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.DiscreteEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.DKConditionalEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.DNConditionalEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.EstimatorUtils: Returns the revision string.
getRevision() - Method in class weka.estimators.KDConditionalEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.KernelEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.KKConditionalEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.MahalanobisEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.NDConditionalEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.NNConditionalEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.NormalEstimator: Returns the revision string.
getRevision() - Method in class weka.estimators.PoissonEstimator: Returns the revision string.
getRevision() - Method in class weka.experiment.AveragingResultProducer: Returns the revision string.
getRevision() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns the revision string.
getRevision() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns the revision string.
getRevision() - Method in class weka.experiment.CrossValidationResultProducer: Returns the revision string.
getRevision() - Method in class weka.experiment.CSVResultListener: Returns the revision string.
getRevision() - Method in class weka.experiment.DatabaseResultListener: Returns the revision string.
getRevision() - Method in class weka.experiment.DatabaseResultProducer: Returns the revision string.
getRevision() - Method in class weka.experiment.DatabaseUtils: Returns the revision string.
getRevision() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Returns the revision string.
getRevision() - Method in class weka.experiment.Experiment: Returns the revision string.
getRevision() - Method in class weka.experiment.InstanceQuery: Returns the revision string.
getRevision() - Method in class weka.experiment.InstancesResultListener: Returns the revision string.
getRevision() - Method in class weka.experiment.LearningRateResultProducer: Returns the revision string.
getRevision() - Method in class weka.experiment.OutputZipper: Returns the revision string.
getRevision() - Method in class weka.experiment.PairedCorrectedTTester: Returns the revision string.
getRevision() - Method in class weka.experiment.PairedStats: Returns the revision string.
getRevision() - Method in class weka.experiment.PairedStatsCorrected: Returns the revision string.
getRevision() - Method in class weka.experiment.PairedTTester: Returns the revision string.
getRevision() - Method in class weka.experiment.PropertyNode: Returns the revision string.
getRevision() - Method in class weka.experiment.RandomSplitResultProducer: Returns the revision string.
getRevision() - Method in class weka.experiment.RegressionSplitEvaluator: Returns the revision string.
getRevision() - Method in class weka.experiment.RemoteEngine: Returns the revision string.
getRevision() - Method in class weka.experiment.RemoteExperiment: Returns the revision string.
getRevision() - Method in class weka.experiment.RemoteExperimentSubTask: Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixCSV: Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixGnuPlot: Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixHTML: Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixLatex: Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixPlainText: Returns the revision string.
getRevision() - Method in class weka.experiment.ResultMatrixSignificance: Returns the revision string.
getRevision() - Method in class weka.experiment.Stats: Returns the revision string.
getRevision() - Method in class weka.experiment.TaskStatusInfo: Returns the revision string.
getRevision() - Method in class weka.experiment.xml.XMLExperiment: Returns the revision string.
getRevision() - Method in class weka.filters.AllFilter: Returns the revision string.
getRevision() - Method in class weka.filters.CheckSource: Returns the revision string.
getRevision() - Method in class weka.filters.Filter: Returns the revision string.
getRevision() - Method in class weka.filters.MultiFilter: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.AddClassification: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.Discretize: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.instance.Resample: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.instance.SMOTE: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns the revision string.
getRevision() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Add: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddID: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.AddValues: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Center: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ClassAssigner: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Copy: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.MathExpression: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NominalToString: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Obfuscate: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Remove: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Reorder: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Standardize: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.SwapValues: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.attribute.Wavelet: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.Normalize: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.Randomize: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.Resample: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse: Returns the revision string.
getRevision() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Returns the revision string.
getRevision() - Method in class weka.gui.beans.FlowRunner
getRevision() - Method in class weka.gui.sql.DbUtils: Returns the revision string.
getRidge() - Method in class weka.classifiers.functions.LinearRegression: Get the value of Ridge.
getRidge() - Method in class weka.classifiers.functions.Logistic: Gets the ridge in the log-likelihood.
getRidge() - Method in class weka.classifiers.functions.RBFNetwork: Gets the ridge value.
getRidge() - Method in class weka.classifiers.mi.MILR: Gets the ridge in the log-likelihood.
getRocAnalysis() - Method in class weka.associations.Tertius: Get the value of rocAnalysis.
getROCArea(Instances) - Static method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the area under the ROC curve as the Wilcoxon-Mann-Whitney statistic.
getROCString() - Method in class weka.gui.visualize.ThresholdVisualizePanel: This extracts the ROC area string
getRoot() - Method in class weka.core.Trie: returns the root node of the trie
getRoot() - Method in class weka.gui.treevisualizer.Node: Get the value of root.
getRootNode() - Method in class weka.core.xml.XMLDocument: returns the current root node.
getRow(int) - Method in class weka.core.Matrix: Deprecated.

Gets a row of the matrix and returns it as double array.
getRow() - Static method in class weka.gui.experiment.ExperimenterDefaults: the comma-separated list of attribute names that identify a row
getRowCount() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel: Returns the number of rows of this model.
getRowCount() - Method in class weka.experiment.ResultMatrix: returns the number of rows
getRowCount() - Method in class weka.gui.arffviewer.ArffTableModel: returns the number of rows in the model
getRowCount() - Method in class weka.gui.SortedTableModel: Returns the number of rows in the model.
getRowCount() - Method in class weka.gui.sql.ResultSetHelper: returns the number of rows in the resultset.
getRowCount() - Method in class weka.gui.sql.ResultSetTableModel: returns the number of rows in the model.
getRowDimension() - Method in class weka.core.matrix.Matrix: Get row dimension.
getRowHidden(int) - Method in class weka.experiment.ResultMatrix: returns the hidden status of the row, if the index is valid, otherwise false
getRowName(int) - Method in class weka.experiment.ResultMatrix: returns the name of the row, if the index is valid, otherwise null.
getRowNameWidth() - Method in class weka.experiment.ResultMatrix: returns the current width for the row names
getRowOrder() - Method in class weka.experiment.ResultMatrix: returns the current order of the rows, null means the default order
getRowPackedCopy() - Method in class weka.core.matrix.Matrix: Make a one-dimensional row packed copy of the internal array.
getRsource() - Method in class weka.gui.treevisualizer.Edge: Get the value of rsource.
getRtarget() - Method in class weka.gui.treevisualizer.Edge: Get the value of rtarget.
getRuleset() - Method in class weka.classifiers.rules.JRip: Get the ruleset generated by Ripper
getRuleset() - Method in class weka.classifiers.rules.RuleStats: Get the ruleset of the stats
getRulesetSize() - Method in class weka.classifiers.rules.RuleStats: Get the size of the ruleset in the stats
getRulesMustContain() - Method in class weka.associations.FPGrowth: Get the comma separated list of items that rules must contain in order to be output.
getRuleStats(int) - Method in class weka.classifiers.rules.JRip: Get the statistics of the ruleset in the given position
getRunColumn() - Method in class weka.experiment.PairedTTester: Get the value of RunColumn.
getRunColumn() - Method in interface weka.experiment.Tester: Get the value of RunColumn.
getRunLower() - Method in class weka.experiment.Experiment: Get the lower run number for the experiment.
getRunNumber() - Method in class weka.gui.beans.BatchClassifierEvent: Get the run number.
getRunNumber() - Method in class weka.gui.beans.TestSetEvent: Get the run number that this training set belongs to.
getRunNumber() - Method in class weka.gui.beans.TrainingSetEvent: Get the run number that this training set belongs to.
getRuns() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getRuns() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Returns the number of runs
getRuns() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
getRuns() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
getRuns() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getRuns() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
getRuns() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
getRuns() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
getRunUpper() - Method in class weka.experiment.Experiment: Get the upper run number for the experiment.
getS() - Method in class weka.core.matrix.SingularValueDecomposition: Return the diagonal matrix of singular values
getSampleSize() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the number of instances used for estimating attributes
getSampleSize() - Method in class weka.classifiers.functions.LeastMedSq: gets number of samples
getSampleSize() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Gets the subsample size.
getSampleSizePercent() - Method in class weka.classifiers.meta.GridSearch: Gets the sample size for the initial grid search.
getSampleSizePercent() - Method in class weka.filters.supervised.instance.Resample: Gets the subsample size as a percentage of the original set.
getSampleSizePercent() - Method in class weka.filters.unsupervised.instance.Resample: Gets the subsample size as a percentage of the original set.
getSaveDialogTitle() - Method in class weka.gui.visualize.PrintableComponent: returns the title for the save dialog.
getSaveDialogTitle() - Method in interface weka.gui.visualize.PrintableHandler: returns the title for the save dialog
getSaveDialogTitle() - Method in class weka.gui.visualize.PrintablePanel: returns the title for the save dialog
getSaveInstanceData() - Method in class weka.classifiers.trees.ADTree: Gets whether the tree is to save instance data.
getSaveInstanceData() - Method in class weka.classifiers.trees.J48: Check whether instance data is to be saved.
getSaveInstanceData() - Method in class weka.classifiers.trees.J48graft: Check whether instance data is to be saved.
getSaveInstanceData() - Method in class weka.clusterers.Cobweb: Get the value of saveInstances.
getSaveInstances() - Method in class weka.classifiers.trees.M5P: Get whether instance data is being save.
getSaver() - Method in class weka.gui.ConverterFileChooser: returns the saver that was chosen by the user, can be null in case the user aborted the dialog or the open dialog was shown
getSaverForExtension(String) - Static method in class weka.core.converters.ConverterUtils: tries to determine the saver to use for this kind of extension, returns null if none can be found.
getSaverForFile(String) - Static method in class weka.core.converters.ConverterUtils: tries to determine the saver to use for this kind of file, returns null if none can be found.
getSaverForFile(File) - Static method in class weka.core.converters.ConverterUtils: tries to determine the saver to use for this kind of file, returns null if none can be found.
getSaverTemplate() - Method in class weka.gui.beans.Saver: Get the saver
getScale() - Method in class weka.filters.unsupervised.attribute.Normalize: Get the scaling factor.
getScalingEnabled() - Method in class weka.gui.visualize.JComponentWriter: whether scaling is enabled or ignored
getScoreType() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: get quality measure to be used in searching for networks.
getSearch() - Method in class weka.attributeSelection.CheckAttributeSelection: Get the current search method
getSearch() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the search method used
getSearch() - Method in class weka.classifiers.rules.DecisionTable: Gets the current search method
getSearch() - Method in class weka.classifiers.rules.DTNB: Gets the current search method
getSearch() - Method in class weka.filters.supervised.attribute.AttributeSelection: Get the name of the search method
getSearchAlgorithm() - Method in class weka.classifiers.bayes.BayesNet: Get the SearchAlgorithm used as the search algorithm
getSearchBackwards() - Method in class weka.attributeSelection.GreedyStepwise: Get whether to search backwards
getSearchPath() - Method in class weka.classifiers.trees.ADTree: Gets the method of searching the tree for a new insertion.
getSearchPercent() - Method in class weka.attributeSelection.RandomSearch: get the percentage of the search space to consider
getSearchString() - Method in class weka.gui.arffviewer.ArffTable: returns the search string, can be NULL if no search string is set
getSearchTermination() - Method in class weka.attributeSelection.BestFirst: Get the termination criterion (number of non-improving nodes).
getSearchTermination() - Method in class weka.attributeSelection.LinearForwardSelection: Get the termination criterion (number of non-improving nodes).
getSecondValueIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Get the index of the second value used.
getSecondValueIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the second value used.
getSeed() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Gets the seed for the random number generations.
getSeed() - Method in class weka.attributeSelection.GeneticSearch: get the value of the random number generator's seed
getSeed() - Method in class weka.attributeSelection.OneRAttributeEval: Get the random number seed
getSeed() - Method in class weka.attributeSelection.RandomSearch: Get the random seed to use
getSeed() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the seed used for randomly sampling instances.
getSeed() - Method in class weka.attributeSelection.ScatterSearchV1: get the value of the random number generator's seed
getSeed() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Seed for cross validation subset size determination.
getSeed() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the random number seed used for cross validation
getSeed() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Get the seed for randomizing the instances for CV-based hyperparameter selection
getSeed() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getSeed() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Returns the random seed
getSeed() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
getSeed() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getSeed() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
getSeed() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
getSeed() - Method in class weka.classifiers.BVDecompose: Gets the random number seed
getSeed() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets the random number seed
getSeed() - Method in class weka.classifiers.evaluation.EvaluationUtils: Gets the seed for randomization during cross-validation
getSeed() - Method in class weka.classifiers.functions.MultilayerPerceptron
getSeed() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Gets the current seed value for the random number generator
getSeed() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of Seed.
getSeed() - Method in class weka.classifiers.functions.Winnow: Get the value of Seed.
getSeed() - Method in class weka.classifiers.meta.MultiScheme: Gets the random number seed.
getSeed() - Method in class weka.classifiers.RandomizableClassifier: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.rules.ConjunctiveRule: returns the current seed value for randomizing the data
getSeed() - Method in class weka.classifiers.rules.JRip: Gets the current seed value to use in randomizing the data
getSeed() - Method in class weka.classifiers.rules.PART: Get the value of Seed.
getSeed() - Method in class weka.classifiers.rules.Ridor
getSeed() - Method in class weka.classifiers.trees.J48: Get the value of Seed.
getSeed() - Method in class weka.classifiers.trees.RandomForest: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.trees.RandomTree: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.trees.REPTree: Get the value of Seed.
getSeed() - Method in class weka.clusterers.RandomizableClusterer: Gets the seed for the random number generations
getSeed() - Method in class weka.clusterers.RandomizableDensityBasedClusterer: Gets the seed for the random number generations
getSeed() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer: Gets the seed for the random number generations
getSeed() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Returns the seed for random number generator.
getSeed() - Method in interface weka.core.Randomizable: Gets the seed for the random number generations
getSeed() - Method in class weka.core.TestInstances: returns the current seed value
getSeed() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Gets the random number seed.
getSeed() - Method in class weka.datagenerators.DataGenerator: Gets the random number seed.
getSeed() - Method in class weka.filters.supervised.attribute.ClassOrder: Get the current randomization seed
getSeed() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the random number seed used for shuffling the dataset.
getSeed() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Returns the current seed value for randomizing the order of the generated data
getSeed() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Get the seed value for the random number generator.
getSeed() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the random number seed used for shuffling the dataset.
getSeed() - Method in class weka.gui.beans.CrossValidationFoldMaker: Get the currently set seed
getSeed() - Method in class weka.gui.beans.TrainTestSplitMaker: Get the value of the random seed
getSelectedAttributes() - Method in class weka.gui.AttributeSelectionPanel: Gets an array containing the indices of all selected attributes.
getSelectedBuffer() - Method in class weka.gui.ResultHistoryPanel: Gets the buffer associated with the currently selected item in the list.
getSelectedName() - Method in class weka.gui.ResultHistoryPanel: Get the name of the currently selected item in the list
getSelectedObject() - Method in class weka.gui.ResultHistoryPanel: Gets the object associated with the currently selected item in the list.
getSelectedRange() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Gets the current range selection.
getSelectedRange() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the value of m_SelectedRange.
getSelectedTag() - Method in class weka.core.SelectedTag: Gets the selected Tag.
getSelection() - Method in class weka.core.Range: Gets an array containing all the selected values, in the order that they were selected (or ascending order if range inversion is on)
getSelectionModel() - Method in class weka.gui.AttributeListPanel: Gets the selection model used by the table.
getSelectionModel() - Method in class weka.gui.AttributeSelectionPanel: Gets the selection model used by the table.
getSelectionModel() - Method in class weka.gui.ResultHistoryPanel: Gets the selection model used by the results list.
getSelectionThreshold() - Method in class weka.attributeSelection.RaceSearch: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getSeparatingThreshold() - Method in class weka.classifiers.functions.pace.ChisqMixture: Gets the separating threshold value.
getSeparatingThreshold() - Method in class weka.classifiers.functions.pace.NormalMixture: Gets the separating threshold value.
getSeperator() - Method in class weka.gui.HierarchyPropertyParser: Get the seperator between levels.
getSequentialAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Sequential Attribute Indexes array
getSequentialInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Sequential Instance Indexes array
getSequentialNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes in the Sequential array
getSequentialNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of instances in the Sequential array
getSerializedClassifierFile() - Method in class weka.filters.supervised.attribute.AddClassification: Gets the file pointing to a serialized, trained classifier.
getSERObject() - Method in class weka.clusterers.OPTICS: Returns the internal database
getSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent: Get the set number (ie which fold this is)
getSetNumber() - Method in class weka.gui.beans.BatchClustererEvent: Get the set number (ie which fold this is)
getSetNumber() - Method in class weka.gui.beans.TestSetEvent: Get the test set number (eg.
getSetNumber() - Method in class weka.gui.beans.TrainingSetEvent: Get the set number (eg.
getShape() - Method in class weka.gui.treevisualizer.Node: Get the value of shape.
getShowAttBars() - Method in class weka.gui.visualize.VisualizePanel: Gets whether or not attribute bars are being displayed.
getShowAverage() - Method in class weka.experiment.ResultMatrix: returns whether average per column is displayed or not
getShowAverage() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns whether the Average is shown by default
getShowAverage() - Method in class weka.gui.experiment.OutputFormatDialog: returns whether the average for each column is displayed.
getShowClassPanel() - Method in class weka.gui.visualize.VisualizePanel: Gets whether or not the class panel is being displayed.
getShowGUI() - Method in class weka.clusterers.OPTICS: Returns the flag for showing the OPTICS visualizer GUI.
getShowStdDev() - Method in class weka.experiment.ResultMatrix: returns whether std deviations are displayed or not
getShowStdDevs() - Method in class weka.experiment.PairedTTester: Returns true if standard deviations have been requested.
getShowStdDevs() - Method in interface weka.experiment.Tester: Returns true if standard deviations have been requested.
getShowStdDevs() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns whether StdDevs are shown by default
getShowZeroInstancesAsUnknown() - Method in class weka.gui.InstancesSummaryPanel: Get whether to show zero instances as unknown (i.e.
getShrinkage() - Method in class weka.classifiers.meta.AdditiveRegression: Get the shrinkage rate.
getShrinkage() - Method in class weka.classifiers.meta.LogitBoost: Get the value of Shrinkage.
getShrinking() - Method in class weka.classifiers.functions.LibSVM: whether to use the shrinking heuristics
getShuffle() - Method in class weka.classifiers.rules.Ridor
getSigma() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the value of sigma.
getSigma() - Method in class weka.classifiers.BVDecompose: Get the calculated sigma squared
getSigma() - Method in class weka.classifiers.functions.supportVector.Puk: Gets the sigma value.
getSignificance(int, int) - Method in class weka.experiment.ResultMatrix: returns the significance at the given position, if the position is valid, otherwise SIGNIFICANCE_ATIE
getSignificance() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default significance
getSignificanceCount(int, int) - Method in class weka.experiment.ResultMatrix: counts the occurrences of the given significance type in the given column.
getSignificanceLevel() - Method in class weka.associations.Apriori: Get the value of significanceLevel.
getSignificanceLevel() - Method in class weka.attributeSelection.RaceSearch: Get the significance level
getSignificanceLevel() - Method in class weka.experiment.PairedTTester: Get the value of SignificanceLevel.
getSignificanceLevel() - Method in interface weka.experiment.Tester: Get the value of SignificanceLevel.
getSignificanceWidth() - Method in class weka.experiment.ResultMatrix: returns the current width for the significance
getSilent() - Method in class weka.core.Check: Get whether silent mode is turned on
getSilent() - Method in class weka.core.Javadoc: whether output in the console is suppressed
getSilent() - Method in class weka.estimators.CheckEstimator: Get whether silent mode is turned on
getSimpleStats(int) - Method in class weka.classifiers.rules.RuleStats: Get the simple stats of one rule, including 6 parameters: 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives; 4: false positives; 5: false negatives
getSIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the shape selected for creating splits.
getSingleIndex() - Method in class weka.core.SingleIndex: Gets the string representing the selected range of values
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.LED24: Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.regression.Expression: Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleModeFlag() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.DataGenerator: Return if single mode is set for the given data generator mode depends on option setting and or generator type.
getSingleton() - Static method in class weka.core.logging.Logger: Returns the singleton instance of the logger.
getSingleton() - Static method in class weka.gui.beans.KnowledgeFlowApp: Return the singleton instance of the KnowledgeFlow
getSingleton() - Static method in class weka.gui.GUIChooser: Get the singleton instance of the GUIChooser
getSingleton() - Static method in class weka.gui.Main: Return the singleton instance of the Main GUI.
getSingularValues() - Method in class weka.core.matrix.SingularValueDecomposition: Return the one-dimensional array of singular values
getSize() - Method in class weka.core.Debug.Log: returns the size of the files
getSizePer() - Method in class weka.classifiers.trees.BFTree: Get training set size.
getSizePer() - Method in class weka.classifiers.trees.SimpleCart: Get training set size.
getSkipIdentical() - Method in class weka.core.neighboursearch.LinearNNSearch: Gets whether if identical instances are skipped from the neighbourhood.
getSlope() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns the slope of the function.
getSmoothing() - Method in class weka.classifiers.trees.m5.Rule: Get whether or not smoothing has been turned on
getSmoothingParameter() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Gets the smoothing value to be used to avoid zero WordGivenClass probabilities.
getSort() - Method in class weka.filters.unsupervised.attribute.AddValues: Gets whether the labels are sorted or not.
getSortColumn() - Method in class weka.experiment.PairedTTester: Returns the column to sort on, -1 means the default sorting.
getSortColumn() - Method in interface weka.experiment.Tester: Returns the column to sort on, -1 means the default sorting.
getSortColumnName() - Method in class weka.experiment.PairedTTester: Returns the name of the column to sort on.
getSortColumnName() - Method in interface weka.experiment.Tester: Returns the name of the column to sort on.
getSorting() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default sorting (empty string means none)
getSource() - Method in class weka.gui.beans.BeanConnection: returns the source BeanInstance for this connection
getSource() - Method in class weka.gui.treevisualizer.Edge: Get the value of source.
getSourceCode() - Method in class weka.classifiers.CheckSource: Gets the class to test.
getSourceCode() - Method in class weka.filters.CheckSource: Gets the class to test.
getSparseData() - Method in class weka.experiment.InstanceQuery: Gets whether data is to be returned as a set of sparse instances
getSplitByDataSet() - Method in class weka.experiment.RemoteExperiment: Returns true if sub experiments are to be created on the basis of data set..
getSplitDim() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode: Gets the splitting dimension.
getSplitEvaluator() - Method in class weka.experiment.CrossValidationResultProducer: Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.RandomSplitResultProducer: Get the SplitEvaluator.
getSplitOnResiduals() - Method in class weka.classifiers.trees.LMT: Get the value of splitOnResiduals.
getSplitPoint() - Method in class weka.classifiers.rules.JRip.NumericAntd: Get split point of this numeric antecedent
getSplitPoint() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the split point used for numeric selection
getSplitValue() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode: Gets the splitting value.
getSquaredError() - Method in class weka.clusterers.SimpleKMeans: Gets the squared error for all clusters
getStamp() - Method in class weka.core.Debug.Timestamp: returns the associated date/time
getStandardDeviation(Instance) - Method in class weka.classifiers.functions.GaussianProcesses: Gives the variance of the prediction at the given instance
getStart() - Method in class weka.core.Debug.Clock: returns the start time
getStartMessage() - Method in class weka.gui.beans.Loader: Gets a string that describes the start action.
getStartMessage() - Method in interface weka.gui.beans.Startable: Gets a string that describes the start action.
getStartPoint() - Method in class weka.attributeSelection.RankSearch: Get the point at which to start evaluating the ranking
getStartSequentially() - Method in class weka.gui.beans.FlowRunner: Gets whether Startable beans will be launched sequentially or all in parallel.
getStartSet() - Method in class weka.attributeSelection.BestFirst: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.GeneticSearch: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.GreedyStepwise: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.LinearForwardSelection: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.RandomSearch: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.Ranker: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in interface weka.attributeSelection.StartSetHandler: Returns a list of attributes (and or attribute ranges) as a String
getStaticIcon() - Method in class weka.gui.beans.BeanVisual: Returns the static icon
getStats() - Method in class weka.core.neighboursearch.PerformanceStats: Returns a string representation of the statistics.
getStats() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns a string representation of the statistics.
getStatus() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the status
getStatus() - Method in class weka.gui.beans.InstanceEvent: Get the status
getStatusFrequency() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Get how often progress is reported to the status bar.
getStatusMessage() - Method in class weka.experiment.TaskStatusInfo: Get the status message.
getStatusTable() - Method in class weka.gui.beans.LogPanel: The JTable used for the status messages (in case clients want to attach listeners etc.)
getStdDev() - Method in class weka.estimators.KernelEstimator: Return the standard deviation of this kernel estimator.
getStdDev() - Method in class weka.estimators.NormalEstimator: Return the value of the standard deviation of this normal estimator.
getStdDev(int, int) - Method in class weka.experiment.ResultMatrix: returns the std deviation at the given position, if the position is valid, otherwise 0
getStdDevCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the standard deviation of coords per point.
getStdDevIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the standard deviation of internal nodes visited.
getStdDevLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the standard deviation of leaves visited.
getStdDevPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the standard deviation of points visited.
getStdDevPrec() - Method in class weka.experiment.ResultMatrix: returns the current standard deviation precision
getStdDevPrec() - Method in class weka.gui.experiment.OutputFormatDialog: Gets the precision used for printing the std.
getStdDevPrecision() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default precision for the stddevs
getStddevValue() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition
getStdDevWidth() - Method in class weka.experiment.ResultMatrix: returns the current width for the std dev
getStemmer() - Method in class weka.core.stemmers.SnowballStemmer: returns the name of the current stemmer, null if none is set.
getStemmer() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the current stemming algorithm, null if none is used.
getStepSize() - Method in class weka.attributeSelection.RankSearch: Get the number of attributes to add from the rankining in each iteration
getStepSize() - Method in class weka.experiment.LearningRateResultProducer: Get the value of StepSize.
getStop() - Method in class weka.core.Debug.Clock: returns the stop time or, if still running, the current time
getStopwords() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: returns the file used for obtaining the stopwords, if the file represents a directory then the default ones are used.
getString(String) - Static method in class weka.associations.gsp.Messages: getString.
getString(String, Locale) - Static method in class weka.associations.gsp.Messages: getString.
getString(String) - Static method in class weka.associations.Messages: getString.
getString(String, Locale) - Static method in class weka.associations.Messages: getString.
getString(int[]) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Returns the list of indices as a string.
getString(int[]) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Returns the list of indices as a string.
getString() - Method in class weka.core.Trie.TrieNode: returns the full string up to the root
getString(String) - Static method in class weka.gui.arffviewer.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.arffviewer.Messages: getString.
getString(String) - Static method in class weka.gui.beans.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.beans.Messages: getString.
getString(String) - Static method in class weka.gui.beans.xml.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.beans.xml.Messages: getString.
getString(String) - Static method in class weka.gui.boundaryvisualizer.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.boundaryvisualizer.Messages: getString.
getString(String) - Static method in class weka.gui.experiment.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.experiment.Messages: getString.
getString(String) - Static method in class weka.gui.explorer.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.explorer.Messages: getString.
getString(String) - Static method in class weka.gui.graphvisualizer.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.graphvisualizer.Messages: getString.
getString(String) - Static method in class weka.gui.hierarchyvisualizer.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.hierarchyvisualizer.Messages: getString.
getString(String) - Static method in class weka.gui.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.Messages: getString.
getString(String) - Static method in class weka.gui.sql.event.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.sql.event.Messages: getString.
getString(String) - Static method in class weka.gui.sql.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.sql.Messages: getString.
getString(String) - Static method in class weka.gui.streams.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.streams.Messages: getString.
getString(String) - Static method in class weka.gui.treevisualizer.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.treevisualizer.Messages: getString.
getString(String) - Static method in class weka.gui.visualize.Messages: getString.
getString(String, Locale) - Static method in class weka.gui.visualize.Messages: getString.
getStringAttributes() - Method in class weka.core.converters.CSVLoader: Returns the current attribute range to be forced to type string.
getStringSelection() - Method in class weka.gui.arffviewer.ArffTable: returns the selected content in a StringSelection that can be copied to the clipboard and used in Excel, if nothing is selected the whole table is copied to the clipboard
getStroke() - Method in class weka.gui.visualize.PostscriptGraphics
getStructure() - Method in class weka.core.converters.AbstractLoader
getStructure() - Method in class weka.core.converters.ArffLoader.ArffReader: Returns the header format
getStructure() - Method in class weka.core.converters.ArffLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.C45Loader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.ConverterUtils.DataSource: returns the structure of the data.
getStructure(int) - Method in class weka.core.converters.ConverterUtils.DataSource: returns the structure of the data, with the defined class index.
getStructure() - Method in class weka.core.converters.CSVLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.DatabaseLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.LibSVMLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in interface weka.core.converters.Loader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.SerializedInstancesLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.SVMLightLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.TextDirectoryLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.XRFFLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure(String) - Method in class weka.gui.beans.ClassAssigner: Get the structure of the output encapsulated in the named event.
getStructure(String) - Method in class weka.gui.beans.ClassValuePicker
getStructure() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the instances structure (may be null if this is not a NEW_BATCH event)
getStructure() - Method in class weka.gui.beans.InstanceEvent: Get the instances structure (may be null if this is not a FORMAT_AVAILABLE event)
getStructure(String) - Method in class weka.gui.beans.Loader: Get the structure of the output encapsulated in the named event.
getStructure(String) - Method in interface weka.gui.beans.StructureProducer: Get the structure of the output encapsulated in the named event.
getSubFlow() - Method in class weka.gui.beans.MetaBean
getSubmenuTitle() - Method in interface weka.gui.MainMenuExtension: Returns the name of the submenu.
getSubsequenceLength() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the length of the subsequence
getSubsetEvaluator() - Method in class weka.attributeSelection.FilteredSubsetEval: Get the subset evaluator to use
getSubsetSizeEvaluator() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Get the subset evaluator used for subset size determination.
getSubSpaceSize() - Method in class weka.classifiers.meta.RandomSubSpace: Gets the size of each subSpace, as a percentage of the training set size.
getSubtreeRaising() - Method in class weka.classifiers.trees.J48: Get the value of subtreeRaising.
getSubtreeRaising() - Method in class weka.classifiers.trees.J48graft: Get the value of subtreeRaising.
getSuccess() - Method in class weka.core.CheckGOE: returns the success of the tests
getSuccess() - Method in class weka.core.CheckOptionHandler: returns the success of the tests
getSuitableTargets(EventSetDescriptor) - Method in class weka.gui.beans.MetaBean: Return a list of input beans capable of receiving the supplied event
getSummary() - Method in class weka.gui.SetInstancesPanel: Gets the instances summary panel associated with this panel
getSumOfCounts() - Method in class weka.estimators.DiscreteEstimator: Get the sum of all the counts
getSumOfWeights() - Method in class weka.estimators.NormalEstimator: Return the sum of the weights for this normal estimator.
getSupportedCursorScrollType() - Method in class weka.experiment.DatabaseUtils: Returns the type of scrolling that the cursor supports, -1 if not supported or not connected.
getSVMType() - Method in class weka.classifiers.functions.LibLINEAR: Gets type of SVM
getSVMType() - Method in class weka.classifiers.functions.LibSVM: Gets type of SVM
getSymbols() - Method in class weka.core.mathematicalexpression.Parser: Returns the current variable - value relation in use.
getSymbols() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Returns the current variable - value relation in use.
getSystemInfo() - Method in class weka.core.SystemInfo: returns a copy of the system info.
getSystemLookAndFeel() - Static method in class weka.gui.LookAndFeel: returns the system LnF classname
getSystemWide() - Static method in class weka.core.Environment: Get the singleton system-wide (visible to every class in the running VM) set of environment variables.
getTabbedPane() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the tabbedpane instance
getTabbedPane() - Method in class weka.gui.explorer.Explorer: returns the tabbed pane of the Explorer
getTable() - Method in class weka.gui.arffviewer.ArffPanel: returns the table component
getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class weka.gui.arffviewer.ArffTableCellRenderer: Returns the default table cell renderer.
getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int) - Method in class weka.gui.sql.ResultSetTableCellRenderer: Returns the default table cell renderer.
getTableModel() - Method in class weka.gui.AttributeSelectionPanel: Get the table model in use (or null if no instances have been set yet).
getTableName() - Method in class weka.core.converters.DatabaseSaver: Gets the table's name.
getTabs() - Static method in class weka.gui.explorer.ExplorerDefaults: returns an array with the classnames of all the additional panels to display as tabs in the Explorer.
getTabTitle() - Method in class weka.gui.explorer.AssociationsPanel: Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.AttributeSelectionPanel: Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.ClassifierPanel: Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.ClustererPanel: Returns the title for the tab in the Explorer
getTabTitle() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel: Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.PreprocessPanel: Returns the title for the tab in the Explorer
getTabTitle() - Method in class weka.gui.explorer.VisualizePanel: Returns the title for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.AssociationsPanel: Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.AttributeSelectionPanel: Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.ClassifierPanel: Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.ClustererPanel: Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in interface weka.gui.explorer.Explorer.ExplorerPanel: Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.PreprocessPanel: Returns the tooltip for the tab in the Explorer
getTabTitleToolTip() - Method in class weka.gui.explorer.VisualizePanel: Returns the tooltip for the tab in the Explorer
getTabuList() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
getTabuList() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
getTags() - Method in class weka.core.SelectedTag: Gets the set of all valid Tags.
getTags() - Method in class weka.gui.CostMatrixEditor: Some objects can return tags, but a cost matrix cannot.
getTags() - Method in class weka.gui.GenericArrayEditor: Returns null as we don't support getting values as tags.
getTags() - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting values as tags.
getTags() - Method in class weka.gui.SelectedTagEditor: Gets the list of tags that can be selected from.
getTags() - Method in class weka.gui.SimpleDateFormatEditor: Some objects can return tags, but a date format cannot.
getTarget() - Method in class weka.gui.beans.BeanConnection: Returns the target BeanInstance for this connection
getTarget() - Method in class weka.gui.treevisualizer.Edge: Get the value of target.
getTargetClass() - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary: Gets the Target Class
getTargetMetaData() - Method in class weka.core.pmml.MiningSchema: Get the Target meta data.
getTaskResult() - Method in class weka.experiment.TaskStatusInfo: Get the returnable result of this task.
getTaskStatus() - Method in class weka.experiment.RemoteExperimentSubTask
getTaskStatus() - Method in interface weka.experiment.Task: Clients should be able to call this method at any time to obtain information on a current task.
getTaskStatus() - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Return status information for this sub task
getTechnicalInformation() - Method in class weka.associations.Apriori: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.associations.FPGrowth: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns TechnicalInformation about the paper related to the algorithm.
getTechnicalInformation() - Method in class weka.associations.PredictiveApriori: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.associations.Tertius: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.CfsSubsetEval: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.ConsistencySubsetEval: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.GeneticSearch: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.LinearForwardSelection: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.RaceSearch: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.RandomSearch: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.RankSearch: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.ScatterSearchV1: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.SVMAttributeEval: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.AODE: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.AODEsr: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.DMNBtext: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.HNB: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayes: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesSimple: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.ADNode: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.BIFReader: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.K2: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.global.TAN: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.K2: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.net.search.local.TAN: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.bayes.WAODE: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.BVDecompose: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.BVDecomposeSegCVSub: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.GaussianProcesses: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.LeastMedSq: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.LibLINEAR: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.LibSVM: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.Logistic: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.pace.MixtureDistribution: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.PaceRegression: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.SimpleLogistic: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.SMO: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.SMOreg: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.SPegasos: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.Puk: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.RegSMO: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.VotedPerceptron: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.functions.Winnow: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.IB1: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.IBk: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.KStar: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.LBR: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.lazy.LWL: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.AdaBoostM1: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.AdditiveRegression: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Bagging: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.ClassificationViaRegression: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.CVParameterSelection: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Dagging: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Decorate: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.END: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Grading: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.LogitBoost: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.MetaCost: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.MultiBoostAB: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.nestedDichotomies.ND: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.RandomSubSpace: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.RotationForest: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Stacking: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.StackingC: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.meta.Vote: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.CitationKNN: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MDD: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIBoost: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIDD: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIEMDD: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MINND: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIOptimalBall: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MISMO: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MISVM: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.mi.MIWrapper: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.misc.VFI: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.DecisionTable: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.DTNB: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.JRip: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.M5Rules: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.NNge: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.OneR: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.PART: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.rules.Prism: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.ADTree: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.BFTree: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.FT: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.Id3: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.J48: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.J48graft: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.LADTree: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.LMT: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.m5.M5Base: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.NBTree: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.RandomForest: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.SimpleCart: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.classifiers.trees.UserClassifier: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.CLOPE: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.Cobweb: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.DBSCAN: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.FarthestFirst: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.OPTICS: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.sIB: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.clusterers.XMeans: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.ChebyshevDistance: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.EuclideanDistance: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.ManhattanDistance: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.BallTree: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.CoverTree: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.KDTree: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.Optimization: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.core.stemmers.LovinsStemmer: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in interface weka.core.TechnicalInformationHandler: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.datagenerators.classifiers.classification.LED24: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.experiment.PairedCorrectedTTester: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.supervised.attribute.Discretize: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.supervised.instance.SMOTE: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.Wavelet: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTechnicalInformation() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
getTempDir() - Static method in class weka.core.Debug: returns the system temp directory
getTester() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the display name of the preferred Tester algorithm
getTestEvaluator() - Method in class weka.attributeSelection.CheckAttributeSelection: Gets whether the evaluator is being tested or the search method.
getTestOrTrain() - Method in class weka.gui.beans.BatchClustererEvent: Get whether the set of instances is a test or a training set
getTestPredictions(Classifier, Instances) - Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.
getTestSet() - Method in class weka.gui.beans.BatchClassifierEvent: Get the test set
getTestSet() - Method in class weka.gui.beans.BatchClustererEvent: Get the training/test set
getTestSet() - Method in class weka.gui.beans.TestSetEvent: Get the test set instances
getText() - Method in class weka.gui.beans.BeanVisual: Get the visual's label
getText() - Method in class weka.gui.beans.TextEvent: Describe getText method here.
getTextTitle() - Method in class weka.gui.beans.TextEvent: Describe getTextTitle method here.
getTFTransform() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.
getThreshold() - Method in class weka.attributeSelection.GreedyStepwise: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold() - Method in class weka.attributeSelection.RaceSearch: Get the threshold
getThreshold() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets the threshold by which attributes can be discarded.
getThreshold() - Method in class weka.attributeSelection.Ranker: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold() - Method in class weka.attributeSelection.ScatterSearchV1: Get the treshold
getThreshold() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the value of the threshold
getThreshold() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Return the threshold being used.
getThreshold() - Method in class weka.classifiers.functions.PaceRegression: Gets the threshold for olsc estimator
getThreshold() - Method in class weka.classifiers.functions.Winnow: Get the value of Threshold.
getThreshold() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the threshold for the max error when predicting a numeric class.
getThresholdInstance(Instances, double) - Static method in class weka.classifiers.evaluation.ThresholdCurve: Gets the index of the instance with the closest threshold value to the desired target
getTimestamp() - Static method in class weka.experiment.CrossValidationResultProducer: Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.RandomSplitResultProducer: Gets a Double representing the current date and time.
getTitle() - Method in class weka.gui.arffviewer.ArffPanel: returns the title for the Tab, i.e.
getToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils: Gets token.
getTokenizer() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the current tokenizer algorithm.
getTolerance() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Get the tolerance value
getTolerance() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: returns the current tolerance
getToleranceParameter() - Method in class weka.attributeSelection.SVMAttributeEval: Get the value of T used with SMO
getToleranceParameter() - Method in class weka.classifiers.functions.SMO: Get the value of tolerance parameter.
getToleranceParameter() - Method in class weka.classifiers.mi.MISMO: Get the value of tolerance parameter.
getToolTipText() - Method in class weka.experiment.PairedCorrectedTTester: returns a string that is displayed as tooltip on the "perform test" button in the experimenter
getToolTipText() - Method in class weka.experiment.PairedTTester: returns a string that is displayed as tooltip on the "perform test" button in the experimenter
getToolTipText() - Method in interface weka.experiment.Tester: returns a string that is displayed as tooltip on the "perform test" button in the experimenter
getToolTipText(MouseEvent) - Method in class weka.gui.AttributeVisualizationPanel: Returns "<nominal value> [<nominal value count>]" if displaying a bar plot and mouse is on some bar.
getToolTipText() - Method in class weka.gui.GenericObjectEditor.GOETreeNode: Get the tool tip for this node
getToolTipText(PrintableComponent) - Static method in class weka.gui.visualize.PrintableComponent: Returns a tooltip only if the user wants it.
getTop() - Method in class weka.gui.treevisualizer.Node: Get the value of top.
getTotalCoordsPerPoint() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the total sum of coords per point.
getTotalCount(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of nodes there are.
getTotalGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of groups of siblings there are.
getTotalHeight(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of levels there are.
getTotalIntNodesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the total number of internal nodes visited.
getTotalLeavesVisited() - Method in class weka.core.neighboursearch.TreePerformanceStats: Returns the total number of leaves visited.
getTotalPointsVisited() - Method in class weka.core.neighboursearch.PerformanceStats: Returns the total number of points visited.
getTotalSupport() - Method in class weka.associations.FPGrowth.AssociationRule: Get the total support for this rule (premise + consequence).
getTotalTransactions() - Method in class weka.associations.FPGrowth.AssociationRule: Get the total number of transactions in the data.
getToYear() - Static method in class weka.core.Copyright: returns the end year of the copyright (i.e., current year)
getTPRate() - Method in class weka.associations.tertius.Rule: Get the rate of True Positive instances of this rule.
getTrainingSet() - Method in class weka.gui.beans.TrainingSetEvent: Get the training instances
getTrainingTime() - Method in class weka.classifiers.functions.MultilayerPerceptron
getTrainIterations() - Method in class weka.classifiers.BVDecompose: Gets the maximum number of boost iterations
getTrainPercent() - Method in class weka.experiment.RandomSplitResultProducer: Get the value of TrainPercent.
getTrainPercent() - Method in class weka.gui.beans.TrainTestSplitMaker: Get the percentage of the data that will be in the training portion of the split
getTrainPercentage() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the training percentage in case of splits
getTrainPoolSize() - Method in class weka.classifiers.BVDecompose: Get the number of instances in the training pool.
getTrainSet() - Method in class weka.gui.beans.BatchClassifierEvent: Get the train set
getTrainSize() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the training size
getTrainTestPredictions(Classifier, Instances, Instances) - Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.
getTransactionsMustContain() - Method in class weka.associations.FPGrowth: Gets the comma separated list of items that transactions must contain in order to be considered for large item sets and rules.
getTransform() - Method in class weka.gui.visualize.PostscriptGraphics
getTransformAllValues() - Method in class weka.filters.supervised.attribute.NominalToBinary: Gets if all nominal values are turned into new attributes, not only if there are more than 2.
getTransformAllValues() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets if all nominal values are turned into new attributes, not only if there are more than 2.
getTransformationDictionary() - Method in class weka.core.pmml.MiningSchema: Get the transformation dictionary .
getTransformBackToOriginal() - Method in class weka.attributeSelection.PrincipalComponents: Gets whether the data is to be transformed back to the original space.
getTransformMethod() - Method in class weka.classifiers.mi.SimpleMI: Get the method used in transformation.
getTranslation() - Method in class weka.filters.unsupervised.attribute.Normalize: Get the translation.
getTraversal() - Method in class weka.classifiers.meta.GridSearch: Gets the type of traversal for the grid.
getTrimingThreshold() - Method in class weka.classifiers.functions.pace.ChisqMixture: Gets the triming thresholding value.
getTrimingThreshold() - Method in class weka.classifiers.functions.pace.NormalMixture: Gets the triming thresholding value.
getTrueNegative() - Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of negative instances predicted as negative
getTruePositive() - Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of positive instances predicted as positive
getTruePositiveRate() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the true positive rate.
getTStart() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
getTStart() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
getTwoClassStats(int) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the performance with respect to one of the classes as a TwoClassStats object.
getType() - Method in class weka.associations.tertius.IndividualLiteral
getType() - Method in class weka.associations.tertius.LiteralSet: Give the type of properties in this set (individual or part properties).
getType() - Method in class weka.attributeSelection.LinearForwardSelection: Get the type
getType() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Get the type
getType() - Method in class weka.classifiers.functions.neural.NeuralConnection
getType() - Method in class weka.core.AttributeLocator: returns the type of attribute that is located
getType(RevisionHandler) - Static method in class weka.core.RevisionUtils: Determines the type of a (sanitized) revision string returned by the RevisionHandler.
getType(String) - Static method in class weka.core.RevisionUtils: Determines the type of a (sanitized) revision string.
getType() - Method in class weka.core.TechnicalInformation: returns the type of this technical information
getType(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns the TYPE of the attribute at the given position
getType(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns the TYPE of the attribute at the given position
getType(int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the TYPE of the attribute at the given position
getType(int, int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the TYPE of the attribute at the given position
getType() - Method in class weka.gui.sql.event.ConnectionEvent: returns the type of this event, CONNECT or DISCONNECT
getU() - Method in class weka.core.Matrix: Deprecated.

Returns the U part of the matrix.
getU() - Method in class weka.core.matrix.LUDecomposition: Return upper triangular factor
getU() - Method in class weka.core.matrix.SingularValueDecomposition: Return the left singular vectors
getUID(String) - Static method in class weka.core.SerializationHelper: reads or creates the serialVersionUID for the given class.
getUID(Class) - Static method in class weka.core.SerializationHelper: reads or creates the serialVersionUID for the given class.
getUnpruned() - Method in class weka.classifiers.rules.PART: Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.J48: Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.J48graft: Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.m5.M5Base: Get whether unpruned tree/rules are being generated
getUnpruned() - Method in class weka.classifiers.trees.m5.Rule: Get whether unpruned tree/rules are being generated
getUpdateCount() - Method in class weka.core.converters.DatabaseConnection: Dewtermines if the current query retrieves a result set or updates a table
getUpdateIncrementalClassifier() - Method in class weka.gui.beans.Classifier: Get whether an incremental classifier will be updated on the incoming instance stream.
getUpper() - Method in class weka.gui.experiment.RunNumberPanel: Gets the current upper run number.
getUpperBoundMinSupport() - Method in class weka.associations.Apriori: Get the value of upperBoundMinSupport.
getUpperBoundMinSupport() - Method in class weka.associations.FPGrowth: Get the value of upperBoundMinSupport.
getUpperCase() - Method in class weka.core.converters.DatabaseConnection: Check if the property checkUpperCaseNames in the DatabaseUtils file is set to true or false.
getUpperNumericBound() - Method in class weka.core.Attribute: Returns the upper bound of a numeric attribute.
getUpperSize() - Method in class weka.experiment.LearningRateResultProducer: Get the value of UpperSize.
getUrl() - Method in interface weka.core.converters.DatabaseConverter
getUrl() - Method in class weka.core.converters.DatabaseLoader: Gets the URL
getUrl() - Method in class weka.core.converters.DatabaseSaver: Gets the database URL.
getURL() - Static method in class weka.core.Copyright: returns the URL of the owner
getURL() - Method in class weka.gui.DatabaseConnectionDialog: Returns URL from dialog
getURL(String, String) - Static method in class weka.gui.Loader: returns a URL for the given filename, can be NULL if it fails
getURL(String) - Method in class weka.gui.Loader: returns a URL for the given filename, can be NULL if it fails
getURL() - Method in class weka.gui.sql.ConnectionPanel: returns the current URL.
getURL() - Method in class weka.gui.sql.event.ResultChangedEvent: returns the database URL that produced the table model
getURL() - Method in class weka.gui.sql.ResultSetTable: returns the database URL that produced the table model
getURL() - Method in class weka.gui.sql.SqlViewer: returns the database URL from the currently active tab in the ResultPanel, otherwise an empty string.
getURL() - Method in class weka.gui.sql.SqlViewerDialog: returns the chosen URL, if any
getURLFileLoaders() - Static method in class weka.core.converters.ConverterUtils: returns a vector with the classnames of all the URL file loaders.
getURLLoaderForExtension(String) - Static method in class weka.core.converters.ConverterUtils: tries to determine the URL loader to use for this kind of extension, returns null if none can be found.
getURLLoaderForFile(String) - Static method in class weka.core.converters.ConverterUtils: tries to determine the URL loader to use for this kind of file, returns null if none can be found.
getURLLoaderForFile(File) - Static method in class weka.core.converters.ConverterUtils: tries to determine the URL loader to use for this kind of file, returns null if none can be found.
getUsageType() - Method in class weka.core.pmml.MiningFieldMetaInfo: Get the usage type of this field.
getUseADTree() - Method in class weka.classifiers.bayes.BayesNet: Method declaration
getUseAIC() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of useAIC.
getUseAIC() - Method in class weka.classifiers.trees.FT: Get the value of useAIC.
getUseAIC() - Method in class weka.classifiers.trees.LMT: Get the value of useAIC.
getUseAIC() - Method in class weka.classifiers.trees.lmt.LogisticBase: Get the value of useAIC.
getUseArcReversal() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: get use the arc reversal operation
getUseArcReversal() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: get use the arc reversal operation
getUseBetterEncoding() - Method in class weka.filters.supervised.attribute.Discretize: Gets whether better encoding is to be used for MDL.
getUseClassification() - Static method in class weka.gui.experiment.ExperimenterDefaults: whether classification or regression is used
getUseCpuTime() - Method in class weka.core.Debug.Clock: returns whether the use of CPU is time is enabled/disabled (regardless whether the system supports it or not)
getUseCrossOver() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getUseCrossOver() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getUseCrossValidation() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of useCrossValidation.
getUseCustomDimensions() - Method in class weka.gui.visualize.JComponentWriter: whether custom dimensions are to used for the size of the image
getUsedAttributes() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns an array of the indices of the attributes used in the logistic model.
getUseEqualFrequency() - Method in class weka.classifiers.meta.RegressionByDiscretization: Get the value of UseEqualFrequency.
getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.Discretize: Get the value of UseEqualFrequency.
getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Get the value of UseEqualFrequency.
getUseErrorRate() - Method in class weka.classifiers.trees.BFTree: Get if use error rate in internal cross-validation.
getUseGini() - Method in class weka.classifiers.trees.BFTree: Get if use Gini index as splitting criterion.
getUseGUI() - Method in class weka.core.Memory: whether to display a dialog in case of a problem (= TRUE) or just print on stderr (= FALSE)
getUseIBk() - Method in class weka.classifiers.rules.DecisionTable: Gets whether IBk is being used instead of the majority class
getUseKDTree() - Method in class weka.clusterers.XMeans: Gets whether the KDTree is used or not.
getUseKernelEstimator() - Method in class weka.classifiers.bayes.NaiveBayes: Gets if kernel estimator is being used.
getUseKononenko() - Method in class weka.filters.supervised.attribute.Discretize: Gets whether Kononenko's MDL criterion is to be used.
getUseLaplace() - Method in class weka.classifiers.bayes.AODEsr: Gets if laplace correction is being used.
getUseLaplace() - Method in class weka.classifiers.trees.J48: Get the value of useLaplace.
getUseLaplace() - Method in class weka.classifiers.trees.J48graft: Get the value of useLaplace.
getUseLeastValues() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Gets whether to use values with least or most instances
getUseLowerOrder() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Gets whether lower-order terms are used.
getUseMEstimates() - Method in class weka.classifiers.bayes.AODE: Gets if m-estimaces is being used.
getUseMissing() - Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the flag if missing values are treated as extra values.
getUseMutation() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getUseMutation() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getUseNormalization() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns whether normalization is used.
getUseOneSE() - Method in class weka.classifiers.trees.BFTree: Get if use the 1SE rule to choose final model.
getUseOneSE() - Method in class weka.classifiers.trees.SimpleCart: Get if use the 1SE rule to choose final model.
getUseORForMustContainList() - Method in class weka.associations.FPGrowth: Gets whether OR is to be used rather than AND when considering must contain lists.
getUsePairwiseCoupling() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
getUseProb() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
getUsePropertyIterator() - Method in class weka.experiment.Experiment: Gets whether the custom property iterator should be used.
getUsePrune() - Method in class weka.classifiers.trees.SimpleCart: Get if use minimal cost-complexity pruning.
getUsePruning() - Method in class weka.classifiers.rules.JRip: Gets whether pruning is performed
getUser() - Method in interface weka.core.converters.DatabaseConverter
getUser() - Method in class weka.core.converters.DatabaseLoader: Gets the user name
getUser() - Method in class weka.core.converters.DatabaseSaver: Gets the database user.
getUser() - Method in class weka.gui.sql.ConnectionPanel: returns the current User.
getUser() - Method in class weka.gui.sql.event.ResultChangedEvent: returns the user that produced the table model
getUser() - Method in class weka.gui.sql.ResultSetTable: returns the user that produced the table model
getUser() - Method in class weka.gui.sql.SqlViewer: returns the user from the currently active tab in the ResultPanel, otherwise an empty string.
getUser() - Method in class weka.gui.sql.SqlViewerDialog: returns the chosen user, if any
getUseRelativePath() - Method in class weka.core.converters.AbstractFileLoader: Gets whether relative paths are to be used
getUseRelativePath() - Method in class weka.core.converters.AbstractFileSaver: Gets whether relative paths are to be used
getUseRelativePath() - Method in interface weka.core.converters.FileSourcedConverter: Gets whether relative paths are to be used
getUseRelativePath() - Method in class weka.gui.beans.SerializedModelSaver: Get whether to use relative paths for the directory.
getUseRelativePaths() - Static method in class weka.gui.experiment.ExperimenterDefaults: whether relative paths are used by default
getUseResampling() - Method in class weka.classifiers.meta.AdaBoostM1: Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.LogitBoost: Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get whether resampling is turned on
getUsername() - Method in class weka.experiment.DatabaseUtils: Get the database username.
getUsername() - Method in class weka.gui.DatabaseConnectionDialog: Returns Username from dialog
getUserOptions() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation: returns the options the user supplied for the kernel
getUserOptions() - Method in class weka.core.CheckOptionHandler: Gets the current user-supplied options (creates a copy)
getUseStars() - Method in class weka.core.Javadoc: whether the Javadoc is prefixed with "*"
getUseStoplist() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the words on the stoplist are to be ignored (The stoplist is in weka.core.StopWords).
getUseSupervisedDiscretization() - Method in class weka.classifiers.bayes.NaiveBayes: Get whether supervised discretization is to be used.
getUseTournamentSelection() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getUseTournamentSelection() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getUseTraining() - Method in class weka.attributeSelection.ClassifierSubsetEval: Get if training data is to be used instead of hold out/test data
getUseTree() - Method in class weka.classifiers.trees.m5.Rule: get whether an m5 tree is being used rather than rules
getUseUnsmoothed() - Method in class weka.classifiers.trees.m5.M5Base: Get whether or not smoothing is being used
getV() - Method in class weka.core.matrix.EigenvalueDecomposition: Return the eigenvector matrix
getV() - Method in class weka.core.matrix.SingularValueDecomposition: Return the right singular vectors
getValidating() - Method in class weka.core.xml.XMLDocument: returns whether a validating parser is used.
getValidating() - Method in class weka.core.xml.XMLOptions: returns whether a validating parser is used.
getValidationChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the validation chunk size
getValidationSetSize() - Method in class weka.classifiers.functions.MultilayerPerceptron
getValidationThreshold() - Method in class weka.classifiers.functions.MultilayerPerceptron
getValue() - Method in class weka.classifiers.trees.adtree.PredictionNode: Gets the prediction value of the node.
getValue() - Method in class weka.core.pmml.FieldMetaInfo.Value
getValue(Object, PropertyPath.Path) - Static method in class weka.core.PropertyPath: returns the value specified by the given path from the object
getValue(Object, String) - Static method in class weka.core.PropertyPath: returns the value specified by the given path from the object
getValue(TechnicalInformation.Field) - Method in class weka.core.TechnicalInformation: returns the value associated with the given field, or empty if field is not currently stored.
getValue(Instance, int) - Static method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Returns either a String object for nominal attributes or a Double for numeric ones.
getValue() - Method in class weka.gui.CostMatrixEditor: Gets the cost matrix that is being edited.
getValue() - Method in class weka.gui.GenericArrayEditor: Gets the current object array.
getValue() - Method in class weka.gui.GenericObjectEditor: Gets the current Object.
getValue() - Method in class weka.gui.HierarchyPropertyParser: Get the value of current node
getValue() - Method in class weka.gui.SimpleDateFormatEditor: Gets the date format that is being edited.
getValue() - Method in class weka.gui.SortedTableModel.SortContainer: Returns the value to sort on.
getValueAt(int, int) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel: Returns the value for the JTable for a given position.
getValueAt(int, int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the value for the cell at columnindex and rowIndex
getValueAt(int, int) - Method in class weka.gui.SortedTableModel: Returns the value for the cell at columnIndex and rowIndex.
getValueAt(int, int) - Method in class weka.gui.sql.ResultSetTableModel: returns the value for the cell at columnindex and rowIndex.
getValueIndex() - Method in class weka.associations.FPGrowth.BinaryItem: Get the value index for this item.
getValueIndices() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the indices of the indicator values.
getValueName(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: returns value of a node
getValueRange() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the range containing the indicator values.
getValues(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: returns array of values of a node
getValues(int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: returns array of values of a node
getValues() - Method in class weka.classifiers.meta.GridSearch: returns the parameter pair that was found to work best
getValues() - Method in class weka.core.pmml.TargetMetaInfo: Get the values (discrete case only) for this Target.
getValues() - Method in class weka.gui.visualize.VisualizePanelEvent
getValuesList() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: returns the range for each attribute as string
getValuesOutput() - Method in class weka.associations.Tertius: Get the value of valuesOutput.
getVarbValues() - Method in class weka.core.Optimization: Get the variable values.
getVariableNames() - Method in class weka.core.Environment: Get the names of the variables (keys) stored in the internal map.
getVariableValue(String) - Method in class weka.core.Environment: Get the value for a particular variable.
getVariance() - Method in class weka.classifiers.BVDecompose: Get the calculated variance
getVarianceCovered() - Method in class weka.attributeSelection.PrincipalComponents: Gets the proportion of total variance to account for when retaining principal components
getVarianceCovered() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Gets the proportion of total variance to account for when retaining principal components.
getVectorOfAttrTypes() - Method in class weka.estimators.CheckEstimator.AttrTypes
getVerbose() - Method in class weka.associations.Apriori: Gets whether algorithm is run in verbose mode
getVerbose() - Method in class weka.attributeSelection.ExhaustiveSearch: get whether or not output is verbose
getVerbose() - Method in class weka.attributeSelection.LinearForwardSelection: Get whether output is to be verbose
getVerbose() - Method in class weka.attributeSelection.RandomSearch: get whether or not output is verbose
getVerbose() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Get whether output is to be verbose
getVerbose() - Method in class weka.classifiers.meta.Dagging: Gets the verbose state
getVersion() - Method in class weka.core.xml.XMLSerialization: returns the WEKA version with which the serialized object was created
getVisible() - Method in class weka.gui.treevisualizer.Node: Get the value of visible.
getVisibleColCount() - Method in class weka.experiment.ResultMatrix: returns the number of visible columns
getVisibleRowCount() - Method in class weka.experiment.ResultMatrix: returns the number of visible rows
getVisual() - Method in class weka.gui.beans.AbstractDataSink: Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.AbstractDataSource: Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.AbstractEvaluator: Get the visual
getVisual() - Method in class weka.gui.beans.AbstractTestSetProducer: Get the visual for this bean
getVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Get the visual for this bean
getVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer: Get the visual for this bean
getVisual() - Method in class weka.gui.beans.Associator: Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.ClassAssigner
getVisual() - Method in class weka.gui.beans.Classifier: Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.ClassValuePicker
getVisual() - Method in class weka.gui.beans.Clusterer: Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.CostBenefitAnalysis
getVisual() - Method in class weka.gui.beans.DataVisualizer: Return the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.Filter: Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.GraphViewer: Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.MetaBean: Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.ModelPerformanceChart: Return the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.PredictionAppender: Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.SerializedModelSaver: Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.StripChart: Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.TextViewer: Get the visual appearance of this bean
getVisual() - Method in interface weka.gui.beans.Visible: Get the visual representation
getVisualizeMenuItem(Instances) - Method in interface weka.gui.visualize.plugins.ErrorVisualizePlugin: Get a JMenu or JMenuItem which contain action listeners that perform the visualization of the classifier errors.
getVisualizeMenuItem(String, String) - Method in interface weka.gui.visualize.plugins.GraphVisualizePlugin: Get a JMenu or JMenuItem which contain action listeners that perform the visualization of the graph in XML BIF format.
getVisualizeMenuItem(String, String) - Method in interface weka.gui.visualize.plugins.TreeVisualizePlugin: Get a JMenu or JMenuItem which contain action listeners that perform the visualization of the tree in GraphViz's dotty format.
getVisualizeMenuItem(FastVector, Attribute) - Method in interface weka.gui.visualize.plugins.VisualizePlugin: Get a JMenu or JMenuItem which contain action listeners that perform the visualization, using some but not necessarily all of the data.
getVoteFlag() - Method in class weka.datagenerators.classifiers.classification.RDG1: Gets the vote flag.
getWBias() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated bias according to the Webb definition
getWeight() - Method in class weka.classifiers.bayes.AODE: Gets the weight used in m-estimate
getWeightByConfidence() - Method in class weka.classifiers.misc.VFI: Get whether feature intervals are being weighted by confidence
getWeightByDistance() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get whether nearest neighbours are being weighted by distance
getWeightingKernel() - Method in class weka.classifiers.lazy.LWL: Gets the kernel weighting method to use.
getWeightMethod() - Method in class weka.classifiers.mi.MIWrapper: Returns the current weighting method for instances.
getWeightMethod() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Returns the current weighting method for instances.
getWeights() - Method in class weka.classifiers.functions.LibLINEAR: Gets the parameters C of class i to weight[i]*C (default 1).
getWeights() - Method in class weka.classifiers.functions.LibSVM: Gets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
getWeights() - Method in class weka.classifiers.functions.neural.NeuralNode: call this function to get the weights array.
getWeights() - Method in class weka.estimators.KernelEstimator: Return the weights of the kernels.
getWeights() - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Get weights
getWeights() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
getWeightThreshold() - Method in class weka.classifiers.meta.AdaBoostM1: Get the degree of weight thresholding
getWeightThreshold() - Method in class weka.classifiers.meta.LogitBoost: Get the degree of weight thresholding
getWeightTrimBeta() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of weightTrimBeta.
getWeightTrimBeta() - Method in class weka.classifiers.trees.FT: Get the value of weightTrimBeta.
getWeightTrimBeta() - Method in class weka.classifiers.trees.LMT: Get the value of weightTrimBeta.
getWeightTrimBeta() - Method in class weka.classifiers.trees.lmt.LogisticBase: Get the value of weightTrimBeta.
getWholeDataErr() - Method in class weka.classifiers.rules.Ridor
getWidth() - Method in class weka.gui.beans.BeanInstance: Gets the width of this bean
getWindow(Class) - Method in class weka.gui.Main: returns the first instance of the given window class, null if none can be found.
getWindow(String) - Method in class weka.gui.Main: returns the first window with the given title, null if none can be found.
getWindowList() - Method in class weka.gui.Main: returns all currently open frames.
getWindowSize() - Method in class weka.classifiers.lazy.IBk: Gets the maximum number of instances allowed in the training pool.
getWithPrefix(String) - Method in class weka.core.Trie: returns all stored strings that match the given prefix
getWords() - Method in class weka.core.CheckScheme: returns the words used for assembling strings in a comma-separated list.
getWords() - Method in class weka.core.TestInstances: returns the words used for assembling strings in a comma-separated list.
getWordSeparators() - Method in class weka.core.CheckScheme: returns the word separators (chars) to use for assembling strings.
getWordSeparators() - Method in class weka.core.TestInstances: returns the word separators (chars) to use for assembling strings.
getWordsToKeep() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets the number of words (per class if there is a class attribute assigned) to attempt to keep.
getWrappedAlgorithm() - Method in class weka.gui.beans.Associator: Returns the wrapped associator
getWrappedAlgorithm() - Method in class weka.gui.beans.Classifier: Returns the wrapped classifier
getWrappedAlgorithm() - Method in class weka.gui.beans.Clusterer: Returns the wrapped clusterer
getWrappedAlgorithm() - Method in class weka.gui.beans.Filter: Get the filter wrapped by this bean
getWrappedAlgorithm() - Method in class weka.gui.beans.Loader: Get the loader
getWrappedAlgorithm() - Method in class weka.gui.beans.Saver: Get the saver
getWrappedAlgorithm() - Method in interface weka.gui.beans.WekaWrapper: Get the algorithm
getWriteMode() - Method in class weka.core.converters.AbstractSaver: Gets the write mode.
getWriteMode() - Method in interface weka.core.converters.Saver: Gets the write mode
getWriteOPTICSresults() - Method in class weka.clusterers.OPTICS: Returns the flag for writing actions
getWriter() - Method in class weka.core.converters.AbstractFileSaver: Gets the writer
getWriter(String) - Method in class weka.gui.visualize.PrintableComponent: returns the JComponentWriter associated with the given name, is null if not found.
getWriter(String) - Method in interface weka.gui.visualize.PrintableHandler: returns the JComponentWriter associated with the given name, is null if not found
getWriter(String) - Method in class weka.gui.visualize.PrintablePanel: returns the JComponentWriter associated with the given name, is null if not found
getWriters() - Method in class weka.gui.visualize.PrintableComponent: returns a Hashtable with the current available JComponentWriters in the save dialog.
getWriters() - Method in interface weka.gui.visualize.PrintableHandler: returns a Hashtable with the current available JComponentWriters in the save dialog.
getWriters() - Method in class weka.gui.visualize.PrintablePanel: returns a Hashtable with the current available JComponentWriters in the save dialog.
getWVariance() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated variance according to the Webb definition
getX() - Method in class weka.classifiers.functions.neural.NeuralConnection
getX() - Method in class weka.gui.beans.BeanInstance: Gets the x coordinate of this bean
getXBase() - Method in class weka.classifiers.meta.GridSearch: Get the value of the base for X.
getXExpression() - Method in class weka.classifiers.meta.GridSearch: Get the expression for the X value.
getXindex() - Method in class weka.gui.visualize.PlotData2D: Get the currently set x index of the data
getXIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute on the x axis
getXLabelFreq() - Method in class weka.gui.beans.StripChart: Get the frequency by which x axis values are printed
getXMax() - Method in class weka.classifiers.meta.GridSearch: Get the value of the Maximum of X.
getXMin() - Method in class weka.classifiers.meta.GridSearch: Get the value of the minimum of X.
getXMLDocument() - Method in class weka.core.xml.XMLOptions: returns the handler of the XML document.
getXProperty() - Method in class weka.classifiers.meta.GridSearch: Get the X property to test (normally the filter).
getXScale() - Method in class weka.gui.visualize.JComponentWriter: returns the scale factor for the x-axis
getXScale() - Method in class weka.gui.visualize.PrintableComponent: returns the scale factor for the x-axis.
getXScale() - Method in interface weka.gui.visualize.PrintableHandler: returns the scale factor for the x-axis
getXScale() - Method in class weka.gui.visualize.PrintablePanel: returns the scale factor for the x-axis
getXStep() - Method in class weka.classifiers.meta.GridSearch: Get the value of the step size for X.
getY() - Method in class weka.classifiers.functions.neural.NeuralConnection
getY() - Method in class weka.gui.beans.BeanInstance: Gets the y coordinate of this bean
getYBase() - Method in class weka.classifiers.meta.GridSearch: Get the value of the base for Y.
getYExpression() - Method in class weka.classifiers.meta.GridSearch: Get the expression for the Y value.
getYindex() - Method in class weka.gui.visualize.PlotData2D: Get the currently set y index of the data
getYIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute on the y axis
getYMax() - Method in class weka.classifiers.meta.GridSearch: Get the value of the Maximum of Y.
getYMin() - Method in class weka.classifiers.meta.GridSearch: Get the value of the minimum of Y.
getYProperty() - Method in class weka.classifiers.meta.GridSearch: Get the Y property (normally the classifier).
getYScale() - Method in class weka.gui.visualize.JComponentWriter: returns the scale factor for the y-axis
getYScale() - Method in class weka.gui.visualize.PrintableComponent: returns the scale factor for the y-axis.
getYScale() - Method in interface weka.gui.visualize.PrintableHandler: returns the scale factor for the y-axis
getYScale() - Method in class weka.gui.visualize.PrintablePanel: returns the scale factor for the y-axis
getYStep() - Method in class weka.classifiers.meta.GridSearch: Get the value of the step size for Y.
globalBlendTipText() - Method in class weka.classifiers.lazy.KStar: Returns the tip text for this property
globalInfo() - Method in class weka.associations.Apriori: Returns a string describing this associator
globalInfo() - Method in class weka.associations.FilteredAssociator: Returns a string describing this Associator
globalInfo() - Method in class weka.associations.FPGrowth: Returns a string describing this associator
globalInfo() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns global information about the algorithm.
globalInfo() - Method in class weka.associations.PredictiveApriori: Returns a string describing this associator
globalInfo() - Method in class weka.associations.Tertius: Returns a string describing this associator.
globalInfo() - Method in class weka.attributeSelection.BestFirst: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.CfsSubsetEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ConsistencySubsetEval: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
globalInfo() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.FilteredAttributeEval
globalInfo() - Method in class weka.attributeSelection.FilteredSubsetEval
globalInfo() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.GeneticSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.GreedyStepwise: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.LinearForwardSelection: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.PrincipalComponents: Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.RaceSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.RandomSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.Ranker: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.RankSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ScatterSearchV1: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.bayes.AODE: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.AODEsr: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.BayesianLogisticRegression
globalInfo() - Method in class weka.classifiers.bayes.BayesNet: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.DMNBtext: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.HNB: Returns a string describing this classifier.
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayes: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesSimple: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.net.BIFReader: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: This will return a string describing the class.
globalInfo() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator: Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.search.ci.CISearchAlgorithm: Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.search.fixed.NaiveBayes: Returns a string describing this object
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.K2: This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.global.TAN: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.K2: This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: This will return a string describing the search algorithm.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.net.search.local.TAN: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.WAODE: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.BVDecompose: Returns a string describing this object
globalInfo() - Method in class weka.classifiers.BVDecomposeSegCVSub: Returns a string describing this object
globalInfo() - Method in class weka.classifiers.functions.GaussianProcesses: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.IsotonicRegression: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.LeastMedSq: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.LibLINEAR: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.LibSVM: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.LinearRegression: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.Logistic: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.MultilayerPerceptron: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.functions.PaceRegression: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.PLSClassifier: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.RBFNetwork: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.SimpleLogistic: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.SMO: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.SMOreg: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.SPegasos: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel: Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.Puk: Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.supportVector.RegSMO: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Returns a string describing the object
globalInfo() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns a string describing the kernel
globalInfo() - Method in class weka.classifiers.functions.VotedPerceptron: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.Winnow: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.IB1: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.IBk: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.lazy.KStar: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.LBR
globalInfo() - Method in class weka.classifiers.lazy.LWL: Returns a string describing classifier.
globalInfo() - Method in class weka.classifiers.meta.AdaBoostM1: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.AdditiveRegression: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.meta.Bagging: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.ClassificationViaClustering: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.ClassificationViaRegression: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.CostSensitiveClassifier
globalInfo() - Method in class weka.classifiers.meta.CVParameterSelection: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.meta.Dagging: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.Decorate: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.END: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.FilteredClassifier: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.meta.Grading: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.GridSearch: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.LogitBoost: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MetaCost: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MultiBoostAB: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MultiClassClassifier
globalInfo() - Method in class weka.classifiers.meta.MultiScheme: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND
globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND
globalInfo() - Method in class weka.classifiers.meta.nestedDichotomies.ND
globalInfo() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
globalInfo() - Method in class weka.classifiers.meta.RandomCommittee: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.RandomSubSpace: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.RotationForest: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.Stacking: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.StackingC: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.ThresholdSelector
globalInfo() - Method in class weka.classifiers.meta.Vote: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.mi.CitationKNN: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MDD: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIBoost: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIDD: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIEMDD: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MILR: Returns the tip text for this property
globalInfo() - Method in class weka.classifiers.mi.MINND: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIOptimalBall: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MISMO: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.mi.MISVM: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.MIWrapper: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.mi.SimpleMI: Returns a string describing this filter
globalInfo() - Method in class weka.classifiers.misc.HyperPipes: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.misc.SerializedClassifier: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.misc.VFI: Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.DecisionTable: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.DTNB: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.JRip: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.M5Rules: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.NNge: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.OneR: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.PART: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.Prism: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.Ridor: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.ZeroR: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.ADTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.BFTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.DecisionStump: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.FT: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.Id3: Returns a string describing the classifier.
globalInfo() - Method in class weka.classifiers.trees.J48: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.J48graft: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.LADTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.LMT: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.m5.M5Base: returns information about the classifier
globalInfo() - Method in class weka.classifiers.trees.NBTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.RandomForest: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.RandomTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.REPTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.SimpleCart: Return a description suitable for displaying in the explorer/experimenter.
globalInfo() - Method in class weka.classifiers.trees.UserClassifier: This will return a string describing the classifier.
globalInfo() - Method in class weka.clusterers.CLOPE: Returns a string describing this DataMining-Algorithm
globalInfo() - Method in class weka.clusterers.Cobweb: Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.DBSCAN: Returns a string describing this DataMining-Algorithm
globalInfo() - Method in class weka.clusterers.EM: Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.FarthestFirst: Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.FilteredClusterer: Returns a string describing this clusterer.
globalInfo() - Method in class weka.clusterers.HierarchicalClusterer: This will return a string describing the clusterer.
globalInfo() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns a string describing classifier
globalInfo() - Method in class weka.clusterers.OPTICS: Returns a string describing this DataMining-Algorithm
globalInfo() - Method in class weka.clusterers.sIB: Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.SimpleKMeans: Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.XMeans: Returns a string describing this clusterer.
globalInfo() - Method in class weka.core.ChebyshevDistance: Returns a string describing this object.
globalInfo() - Method in class weka.core.converters.ArffLoader: Returns a string describing this Loader
globalInfo() - Method in class weka.core.converters.ArffSaver: Returns a string describing this Saver
globalInfo() - Method in class weka.core.converters.C45Loader: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.core.converters.C45Saver: Returns a string describing this Saver
globalInfo() - Method in class weka.core.converters.CSVLoader: Returns a string describing this attribute evaluator.
globalInfo() - Method in class weka.core.converters.CSVSaver: Returns a string describing this Saver
globalInfo() - Method in class weka.core.converters.DatabaseLoader: Returns a string describing this Loader
globalInfo() - Method in class weka.core.converters.DatabaseSaver: Returns a string describing this Saver.
globalInfo() - Method in class weka.core.converters.LibSVMLoader: Returns a string describing this Loader.
globalInfo() - Method in class weka.core.converters.LibSVMSaver: Returns a string describing this Saver
globalInfo() - Method in class weka.core.converters.SerializedInstancesLoader: Returns a string describing this object
globalInfo() - Method in class weka.core.converters.SerializedInstancesSaver: Returns a string describing this Saver.
globalInfo() - Method in class weka.core.converters.SVMLightLoader: Returns a string describing this Loader.
globalInfo() - Method in class weka.core.converters.SVMLightSaver: Returns a string describing this Saver.
globalInfo() - Method in class weka.core.converters.TextDirectoryLoader: Returns a string describing this loader
globalInfo() - Method in class weka.core.converters.XRFFLoader: Returns a string describing this Loader
globalInfo() - Method in class weka.core.converters.XRFFSaver: Returns a string describing this Saver
globalInfo() - Method in class weka.core.EditDistance: Returns a string describing this object.
globalInfo() - Method in class weka.core.EuclideanDistance: Returns a string describing this object.
globalInfo() - Method in class weka.core.ManhattanDistance: Returns a string describing this object.
globalInfo() - Method in class weka.core.neighboursearch.BallTree: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.BottomUpConstructor: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren: Returns a string describing this object.
globalInfo() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.CoverTree: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.KDTree: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.LinearNNSearch: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns a string describing this nearest neighbour search algorithm.
globalInfo() - Method in class weka.core.NormalizableDistance: Returns a string describing this object.
globalInfo() - Method in class weka.core.stemmers.IteratedLovinsStemmer: Returns a string describing the stemmer
globalInfo() - Method in class weka.core.stemmers.LovinsStemmer: Returns a string describing the stemmer
globalInfo() - Method in class weka.core.stemmers.NullStemmer: Returns a string describing the stemmer
globalInfo() - Method in class weka.core.stemmers.SnowballStemmer: Returns a string describing the stemmer.
globalInfo() - Method in class weka.core.tokenizers.AlphabeticTokenizer: Returns a string describing the stemmer
globalInfo() - Method in class weka.core.tokenizers.NGramTokenizer: Returns a string describing the stemmer
globalInfo() - Method in class weka.core.tokenizers.Tokenizer: Returns a string describing the stemmer
globalInfo() - Method in class weka.core.tokenizers.WordTokenizer: Returns a string describing the stemmer
globalInfo() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.classification.LED24: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.regression.Expression: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.ClusterDefinition: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns a string describing this data generator.
globalInfo() - Method in class weka.experiment.AveragingResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CrossValidationResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.CSVResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.DatabaseResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.DatabaseResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.InstancesResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.LearningRateResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RandomSplitResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RegressionSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.filters.AllFilter: Returns a string describing this filter
globalInfo() - Method in class weka.filters.MultiFilter: Returns a string describing this filter
globalInfo() - Method in class weka.filters.SimpleFilter: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.supervised.attribute.AddClassification: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.Discretize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.supervised.instance.Resample: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.supervised.instance.SMOTE: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Add: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddID: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddValues: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Center: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.ClassAssigner: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Copy: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
globalInfo() - Method in class weka.filters.unsupervised.attribute.MathExpression: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NominalToString: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Obfuscate: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Remove: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Reorder: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Standardize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns a string describing this filter.
globalInfo() - Method in class weka.filters.unsupervised.attribute.SwapValues: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.attribute.Wavelet: Returns a string describing this classifier.
globalInfo() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.Normalize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.Randomize: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.instance.Resample: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Returns a string describing this filter.
globalInfo() - Method in class weka.gui.beans.Associator: Global info (if it exists) for the wrapped classifier
globalInfo() - Method in class weka.gui.beans.AttributeSummarizer: Global info for this bean
globalInfo() - Method in class weka.gui.beans.ClassAssigner: Global info for this bean
globalInfo() - Method in class weka.gui.beans.Classifier: Global info (if it exists) for the wrapped classifier
globalInfo() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Global info for this bean
globalInfo() - Method in class weka.gui.beans.ClassValuePicker: Global info for this bean
globalInfo() - Method in class weka.gui.beans.Clusterer: Global info (if it exists) for the wrapped classifier
globalInfo() - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Global info for this bean
globalInfo() - Method in class weka.gui.beans.CostBenefitAnalysis: Global info for this bean
globalInfo() - Method in class weka.gui.beans.CrossValidationFoldMaker: Global info for this bean
globalInfo() - Method in class weka.gui.beans.DataVisualizer: Global info for this bean
globalInfo() - Method in class weka.gui.beans.Filter: Global info (if it exists) for the wrapped filter
globalInfo() - Method in class weka.gui.beans.GraphViewer: Global info for this bean
globalInfo() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Global info for this bean
globalInfo() - Method in class weka.gui.beans.Loader: Global info (if it exists) for the wrapped loader
globalInfo() - Method in class weka.gui.beans.ModelPerformanceChart: Global info for this bean
globalInfo() - Method in class weka.gui.beans.PredictionAppender: Global description of this bean
globalInfo() - Method in class weka.gui.beans.Saver: Global info (if it exists) for the wrapped loader
globalInfo() - Method in class weka.gui.beans.ScatterPlotMatrix: Global info for this bean
globalInfo() - Method in class weka.gui.beans.SerializedModelSaver: Global info for this bean.
globalInfo() - Method in class weka.gui.beans.StripChart: Global info for this bean
globalInfo() - Method in class weka.gui.beans.TestSetMaker: Global info for this bean
globalInfo() - Method in class weka.gui.beans.TextViewer: Global info for this bean
globalInfo() - Method in class weka.gui.beans.TrainingSetMaker: Global info for this bean
globalInfo() - Method in class weka.gui.beans.TrainTestSplitMaker: Global info for this bean
globalInfo() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Returns a string describing this tool
GLOBALINFO_ENDTAG - Static variable in class weka.core.GlobalInfoJavadoc: the end comment tag for inserting the generated Javadoc
GLOBALINFO_METHOD - Static variable in class weka.core.GlobalInfoJavadoc: the globalInfo method name
GLOBALINFO_STARTTAG - Static variable in class weka.core.GlobalInfoJavadoc: the start comment tag for inserting the generated Javadoc
GlobalInfoJavadoc - Class in weka.core: Generates Javadoc comments from the class's globalInfo method.
GlobalInfoJavadoc() - Constructor for class weka.core.GlobalInfoJavadoc: default constructor
GlobalScoreSearchAlgorithm - Class in weka.classifiers.bayes.net.search.global: This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.
GlobalScoreSearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
goDown(String) - Method in class weka.gui.HierarchyPropertyParser: Go to a certain node of the tree down from the current node according to the specified relative path.
GOEPanel() - Constructor for class weka.gui.GenericObjectEditor.GOEPanel: Creates the GUI editor component.
GOETreeNode() - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode: Creates a tree node that has no parent and no children, but which allows children.
GOETreeNode(Object) - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode: Creates a tree node with no parent, no children, but which allows children, and initializes it with the specified user object.
GOETreeNode(Object, boolean) - Constructor for class weka.gui.GenericObjectEditor.GOETreeNode: Creates a tree node with no parent, no children, initialized with the specified user object, and that allows children only if specified.
goTo(String) - Method in class weka.gui.HierarchyPropertyParser: Go to a certain node of the tree according to the specified path Note that the path must be absolute path from the root.
goToChild(String) - Method in class weka.gui.HierarchyPropertyParser: Go to one child node from the current position in the tree according to the given value
If the child node with the given value cannot be found it returns false, true otherwise.
goToChild(int) - Method in class weka.gui.HierarchyPropertyParser: Go to one child node from the current position in the tree according to the given position
goToParent() - Method in class weka.gui.HierarchyPropertyParser: Go to the parent from the current position in the tree If the current position is the root, it stays there and does not move
goToRoot() - Method in class weka.gui.HierarchyPropertyParser: Go to the root of the tree
gr(double, double) - Static method in class weka.core.Utils: Tests if a is greater than b.
Grading - Class in weka.classifiers.meta: Implements Grading.
Grading() - Constructor for class weka.classifiers.meta.Grading
GraftSplit - Class in weka.classifiers.trees.j48: Class implementing a split for nodes added to a tree during grafting.
GraftSplit(int, double, int, double, double) - Constructor for class weka.classifiers.trees.j48.GraftSplit: constructor
GraftSplit(int, double, int, double, double[][]) - Constructor for class weka.classifiers.trees.j48.GraftSplit: constructor
graph(FPGrowth.FPTreeRoot) - Method in class weka.associations.FPGrowth: Assemble a dot graph representation of the FP-tree.
graph() - Method in class weka.classifiers.bayes.BayesNet: Returns a BayesNet graph in XMLBIF ver 0.3 format.
graph() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.CVParameterSelection: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.FilteredClassifier: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.ThresholdSelector: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.trees.ADTree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.ft.FTtree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.FT: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.J48: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.J48graft: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.LADTree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.LMT: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns graph describing the tree.
graph(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode: Assign a unique identifier to each node in the tree and then calls graphTree
graph() - Method in class weka.classifiers.trees.M5P: Return a dot style String describing the tree.
graph() - Method in class weka.classifiers.trees.NBTree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.RandomTree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.REPTree: Outputs the decision tree as a graph
graph() - Method in class weka.classifiers.trees.UserClassifier
graph() - Method in class weka.clusterers.Cobweb: Generates the graph string of the Cobweb tree
graph() - Method in class weka.clusterers.HierarchicalClusterer
graph() - Method in interface weka.core.Drawable: Returns a string that describes a graph representing the object.
GraphConstants - Interface in weka.gui.graphvisualizer: GraphConstants.java
GraphEdge - Class in weka.gui.graphvisualizer: This class represents an edge in the graph
GraphEdge(int, int, int) - Constructor for class weka.gui.graphvisualizer.GraphEdge
GraphEdge(int, int, int, String, String) - Constructor for class weka.gui.graphvisualizer.GraphEdge
GraphEvent - Class in weka.gui.beans: Event for graphs
GraphEvent(Object, String, String, int) - Constructor for class weka.gui.beans.GraphEvent: Creates a new GraphEvent instance.
GraphListener - Interface in weka.gui.beans: Describe interface TextListener here.
GraphNode - Class in weka.gui.graphvisualizer: This class represents a node in the Graph.
GraphNode(String, String) - Constructor for class weka.gui.graphvisualizer.GraphNode: Constructor
GraphNode(String, String, int) - Constructor for class weka.gui.graphvisualizer.GraphNode: Constructor
GraphPanel - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI: GraphPanel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 16, 2004
Time: 10:28:19 AM
$ Revision 1.4 $
GraphPanel(FastVector, int, boolean, boolean) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel
graphType() - Method in class weka.classifiers.bayes.BayesNet: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.CVParameterSelection: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.FilteredClassifier: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.ThresholdSelector: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.ADTree: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.FT: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.J48: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.J48graft: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.LADTree: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.LMT: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.M5P: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.NBTree: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.RandomTree: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.REPTree: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.UserClassifier: Returns the type of graph this classifier represents.
graphType() - Method in class weka.clusterers.Cobweb: Returns the type of graphs this class represents
graphType() - Method in class weka.clusterers.HierarchicalClusterer
graphType() - Method in interface weka.core.Drawable: Returns the type of graph representing the object.
GraphViewer - Class in weka.gui.beans: A bean encapsulating weka.gui.treevisualize.TreeVisualizer
GraphViewer() - Constructor for class weka.gui.beans.GraphViewer
GraphViewerBeanInfo - Class in weka.gui.beans: Bean info class for the graph viewer
GraphViewerBeanInfo() - Constructor for class weka.gui.beans.GraphViewerBeanInfo
GraphVisualizePlugin - Interface in weka.gui.visualize.plugins: Interface implemented by classes loaded dynamically to visualize graphs in the explorer.
GraphVisualizer - Class in weka.gui.graphvisualizer: This class displays the graph we want to visualize.
GraphVisualizer() - Constructor for class weka.gui.graphvisualizer.GraphVisualizer: Constructor
Sets up the gui and initializes all the other previously uninitialized variables.
GreedyStepwise - Class in weka.attributeSelection: GreedyStepwise :

Performs a greedy forward or backward search through the space of attribute subsets.
GreedyStepwise() - Constructor for class weka.attributeSelection.GreedyStepwise: Constructor
GRID - Static variable in class weka.datagenerators.clusterers.BIRCHCluster: Constant set for choice of pattern.
gridIsExtendableTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
GridSearch - Class in weka.classifiers.meta: Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting.

The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).
GridSearch() - Constructor for class weka.classifiers.meta.GridSearch: the default constructor
grOrEq(double, double) - Static method in class weka.core.Utils: Tests if a is greater or equal to b.
grouping(boolean) - Method in class weka.core.matrix.FlexibleDecimalFormat
grow(Instances) - Method in class weka.classifiers.rules.JRip.RipperRule: Build one rule using the growing data
grow(Instances) - Method in class weka.classifiers.rules.Rule: Build this rule
GT - Static variable in interface weka.core.mathematicalexpression.sym
GT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
GUI - Class in weka.classifiers.bayes.net: GUI interface to Bayesian Networks.
GUI() - Constructor for class weka.classifiers.bayes.net.GUI: Constructor
Sets up the gui and initializes all the other previously uninitialized variables.
GUI_MDI - Static variable in class weka.gui.Main: displays the GUI as MDI.
GUI_SDI - Static variable in class weka.gui.Main: displays the GUI as SDI.
GUIChooser - Class in weka.gui: The main class for the Weka GUIChooser.
GUIChooser() - Constructor for class weka.gui.GUIChooser: Creates the experiment environment gui with no initial experiment
GUIChooser.ChildFrameSDI - Class in weka.gui: Specialized JFrame class.
GUIEDITORS_PROPERTY_FILE - Static variable in class weka.gui.GenericObjectEditor: the properties files containing the class/editor mappings.
GUITipText() - Method in class weka.classifiers.functions.MultilayerPerceptron

H

h(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of h(x) given the mixture.
h(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of h(x) given the mixture, where x is a vector.
h(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Computes the value of h(x) given the mixture.
h(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Computes the value of h(x) given the mixture, where x is a vector.
h1(int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Constructs single Householder transformation for a column
h2(int, int, double, PaceMatrix, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Performs single Householder transformation on one column of a matrix
handles(Capabilities.Capability) - Method in class weka.core.Capabilities: returns true if the classifier handler has the specified capability
handles(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities: returns true if the given capability can be handled.
hasAdditional() - Method in class weka.core.TechnicalInformation: returns true if there are further technical informations stored in this
hasAntds() - Method in class weka.classifiers.rules.ConjunctiveRule: Whether this rule has antecedents, i.e.
hasAntds() - Method in class weka.classifiers.rules.JRip.RipperRule: Whether this rule has antecedents, i.e.
hasAntds() - Method in class weka.classifiers.rules.Rule: Whether this rule has antecedents, i.e.
hasClasspathProblems() - Method in class weka.core.CheckScheme: returns TRUE if the classifier returned a "not in classpath" Exception
hasClasspathProblems() - Method in class weka.estimators.CheckEstimator: returns TRUE if the estimator returned a "not in classpath" Exception
hasDependencies() - Method in class weka.core.Capabilities: Checks whether there are any dependencies at all
hasDependency(Capabilities.Capability) - Method in class weka.core.Capabilities: returns true if the classifier handler has a dependency for the specified capability
hasFalseHead() - Method in class weka.associations.tertius.Rule: Test if the head of the rule is false.
hash - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: attribute value hash code
hashCode() - Method in class weka.associations.FPGrowth.BinaryItem
hashCode() - Method in class weka.associations.ItemSet: Produces a hash code for a item set.
hashCode() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Calculates a hash code
hashCode() - Method in class weka.classifiers.rules.DecisionTableHashKey: Calculates a hash code
hashCode() - Method in class weka.core.SerializedObject: Returns a hashcode for this object.
hashCode() - Method in class weka.core.Trie: Returns the hash code value for this collection.
hashKey(Instance, int) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey: Constructor for a hashKey
hashKey(double[]) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey: Constructor for a hashKey
hasIncomingBatchInstances() - Method in class weka.gui.beans.Classifier: Returns true if this classifier has an incoming connection that is a batch set of instances
hasIncomingBatchInstances() - Method in class weka.gui.beans.Clusterer: Returns true if this clusterer has an incoming connection that is a batch set of instances
hasIncomingStreamInstances() - Method in class weka.gui.beans.Classifier: Returns true if this classifier has an incoming connection that is an instance stream
hasIndex() - Method in class weka.core.PropertyPath.PathElement: returns whether the property is an index-based one
hasInterface(String, String) - Static method in class weka.core.ClassDiscovery: Checks whether the given class implements the given interface.
hasInterface(Class, Class) - Static method in class weka.core.ClassDiscovery: Checks whether the given class implements the given interface.
hasMaxCounterInstances() - Method in class weka.associations.tertius.LiteralSet: Test if all the intances are counter-instances.
hasMaxRows() - Method in class weka.gui.sql.ResultSetHelper: whether a limit on the rows to retrieve was set.
hasMissingValue() - Method in class weka.core.Instance: Tests whether an instance has a missing value.
hasModels() - Method in class weka.classifiers.trees.ft.FTtree: Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
hasModels() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
hasMoreElements(Instances) - Method in class weka.core.converters.ConverterUtils.DataSource: returns whether there are more Instance objects in the data.
hasMoreElements() - Method in class weka.core.FastVector.FastVectorEnumeration: Tests if there are any more elements to enumerate.
hasMoreElements() - Method in class weka.core.tokenizers.AlphabeticTokenizer: returns whether there are more elements still
hasMoreElements() - Method in class weka.core.tokenizers.NGramTokenizer: returns true if there's more elements available
hasMoreElements() - Method in class weka.core.tokenizers.Tokenizer: Tests if this enumeration contains more elements.
hasMoreElements() - Method in class weka.core.tokenizers.WordTokenizer: Tests if this enumeration contains more elements.
hasMoreIterations() - Method in class weka.experiment.Experiment: Returns true if there are more iterations to carry out in the experiment.
hasNext() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
hasNext() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue: Tests, if the queue has some more elements left
hasNext() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue: Tests, if the queue has some more elements left
hasNext() - Method in class weka.core.Trie.TrieIterator: Returns true if the iteration has more elements.
hasPrevious() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
hasResult() - Method in class weka.gui.sql.event.QueryExecuteEvent: whether a ResultSet was produced, e.g.
hasTargetMetaData() - Method in class weka.core.pmml.MiningSchema: Returns true if there is Target meta data.
hasTrueBody() - Method in class weka.associations.tertius.Rule: Test if the body of the rule is true.
hasUID(String) - Static method in class weka.core.SerializationHelper: checks whether the given class contains a serialVersionUID.
hasUID(Class) - Static method in class weka.core.SerializationHelper: checks whether the given class contains a serialVersionUID.
hasZeropoint() - Method in class weka.core.Attribute: Returns whether the attribute has a zeropoint and may be added meaningfully.
HDRankTipText() - Method in class weka.classifiers.mi.CitationKNN: Returns the tip text for this property
Head - Class in weka.associations.tertius: Class representing the head of a rule.
Head() - Constructor for class weka.associations.tertius.Head: Constructor without storing the counter-instances.
Head(Instances) - Constructor for class weka.associations.tertius.Head: Constructor storing the counter-instances.
headContains(Literal) - Method in class weka.associations.tertius.Rule: Test if the head of the rule contains a literal.
header(int) - Method in class weka.experiment.PairedTTester: Creates a "header" string describing the current resultsets.
header(int) - Method in interface weka.experiment.Tester: Creates a "header" string describing the current resultsets.
headerKeys() - Method in class weka.experiment.ResultMatrix: returns an enumeration of the header keys
HEIGHT - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel: default height
HEIGHT - Static variable in class weka.gui.sql.SqlViewer: the height property in the history file.
heuristicStopTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
heuristicTipText() - Method in class weka.classifiers.trees.BFTree: Returns the tip text for this property
heuristicTipText() - Method in class weka.classifiers.trees.SimpleCart: Returns the tip text for this property
hf(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of h(x) / f(x) given the mixture.
hf(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Computes the value of h(x) / f(x) given the mixture.
hiddenLayersTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
HierarchicalBCEngine - Class in weka.gui.graphvisualizer: This class lays out the vertices of a graph in a hierarchy of vertical levels, with a number of nodes in each level.
HierarchicalBCEngine(FastVector, FastVector, int, int) - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine: Constructor - takes in FastVectors of nodes and edges, and the initial width and height of a node
HierarchicalBCEngine(FastVector, FastVector, int, int, boolean) - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine: Constructor - takes in FastVectors of nodes and edges, the initial width and height of a node, and a boolean value to indicate if the edges should be concentrated.
HierarchicalBCEngine() - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine: SimpleConstructor If we want to instantiate the class first, and if information for nodes and edges is not available.
HierarchicalClusterer - Class in weka.clusterers: Hierarchical clustering class.
HierarchicalClusterer() - Constructor for class weka.clusterers.HierarchicalClusterer
HierarchyPropertyParser - Class in weka.gui: This class implements a parser to read properties that have a hierarchy(i.e.
HierarchyPropertyParser() - Constructor for class weka.gui.HierarchyPropertyParser: Default constructor
HierarchyPropertyParser(String, String) - Constructor for class weka.gui.HierarchyPropertyParser: Constructor that builds a tree from the given property with the given delimitor
HierarchyVisualizer - Class in weka.gui.hierarchyvisualizer
HierarchyVisualizer(String) - Constructor for class weka.gui.hierarchyvisualizer.HierarchyVisualizer
HillClimber - Class in weka.classifiers.bayes.net.search.global: This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
HillClimber() - Constructor for class weka.classifiers.bayes.net.search.global.HillClimber
HillClimber - Class in weka.classifiers.bayes.net.search.local: This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
HillClimber() - Constructor for class weka.classifiers.bayes.net.search.local.HillClimber
HISTORY_NAME - Static variable in class weka.gui.sql.ConnectionPanel: the name of the history.
HISTORY_NAME - Static variable in class weka.gui.sql.QueryPanel: the name of the history.
historyChanged(HistoryChangedEvent) - Method in interface weka.gui.sql.event.HistoryChangedListener: This method gets called when a history is modified.
historyChanged(HistoryChangedEvent) - Method in class weka.gui.sql.SqlViewer: This method gets called when a history is modified.
HistoryChangedEvent - Class in weka.gui.sql.event: An event that is generated when a history is modified.
HistoryChangedEvent(Object, String, DefaultListModel) - Constructor for class weka.gui.sql.event.HistoryChangedEvent: constructs the event
HistoryChangedListener - Interface in weka.gui.sql.event: A listener for changes in a history.
hit(Rectangle, Shape, boolean) - Method in class weka.gui.visualize.PostscriptGraphics
HLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
HNB - Class in weka.classifiers.bayes: Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC.

For more information refer to:

H.
HNB() - Constructor for class weka.classifiers.bayes.HNB
holdOutFileTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the tip text for this property
HoldOutSubsetEvaluator - Class in weka.attributeSelection: Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
HoldOutSubsetEvaluator() - Constructor for class weka.attributeSelection.HoldOutSubsetEvaluator
hornClausesTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
HostListPanel - Class in weka.gui.experiment: This panel controls setting a list of hosts for a RemoteExperiment to use.
HostListPanel(RemoteExperiment) - Constructor for class weka.gui.experiment.HostListPanel: Creates the host list panel with the given experiment.
HostListPanel() - Constructor for class weka.gui.experiment.HostListPanel: Create the host list panel initially disabled.
HyperparameterRange - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: CV Hyperparameter Range
hyperparameterRangeTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
Hyperparameters - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Array to store Hyperparameter values for each feature.
HyperparameterSelection - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Hyperparameter selection method
hyperparameterSelectionTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
HyperparameterValue - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Best hyperparameter for test phase
hyperparameterValueTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
HyperPipes - Class in weka.classifiers.misc: Class implementing a HyperPipe classifier.
HyperPipes() - Constructor for class weka.classifiers.misc.HyperPipes
hypot(double, double) - Static method in class weka.core.matrix.Maths: sqrt(a^2 + b^2) without under/overflow.

I

I0 - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
I0a - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
I0b - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
I1 - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
I2 - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
I3 - Static variable in class weka.classifiers.functions.supportVector.RegSMOImproved
IB1 - Class in weka.classifiers.lazy: Nearest-neighbour classifier.
IB1() - Constructor for class weka.classifiers.lazy.IB1
IBk - Class in weka.classifiers.lazy: K-nearest neighbours classifier.
IBk(int) - Constructor for class weka.classifiers.lazy.IBk: IBk classifier.
IBk() - Constructor for class weka.classifiers.lazy.IBk: IB1 classifer.
ICON_PATH - Static variable in class weka.gui.beans.BeanVisual
ICSSearchAlgorithm - Class in weka.classifiers.bayes.net.search.ci: This Bayes Network learning algorithm uses conditional independence tests to find a skeleton, finds V-nodes and applies a set of rules to find the directions of the remaining arrows.
ICSSearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
Id3 - Class in weka.classifiers.trees: Class for constructing an unpruned decision tree based on the ID3 algorithm.
Id3() - Constructor for class weka.classifiers.trees.Id3
identity(int, int) - Static method in class weka.core.matrix.Matrix: Generate identity matrix
IDFTransformTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
IDIndexTipText() - Method in class weka.filters.unsupervised.attribute.AddID: Returns the tip text for this property
IDLE - Static variable in class weka.gui.beans.BeanInstance
IFELSE - Static variable in interface weka.core.mathematicalexpression.sym
ignoreClassTipText() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Returns the tip text for this property
ignored() - Method in class weka.core.xml.PropertyHandler: returns an enumeration of the stored display names and classes of properties to ignore.
NOTE: String and Class Objects are mixed in this enumeration, depending whether it is a global property to ignore or just one for a certain class!
ignoredAttributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns the tip text for this property
ignoredAttributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns the tip text for this property
ignoreRangeTipText() - Method in class weka.filters.unsupervised.attribute.MathExpression: Returns the tip text for this property
IMAGES - Static variable in class weka.gui.ComponentHelper: the default directories for images
IMPLICIT - Static variable in class weka.associations.Tertius: Way of handling missing values: max counterinstances
ImproveSolutions() - Method in class weka.attributeSelection.ScatterSearchV1: Improve the solutions previously combined by adding the attributes that improve that solution
Impurity - Class in weka.classifiers.trees.m5: Class for handling the impurity values when spliting the instances
Impurity(int, int, Instances, int) - Constructor for class weka.classifiers.trees.m5.Impurity: Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
includeClassTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
incompleteBeta(double, double, double) - Static method in class weka.core.Statistics: Returns the Incomplete Beta Function evaluated from zero to xx.
incompleteBetaFraction1(double, double, double) - Static method in class weka.core.Statistics: Continued fraction expansion #1 for incomplete beta integral.
incompleteBetaFraction2(double, double, double) - Static method in class weka.core.Statistics: Continued fraction expansion #2 for incomplete beta integral.
incompleteGamma(double, double) - Static method in class weka.core.Statistics: Returns the Incomplete Gamma function.
incompleteGammaComplement(double, double) - Static method in class weka.core.Statistics: Returns the Complemented Incomplete Gamma function.
incorrect() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of incorrect classifications (that is, for which an incorrect prediction was made).
incorrect() - Method in class weka.classifiers.Evaluation: Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incrCoordCount() - Method in class weka.core.neighboursearch.PerformanceStats: Increments the coordinate count (number of coordinates/attributes looked at).
increaseFrequency(int) - Method in class weka.associations.FPGrowth.BinaryItem: Increase the frequency of this item.
increaseFrequency() - Method in class weka.associations.FPGrowth.BinaryItem: Increment the frequency of this item.
incremental(double, int) - Method in class weka.classifiers.trees.m5.Impurity: Incrementally computes the impurirty values
INCREMENTAL - Static variable in interface weka.core.converters.Loader
INCREMENTAL - Static variable in interface weka.core.converters.Saver
IncrementalClassifierEvaluator - Class in weka.gui.beans: Bean that evaluates incremental classifiers
IncrementalClassifierEvaluator() - Constructor for class weka.gui.beans.IncrementalClassifierEvaluator
IncrementalClassifierEvaluatorBeanInfo - Class in weka.gui.beans: Bean info class for the incremental classifier evaluator bean
IncrementalClassifierEvaluatorBeanInfo() - Constructor for class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
IncrementalClassifierEvaluatorCustomizer - Class in weka.gui.beans: GUI Customizer for the incremental classifier evaluator bean
IncrementalClassifierEvaluatorCustomizer() - Constructor for class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer
IncrementalClassifierEvent - Class in weka.gui.beans: Class encapsulating an incrementally built classifier and current instance
IncrementalClassifierEvent(Object, Classifier, Instance, int) - Constructor for class weka.gui.beans.IncrementalClassifierEvent: Creates a new IncrementalClassifierEvent instance.
IncrementalClassifierEvent(Object, Classifier, Instances) - Constructor for class weka.gui.beans.IncrementalClassifierEvent: Creates a new incremental classifier event that encapsulates header information and classifier.
IncrementalClassifierEvent(Object) - Constructor for class weka.gui.beans.IncrementalClassifierEvent
IncrementalClassifierListener - Interface in weka.gui.beans: Interface to something that can process a IncrementalClassifierEvent
IncrementalConverter - Interface in weka.core.converters: Marker interface for a loader/saver that can retrieve instances incrementally
IncrementalEstimator - Interface in weka.estimators: Interface for an incremental probability estimators.
incrIntNodeCount() - Method in class weka.core.neighboursearch.TreePerformanceStats: Increments the internal node count.
incrLeafCount() - Method in class weka.core.neighboursearch.TreePerformanceStats: Increments the leaf count.
incrPointCount() - Method in class weka.core.neighboursearch.PerformanceStats: Increments the point count (number of datapoints looked at).
index() - Method in class weka.core.Attribute: Returns the index of this attribute.
index(int) - Method in class weka.core.Instance: Returns the index of the attribute stored at the given position.
index(int) - Method in class weka.core.SparseInstance: Returns the index of the attribute stored at the given position.
INDEX_BEANCONNECTIONS - Static variable in class weka.gui.beans.xml.XMLBeans: the index in the Vector, where the BeanConnections are stored (Instances and Connections are stored in a Vector and then serialized)
INDEX_BEANINSTANCES - Static variable in class weka.gui.beans.xml.XMLBeans: the index in the Vector, where the BeanInstances are stored (Instances and Connections are stored in a Vector and then serialized)
Indexes(int, int, boolean, int) - Constructor for class weka.classifiers.lazy.LBR.Indexes: constructor
Indexes(LBR.Indexes) - Constructor for class weka.classifiers.lazy.LBR.Indexes: constructor
indexOf(Literal) - Method in class weka.associations.tertius.Predicate
indexOf(Object) - Method in class weka.core.FastVector: Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOf(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Searches for the first occurrence of elem.
indexOf(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Searches for the first occurrence of elem, beginning the search at index.
indexOfMax() - Method in class weka.core.matrix.DoubleVector: Returns the index of the maximum.
indexOfValue(String) - Method in class weka.core.Attribute: Returns the index of a given attribute value.
indexToString(int) - Static method in class weka.core.SingleIndex: Creates a string representation of the given index.
indicesToRangeList(int[]) - Static method in class weka.core.Range: Creates a string representation of the indices in the supplied array.
INDIVIDUAL_PROPERTY - Static variable in class weka.associations.tertius.IndividualLiteral
IndividualInstance - Class in weka.associations.tertius
IndividualInstance(Instance, Instances) - Constructor for class weka.associations.tertius.IndividualInstance
IndividualInstance(IndividualInstance) - Constructor for class weka.associations.tertius.IndividualInstance
IndividualInstances - Class in weka.associations.tertius
IndividualInstances(Instances, Instances) - Constructor for class weka.associations.tertius.IndividualInstances
IndividualLiteral - Class in weka.associations.tertius
IndividualLiteral(Predicate, String, int, int, int, int) - Constructor for class weka.associations.tertius.IndividualLiteral
individualPredictions(Instance) - Method in class weka.classifiers.meta.MultiClassClassifier: Returns the individual predictions of the base classifiers for an instance.
INFO - Static variable in class weka.core.Debug: the log level Info
info(int[]) - Static method in class weka.core.Utils: Computes entropy for an array of integers.
infoGain() - Method in class weka.classifiers.trees.j48.BinC45Split: Returns (C4.5-type) information gain for the generated split.
infoGain() - Method in class weka.classifiers.trees.j48.C45Split: Returns (C4.5-type) information gain for the generated split.
InfoGainAttributeEval - Class in weka.attributeSelection: InfoGainAttributeEval :

Evaluates the worth of an attribute by measuring the information gain with respect to the class.

InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).

Valid options are:
InfoGainAttributeEval() - Constructor for class weka.attributeSelection.InfoGainAttributeEval: Constructor
InfoGainSplitCrit - Class in weka.classifiers.trees.j48: Class for computing the information gain for a given distribution.
InfoGainSplitCrit() - Constructor for class weka.classifiers.trees.j48.InfoGainSplitCrit
InfoPanel - Class in weka.gui.sql: A simple panel for displaying information, e.g.
InfoPanel(JFrame) - Constructor for class weka.gui.sql.InfoPanel: creates the panel
InfoPanelCellRenderer - Class in weka.gui.sql: A specialized renderer that takes care of JLabels in a JList.
InfoPanelCellRenderer() - Constructor for class weka.gui.sql.InfoPanelCellRenderer: the constructor
Init(int, int) - Method in class weka.classifiers.bayes.net.BayesNetGenerator: Init defines a minimal Bayes net with no arcs
initAsNaiveBayesTipText() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
initClassifier(Instances) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
initClassifier(Instances) - Method in interface weka.classifiers.IterativeClassifier: Inits an iterative classifier.
initClassifier(Instances) - Method in class weka.classifiers.trees.ADTree: Sets up the tree ready to be trained, using two-class optimized method.
initClassifier(Instances) - Method in class weka.classifiers.trees.LADTree: Sets up the tree ready to be trained.
initCPTs() - Method in class weka.classifiers.bayes.BayesNet: initializes the conditional probabilities
initCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: initCPTs reserves space for CPTs and set all counts to zero
initCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: initCPTs reserves space for CPTs and set all counts to zero
initCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: initCPTs reserves space for CPTs and set all counts to zero
initCPTs(BayesNet) - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator: initCPTs reserves space for CPTs and set all counts to zero
initDebugVectorsInput() - Method in class weka.clusterers.XMeans: Initialises the debug vector input.
initFileClassIndexTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns the tip text for this property
initFileTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns the tip text for this property
initFilter(Instances) - Method in class weka.filters.unsupervised.attribute.KernelFilter: initializes the filter with the given dataset, i.e., the kernel gets built.
INITIAL_STEP - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
initialAnchorRandomTipText() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Returns the tip text for this property.
initialize() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: (1)Initialize m_Beta[j] to 0.
initialize() - Method in class weka.classifiers.CostMatrix: Initializes the matrix
initialize() - Method in class weka.classifiers.trees.j48.Distribution: Sets all counts to zero.
initialize(int, int, int) - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Resets the object of split information
initialize(int, int, int) - Method in class weka.classifiers.trees.m5.YongSplitInfo: Resets the object of split information
initialize() - Method in class weka.experiment.Experiment: Prepares an experiment for running, initializing current iterator settings.
initialize() - Method in class weka.experiment.RemoteExperiment: Prepares a remote experiment for running, creates sub experiments
initialize() - Method in class weka.gui.visualize.BMPWriter: further initialization
initialize() - Method in class weka.gui.visualize.JPEGWriter: further initialization.
initialize() - Method in class weka.gui.visualize.PNGWriter: further initialization
initializeDown(boolean) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
initializeRanges(int) - Method in class weka.core.Debug.DBO: Initialize ranges, upper limit must be set
initializeRanges() - Method in class weka.core.NormalizableDistance: Initializes the ranges using all instances of the dataset.
initializeRanges(int[]) - Method in class weka.core.NormalizableDistance: Initializes the ranges of a subset of the instances of this dataset.
initializeRanges(int[], int, int) - Method in class weka.core.NormalizableDistance: Initializes the ranges of a subset of the instances of this dataset.
initializeRangesEmpty(int, double[][]) - Method in class weka.core.NormalizableDistance: Used to initialize the ranges.
initializeUp() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
initInternalFields() - Method in class weka.gui.visualize.MatrixPanel: Initializes internal data fields, i.e.
InitPopulation(int) - Method in class weka.attributeSelection.ScatterSearchV1: Creating space for introducing the population
initStructure() - Method in class weka.classifiers.bayes.BayesNet: Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).
innerProduct(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Returns the inner product of two DoubleVectors
INPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is an input unit.
input(Instance) - Method in class weka.filters.AllFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.Filter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.SimpleBatchFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.SimpleStreamFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.AttributeSelection: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.ClassOrder: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.Discretize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.NominalToBinary: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.Resample: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.SMOTE: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.SpreadSubsample: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Add: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddCluster: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddExpression: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddID: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddNoise: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Center: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Copy: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Discretize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MathExpression: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NominalToString: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Normalize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Obfuscate: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Remove: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RemoveType: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Reorder: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Standardize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.StringToNominal: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.SwapValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.NonSparseToSparse: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.Normalize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.Randomize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveRange: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.Resample: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.ReservoirSample: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.SparseToNonSparse: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Input an instance for filtering.
input(Instance) - Method in class weka.gui.streams.InstanceCounter
input(Instance) - Method in class weka.gui.streams.InstanceJoiner
input(Instance) - Method in class weka.gui.streams.InstanceSavePanel
input(Instance) - Method in class weka.gui.streams.InstanceTable
input(Instance) - Method in class weka.gui.streams.InstanceViewer
inputCenterFileTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceCounter
inputFormat(Instances) - Method in class weka.gui.streams.InstanceJoiner: Sets the format of the input instances.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceSavePanel
inputFormat(Instances) - Method in class weka.gui.streams.InstanceTable
inputFormat(Instances) - Method in class weka.gui.streams.InstanceViewer
InputHyperparameterValues - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression: Set of values to be used as hyperparameter values during Cross-Validation.
inputOrderTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
inputs(Vector) - Static method in class weka.gui.beans.BeanConnection: Returns a vector of BeanInstances that can be considered as inputs (or the left-hand side of a sub-flow)
inputsContains(BeanInstance) - Method in class weka.gui.beans.MetaBean
inRanges(Instance, double[][]) - Method in class weka.core.NormalizableDistance: Test if an instance is within the given ranges.
insert(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Inserts an element into the set.
insert(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Inserts a new entry in the hashtable using the specified key.
insert(DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Inserts a new dataObject into the database
insert(DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Inserts a new dataObject into the database
insertAttributeAt(int) - Method in class weka.core.Instance: Inserts an attribute at the given position (0 to numAttributes()).
insertAttributeAt(Attribute, int) - Method in class weka.core.Instances: Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertElementAt(Object, int) - Method in class weka.core.FastVector: Inserts an element at the given position.
insertElementAt(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Inserts the specified object as a component in this list at the specified index.
installLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode: Traverses the tree and installs linear models at each node.
installSmoothedModels() - Method in class weka.classifiers.trees.m5.RuleNode
Instance - Class in weka.core: Class for handling an instance.
Instance(Instance) - Constructor for class weka.core.Instance: Constructor that copies the attribute values and the weight from the given instance.
Instance(double, double[]) - Constructor for class weka.core.Instance: Constructor that inititalizes instance variable with given values.
Instance(int) - Constructor for class weka.core.Instance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
instance(int) - Method in class weka.core.Instances: Returns the instance at the given position.
INSTANCE_AVAILABLE - Static variable in class weka.gui.beans.InstanceEvent
INSTANCE_AVAILABLE - Static variable in class weka.gui.streams.InstanceEvent: Specifies that an instance is available
InstanceComparator - Class in weka.core: A comparator for the Instance class.
InstanceComparator() - Constructor for class weka.core.InstanceComparator: initializes the comparator and includes the class in the comparison
InstanceComparator(boolean) - Constructor for class weka.core.InstanceComparator: initializes the comparator
InstanceCounter - Class in weka.gui.streams: A bean that counts instances streamed to it.
InstanceCounter() - Constructor for class weka.gui.streams.InstanceCounter
InstanceEvent - Class in weka.gui.beans: Event that encapsulates a single instance or header information only
InstanceEvent(Object, Instance, int) - Constructor for class weka.gui.beans.InstanceEvent: Creates a new InstanceEvent instance that encapsulates a single instance only.
InstanceEvent(Object, Instances) - Constructor for class weka.gui.beans.InstanceEvent: Creates a new InstanceEvent instance which encapsulates header information only.
InstanceEvent(Object) - Constructor for class weka.gui.beans.InstanceEvent
InstanceEvent - Class in weka.gui.streams: An event encapsulating an instance stream event.
InstanceEvent(Object, int) - Constructor for class weka.gui.streams.InstanceEvent: Constructs an InstanceEvent with the specified source object and event type
InstanceJoiner - Class in weka.gui.streams: A bean that joins two streams of instances into one.
InstanceJoiner() - Constructor for class weka.gui.streams.InstanceJoiner: Setup the initial states of the member variables
InstanceListener - Interface in weka.gui.beans: Interface to something that can accept instance events
InstanceListener - Interface in weka.gui.streams: An interface for objects interested in listening to streams of instances.
InstanceLoader - Class in weka.gui.streams: A bean that produces a stream of instances from a file.
InstanceLoader() - Constructor for class weka.gui.streams.InstanceLoader
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceCounter
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
instanceProduced(InstanceEvent) - Method in interface weka.gui.streams.InstanceListener
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceSavePanel
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceTable
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceViewer
InstanceProducer - Interface in weka.gui.streams: An interface for objects capable of producing streams of instances.
InstanceQuery - Class in weka.experiment: Convert the results of a database query into instances.
InstanceQuery() - Constructor for class weka.experiment.InstanceQuery: Sets up the database drivers
instanceRangeTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns the tip text for this property
Instances - Class in weka.core: Class for handling an ordered set of weighted instances.
Instances(Reader) - Constructor for class weka.core.Instances: Reads an ARFF file from a reader, and assigns a weight of one to each instance.
Instances(Reader, int) - Constructor for class weka.core.Instances: Deprecated.
instead of using this method in conjunction with the readInstance(Reader) method, one should use the ArffLoader or DataSource class instead.
Instances(Instances) - Constructor for class weka.core.Instances: Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances, int) - Constructor for class weka.core.Instances: Constructor creating an empty set of instances.
Instances(Instances, int, int) - Constructor for class weka.core.Instances: Creates a new set of instances by copying a subset of another set.
Instances(String, FastVector, int) - Constructor for class weka.core.Instances: Creates an empty set of instances.
InstanceSavePanel - Class in weka.gui.streams: A bean that saves a stream of instances to a file.
InstanceSavePanel() - Constructor for class weka.gui.streams.InstanceSavePanel
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.Splitter: Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the subset of instances that apply to a particluar branch of the split.
instancesIndicesTipText() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns the tip text for this property
InstancesResultListener - Class in weka.experiment: Outputs the received results in arff format to a Writer.
InstancesResultListener() - Constructor for class weka.experiment.InstancesResultListener: Sets temporary file.
InstancesSummaryPanel - Class in weka.gui: This panel just displays relation name, number of instances, and number of attributes.
InstancesSummaryPanel() - Constructor for class weka.gui.InstancesSummaryPanel: Creates the instances panel with no initial instances.
InstanceStreamToBatchMaker - Class in weka.gui.beans: Bean that converts an instance stream into a (batch) data set.
InstanceStreamToBatchMaker() - Constructor for class weka.gui.beans.InstanceStreamToBatchMaker
InstanceStreamToBatchMakerBeanInfo - Class in weka.gui.beans: BeanInfo class for the InstanceStreamToBatchMaker bean
InstanceStreamToBatchMakerBeanInfo() - Constructor for class weka.gui.beans.InstanceStreamToBatchMakerBeanInfo
InstanceTable - Class in weka.gui.streams: A bean that takes a stream of instances and displays in a table.
InstanceTable() - Constructor for class weka.gui.streams.InstanceTable
instanceToSchema(Instance, MiningSchema) - Method in class weka.core.pmml.MappingInfo: Convert an Instance to an array of values that matches the format of the mining schema.
InstanceViewer - Class in weka.gui.streams: This is a very simple instance viewer - just displays the dataset as text output as it would be written to a file.
InstanceViewer() - Constructor for class weka.gui.streams.InstanceViewer
intCount - Variable in class weka.core.AttributeStats: The number of int-like values
INTEGER - Static variable in class weka.datagenerators.clusterers.SubspaceCluster: cluster subtype: integer
INTEGER - Static variable in class weka.experiment.DatabaseUtils: Type mapping for INTEGER used for reading experiment results.
intercept() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Return the intercept
internalCacheSizeTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the tip text for this property
internalsTipText() - Method in class weka.classifiers.bayes.WAODE: Returns the tip text for this property
InterquartileRange - Class in weka.filters.unsupervised.attribute: A filter for detecting outliers and extreme values based on interquartile ranges.
InterquartileRange() - Constructor for class weka.filters.unsupervised.attribute.InterquartileRange
intersectSubsets(ScatterSearchV1.Subset, ScatterSearchV1.Subset) - Method in class weka.attributeSelection.ScatterSearchV1: Intersects two subsets
IntervalEstimator - Interface in weka.classifiers: Interface for classifiers that can output confidence intervals
IntVector - Class in weka.core.matrix: A vector specialized on integers.
IntVector() - Constructor for class weka.core.matrix.IntVector: Constructs a null vector.
IntVector(int) - Constructor for class weka.core.matrix.IntVector: Constructs an n-vector of zeros.
IntVector(int, int) - Constructor for class weka.core.matrix.IntVector: Constructs an n-vector of a constant
IntVector(int[]) - Constructor for class weka.core.matrix.IntVector: Constructs a vector given an int array
inverse() - Method in class weka.core.matrix.Matrix: Matrix inverse or pseudoinverse
inverseIterator() - Method in class weka.associations.tertius.SimpleLinkedList
invertSelectionTipText() - Method in class weka.core.NormalizableDistance: Returns the tip text for this property.
invertSelectionTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.supervised.instance.Resample: Returns the tip text for this property.
invertSelectionTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Copy: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.MathExpression: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Remove: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.Resample: Returns the tip text for this property
invertTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
invoke(String, Class[], Object[]) - Method in class weka.core.Jython: executes the specified method on the current interpreter and returns the result, if any
invoke(Object, String, Class[], Object[]) - Static method in class weka.core.Jython: executes the specified method and returns the result, if any
invokeMain(String, String[]) - Static method in class weka.gui.SplashWindow: Invokes the main method of the provided class name.
invokeMethod(String, String, String[]) - Static method in class weka.gui.SplashWindow: Invokes the named method of the provided class name.
is(String) - Method in class weka.core.Stopwords: Returns true if the given string is a stop word.
IS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
isALeaf() - Method in class weka.core.neighboursearch.balltrees.BallNode: Returns true if the node is a leaf node (if both its left and right child are null).
isALeaf() - Method in class weka.core.neighboursearch.CoverTree.CoverTreeNode: Returns whether if the node is a leaf or not.
isALeaf() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode: Checks if node is a leaf.
isAllowed(Class, String) - Method in class weka.core.xml.PropertyHandler: returns whether the given property (display name) is allowed for the given class.
isAllowed(Object, String) - Method in class weka.core.xml.PropertyHandler: returns whether the given property (display name) is allowed for the given object .
isArff(String) - Static method in class weka.core.converters.ConverterUtils.DataSource: returns whether the extension of the location is likely to be of ARFF format, i.e., ending in ".arff" or ".arff.gz" (case-insensitive).
isAttribute() - Method in enum weka.core.Capabilities.Capability: returns true if the capability is an attribute
isAttributeCapability() - Method in enum weka.core.Capabilities.Capability: returns true if the capability is an attribute capability
isAveragable() - Method in class weka.core.Attribute: Returns whether the attribute can be averaged meaningfully.
isBoolean(int) - Method in class weka.datagenerators.clusterers.SubspaceCluster: Returns true if attribute is boolean
isBusy() - Method in class weka.gui.beans.Associator: Returns true if.
isBusy() - Method in interface weka.gui.beans.BeanCommon: Returns true if.
isBusy() - Method in class weka.gui.beans.ClassAssigner: Returns true if.
isBusy() - Method in class weka.gui.beans.Classifier: Returns true if.
isBusy() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Returns true if.
isBusy() - Method in class weka.gui.beans.ClassValuePicker: Returns true if.
isBusy() - Method in class weka.gui.beans.Clusterer: Returns true if.
isBusy() - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Returns true if.
isBusy() - Method in class weka.gui.beans.CostBenefitAnalysis: Returns true if.
isBusy() - Method in class weka.gui.beans.CrossValidationFoldMaker: Returns true if.
isBusy() - Method in class weka.gui.beans.Filter: Returns true if.
isBusy() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Returns true if.
isBusy() - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Returns true if.
isBusy() - Method in class weka.gui.beans.Loader: Returns true if.
isBusy() - Method in class weka.gui.beans.MetaBean: Returns true if.
isBusy() - Method in class weka.gui.beans.PredictionAppender: Returns true if.
isBusy() - Method in class weka.gui.beans.Saver: Returns true if.
isBusy() - Method in class weka.gui.beans.SerializedModelSaver: Returns true if.
isBusy() - Method in class weka.gui.beans.StripChart: Returns true if.
isBusy() - Method in class weka.gui.beans.TestSetMaker: Returns true if.
isBusy() - Method in class weka.gui.beans.TextViewer: Returns true if.
isBusy() - Method in class weka.gui.beans.TrainingSetMaker: Returns true if.
isBusy() - Method in class weka.gui.beans.TrainTestSplitMaker: Returns true if.
isCellEditable(int, int) - Method in class weka.gui.arffviewer.ArffTableModel: returns true if the cell at rowindex and columnindexis editable
isCellEditable(int, int) - Method in class weka.gui.SortedTableModel: Returns true if the cell at rowIndex and columnIndex is editable.
isCellEditable(int, int) - Method in class weka.gui.sql.ResultSetTableModel: returns true if the cell at rowindex and columnindexis editable.
isChanged() - Method in class weka.classifiers.bayes.net.EditableBayesNet: return true when current state differs from the state the network was last saved
isChanged() - Method in class weka.gui.arffviewer.ArffPanel: returns whether the content of the panel was changed
isChanged() - Method in class weka.gui.ViewerDialog: returns whether the data has been changed
isClass() - Method in class weka.associations.tertius.Predicate
isClass() - Method in enum weka.core.Capabilities.Capability: returns true if the capability is a class
isClassCapability() - Method in enum weka.core.Capabilities.Capability: returns true if the capability is a other capability
isClassname(String) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: tests whether the given partial string is the name of a class with classpath - it basically tests, whether the string consists only of alphanumeric literals, underscores and dots.
isConnected() - Method in class weka.experiment.DatabaseUtils: Returns true if a database connection is active.
isConnected() - Method in class weka.gui.sql.event.ConnectionEvent: returns whether the connection is still open.
isContainedBy(Instance) - Method in class weka.associations.gsp.Element: Checks if an Element is contained by a given Instance.
isContinuous() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: checks, whether cluster sub type is continuous
isCoreFileLoader(String) - Static method in class weka.core.converters.ConverterUtils: checks whether the given class is one of the hardcoded core file loaders.
isCoreFileSaver(String) - Static method in class weka.core.converters.ConverterUtils: checks whether the given class is one of the hardcoded core file savers.
isCover(Instance) - Method in class weka.classifiers.rules.ConjunctiveRule: Whether the instance covered by this rule
isCpuTime() - Method in class weka.core.Debug.Clock: whether the measurement is based on the msecs returned from the System class or on the more accurate CPU time.
isCursorScrollable() - Method in class weka.experiment.DatabaseUtils: Checks whether cursors are scrollable in general, false otherwise (also if not connected).
isCursorScrollSensitive() - Method in class weka.experiment.DatabaseUtils: Returns whether the cursors only support forward movement or are scroll sensitive (with ResultSet.CONCUR_READ_ONLY concurrency).
isDate() - Method in class weka.core.Attribute: Tests if the attribute is a date type.
isDebug() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns true if debug is turned on.
isEmpty() - Method in class weka.associations.gsp.Element: Checks if the Element contains any events.
isEmpty() - Method in class weka.associations.tertius.LiteralSet: Test if this set is empty.
isEmpty() - Method in class weka.associations.tertius.Rule: Test if this rule is empty.
isEmpty() - Method in class weka.associations.tertius.SimpleLinkedList
isEmpty() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns true if it is empty.
isEmpty() - Method in class weka.classifiers.functions.pace.PaceMatrix: Check if the matrix is empty
isEmpty() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Tests if this hashtable maps no keys to values.
isEmpty() - Method in class weka.core.matrix.DoubleVector: Checks if it is an empty vector
isEmpty() - Method in class weka.core.matrix.IntVector: Returns true if the vector is empty
isEmpty() - Method in class weka.core.Trie: Returns true if this collection contains no elements.
isEnabled(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities: whether the given capability is enabled.
isEnabled() - Method in class weka.core.Memory: returns whether the memory management is enabled
isEnabledNot(Capabilities.Capability) - Method in class weka.core.FindWithCapabilities: whether the given "not to have" capability is enabled.
isEqual(ScatterSearchV1.Subset) - Method in class weka.attributeSelection.ScatterSearchV1.Subset
isFirstBatchDone() - Method in class weka.filters.Filter: Returns true if the first batch of instances got processed.
isFullRank() - Method in class weka.core.matrix.QRDecomposition: Is the matrix full rank?
isGaussian() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: checks, whether cluster type is gaussian
isHidden() - Method in class weka.gui.beans.BeanConnection: Returns true if this connection is invisible
isHierachic(String) - Method in class weka.gui.HierarchyPropertyParser: Whether the given string has a hierachy structure with the seperators
isIgnored(String) - Method in class weka.core.xml.PropertyHandler: checks whether the given display name is an ignored property
isIgnored(Class, String) - Method in class weka.core.xml.PropertyHandler: checks whether the given display name of a certain class is an ignored property.
isIgnored(Object, String) - Method in class weka.core.xml.PropertyHandler: checks whether the given display name of a given object is an ignored property.
isIncludedIn(Rule) - Method in class weka.associations.tertius.Body: Test if this Body is included in a rule.
isIncludedIn(Rule) - Method in class weka.associations.tertius.Head: Test if this Head is included in a rule.
isIncludedIn(Rule) - Method in class weka.associations.tertius.LiteralSet: Test if this LiteralSet is included in a rule.
isIncremental() - Method in class weka.core.converters.ConverterUtils.DataSource: returns whether the loader is an incremental one.
isInitialAnchorRandom() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Gets whether if the initial anchor is chosen randomly.
isInRange(double) - Method in class weka.core.Attribute: Determines whether a value lies within the bounds of the attribute.
isInRange(int) - Method in class weka.core.Range: Gets whether the supplied cardinal number is included in the current range.
isInteger() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: checks, whether cluster sub type is integer
isKeyword(String) - Method in class weka.experiment.DatabaseUtils: Checks whether the given string is a reserved keyword.
isKOML(String) - Static method in class weka.core.xml.SerialUIDChanger: checks whether the given filename ends with ".koml"
isLeaf() - Method in class weka.classifiers.trees.m5.RuleNode: Return true if this node is a leaf
isLeafReached() - Method in class weka.gui.HierarchyPropertyParser: Whether the current position is a leaf
isMissing(int) - Method in class weka.core.Instance: Tests if a specific value is "missing".
isMissing(Attribute) - Method in class weka.core.Instance: Tests if a specific value is "missing".
isMissing(int) - Method in class weka.core.SparseInstance: Tests if a specific value is "missing".
ISMISSING - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
isMissingAt(int, int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: checks whether the value at the given position is missing
isMissingAt(int, int) - Method in class weka.gui.arffviewer.ArffTableModel: checks whether the value at the given position is missing
isMissingSparse(int) - Method in class weka.core.Instance: Tests if a specific value is "missing".
isMissingValue(double) - Static method in class weka.core.Instance: Tests if the given value codes "missing".
isMonitoring() - Method in class weka.gui.MemoryUsagePanel: Returns whether the thread is still running.
isNewBatch() - Method in class weka.filters.Filter: Returns true if the a new batch was started, either a new instance of the filter was created or the batchFinished() method got called.
isNewer(Object) - Method in class weka.core.Version: checks whether this version is newer than the one from the given version string
isNominal() - Method in class weka.core.Attribute: Test if the attribute is nominal.
isNominal(int) - Method in class weka.datagenerators.clusterers.SubspaceCluster: Returns true if attribute is nominal
isNominal() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns true if selection attribute is nominal.
isNominal() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns true if selection attribute is nominal.
isNonsingular() - Method in class weka.core.matrix.LUDecomposition: Is the matrix nonsingular?
isNormalizeData() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns true if the data is to be normalized first
isNotificationEnabled() - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns whether the notification of changes is enabled
isNotificationEnabled() - Method in class weka.gui.arffviewer.ArffTableModel: returns whether the notification of changes is enabled
isNullAt(int, int) - Method in class weka.gui.sql.ResultSetTableModel: checks whether the value of the cell is NULL.
isNumeric() - Method in class weka.core.Attribute: Tests if the attribute is numeric.
isNumeric() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns true if selection attribute is numeric.
isNumericAt(int) - Method in class weka.gui.sql.ResultSetTableModel: returns whether the column at the given index is numeric.
isOlder(Object) - Method in class weka.core.Version: checks whether this version is older than the one from the given version string
isOpticsOutputs() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject: Returns the flag for writing actions
isOtherCapability() - Method in enum weka.core.Capabilities.Capability: returns true if the capability is a class capability
IsotonicRegression - Class in weka.classifiers.functions: Learns an isotonic regression model.
IsotonicRegression() - Constructor for class weka.classifiers.functions.IsotonicRegression
isOutOfMemory() - Method in class weka.core.Memory: checks if there's still enough memory left by checking whether there is still a 50MB margin between getUsed() and getMax().
isOutputFormatDefined() - Method in class weka.filters.Filter: Returns whether the output format is ready to be collected
isOutputFormatDefined() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns whether the output format is ready to be collected
isPaintable() - Method in class weka.gui.CostMatrixEditor: Indicates whether the object can be represented graphically.
isPaintable() - Method in class weka.gui.FileEditor: Returns true since this editor is paintable.
isPaintable() - Method in class weka.gui.GenericArrayEditor: Returns true to indicate that we can paint a representation of the string array.
isPaintable() - Method in class weka.gui.GenericObjectEditor: Returns true to indicate that we can paint a representation of the Object.
isPaintable() - Method in class weka.gui.SimpleDateFormatEditor: Indicates whether the object can be represented graphically.
isPanelSelected() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: checks whether a panel is currently selected
isPresent() - Static method in class weka.classifiers.functions.LibLINEAR: returns whether the liblinear classes are present or not, i.e.
isPresent() - Static method in class weka.classifiers.functions.LibSVM: returns whether the libsvm classes are present or not, i.e.
isPresent() - Static method in class weka.core.Jython: returns whether the Jython classes are present or not, i.e.
isPresent() - Static method in class weka.core.stemmers.SnowballStemmer: returns whether Snowball is present or not, i.e.
isPresent() - Static method in class weka.core.xml.KOML: returns whether KOML is present or not, i.e.
isPresent() - Static method in class weka.core.xml.XStream: returns whether XStream is present or not, i.e.
isProcessed() - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Gives information about the status of a dataObject
isProcessed() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Gives information about the status of a dataObject
isProcessed() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Gives information about the status of a dataObject
isRandom() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: checks, whether cluster type is random
isReadOnly() - Method in class weka.gui.arffviewer.ArffPanel: returns whether the model is read-only
isReadOnly() - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns whether the model is read-only
isReadOnly() - Method in class weka.gui.arffviewer.ArffTable: returns whether the model is read-only
isReadOnly() - Method in class weka.gui.arffviewer.ArffTableModel: returns whether the model is read-only
isRegular() - Method in class weka.core.Attribute: Returns whether the attribute values are equally spaced.
isRelationValued() - Method in class weka.core.Attribute: Tests if the attribute is relation valued.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.AveragingResultProducer: Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.CSVResultListener: Always says a result is required.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener: Always says a result is required.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultProducer: Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.LearningRateResultProducer: Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in interface weka.experiment.ResultListener: Determines whether the results for a specified key must be generated.
isRootReached() - Method in class weka.gui.HierarchyPropertyParser: Whether the current position is the root
isRunning() - Method in class weka.core.Debug.Clock: whether the time is still being clocked
isSaved() - Method in class weka.classifiers.bayes.net.EditableBayesNet: indicate the network state was saved
isSequentialAttIndexValid() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns whether or not the Sequential Attribute Index requires rebuilding due to a change
isSequentialInstanceIndexValid() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns whether or not the Sequential Instance Index requires rebuilding due to a change
isSerializable(String) - Static method in class weka.core.SerializationHelper: checks whether a class is serializable.
isSerializable(Class) - Static method in class weka.core.SerializationHelper: checks whether a class is serializable.
isShowCoreDistances() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Returns the flag for showCoreDistances
isShowReachabilityDistances() - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Returns the flag for showReachabilityDistances
isSorted() - Method in class weka.gui.SortedTableModel: returns whether the table was sorted
isSPD() - Method in class weka.core.matrix.CholeskyDecomposition: Is the matrix symmetric and positive definite?
isSquare() - Method in class weka.core.matrix.Matrix: returns whether the matrix is a square matrix or not.
isStopword(String) - Static method in class weka.core.Stopwords: Returns true if the given string is a stop word.
isStreamableFilter() - Method in class weka.filters.MultiFilter: tests whether all the enclosed filters are streamable
isString() - Method in class weka.core.Attribute: Tests if the attribute is a string.
isStructureOnly() - Method in class weka.gui.beans.DataSetEvent: Returns true if the encapsulated instances contain just header information
isStructureOnly() - Method in class weka.gui.beans.TestSetEvent: Returns true if the encapsulated instances contain just header information
isStructureOnly() - Method in class weka.gui.beans.TrainingSetEvent: Returns true if the encapsulated instances contain just header information
isSubclass(String, String) - Static method in class weka.core.ClassDiscovery: Checks whether the "otherclass" is a subclass of the given "superclass".
isSubclass(Class, Class) - Static method in class weka.core.ClassDiscovery: Checks whether the "otherclass" is a subclass of the given "superclass".
isSymmetric() - Method in class weka.core.Matrix: Deprecated.

Returns true if the matrix is symmetric.
isSymmetric() - Method in class weka.core.matrix.Matrix: Returns true if the matrix is symmetric.
isUndoEnabled() - Method in interface weka.core.Undoable: returns whether undo support is enabled
isUndoEnabled() - Method in class weka.gui.arffviewer.ArffPanel: returns whether undo support is enabled
isUndoEnabled() - Method in class weka.gui.arffviewer.ArffSortedTableModel: returns whether undo support is enabled
isUndoEnabled() - Method in class weka.gui.arffviewer.ArffTableModel: returns whether undo support is enabled
isUniform() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: checks, whether cluster type is uniform
isUseK2Prior() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Returns whether K2 prior is used
isUseK2Prior() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
isUseVariant1() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Whether variant 1 is used
itemAt(int) - Method in class weka.associations.ItemSet: Gest the index of the value of the specified attribute
items() - Method in class weka.associations.ItemSet: Gest the item set as an int array
ItemSet - Class in weka.associations: Class for storing a set of items.
ItemSet(int) - Constructor for class weka.associations.ItemSet: Constructor
ItemSet(int, int[]) - Constructor for class weka.associations.ItemSet: Constructor
ItemSet(int[]) - Constructor for class weka.associations.ItemSet: Contsructor
itemStateChanged(ItemEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Performs the action associated with the ItemEvent.
IteratedLovinsStemmer - Class in weka.core.stemmers: An iterated version of the Lovins stemmer.
IteratedLovinsStemmer() - Constructor for class weka.core.stemmers.IteratedLovinsStemmer
IteratedSingleClassifierEnhancer - Class in weka.classifiers: Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
IteratedSingleClassifierEnhancer() - Constructor for class weka.classifiers.IteratedSingleClassifierEnhancer
iterationCounter - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Iteration counter
IterativeClassifier - Interface in weka.classifiers: Interface for classifiers that can induce models of growing complexity one step at a time.
iterator() - Method in class weka.associations.tertius.SimpleLinkedList
iterator() - Method in class weka.core.Trie: Returns an iterator over the elements in this collection.

J

J48 - Class in weka.classifiers.trees: Class for generating a pruned or unpruned C4.5 decision tree.
J48() - Constructor for class weka.classifiers.trees.J48
J48graft - Class in weka.classifiers.trees: Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
J48graft() - Constructor for class weka.classifiers.trees.J48graft
Javadoc - Class in weka.core: Abstract superclass for classes that generate Javadoc comments and replace the content between certain comment tags.
Javadoc() - Constructor for class weka.core.Javadoc
JComponentWriter - Class in weka.gui.visualize: This class takes any JComponent and outputs it to a file.
JComponentWriter() - Constructor for class weka.gui.visualize.JComponentWriter: initializes the object
JComponentWriter(JComponent) - Constructor for class weka.gui.visualize.JComponentWriter: initializes the object with the given Component
JComponentWriter(JComponent, File) - Constructor for class weka.gui.visualize.JComponentWriter: initializes the object with the given Component and filename
JListHelper - Class in weka.gui: A helper class for JList GUI elements with DefaultListModel or derived models.
JListHelper() - Constructor for class weka.gui.JListHelper
joinOptions(String[]) - Static method in class weka.core.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
joinSubsets(ScatterSearchV1.Subset, ScatterSearchV1.Subset) - Method in class weka.attributeSelection.ScatterSearchV1: Join two subsets
JPEGWriter - Class in weka.gui.visualize: This class takes any JComponent and outputs it to a JPEG-file.
JPEGWriter() - Constructor for class weka.gui.visualize.JPEGWriter: initializes the object.
JPEGWriter(JComponent) - Constructor for class weka.gui.visualize.JPEGWriter: initializes the object with the given Component.
JPEGWriter(JComponent, File) - Constructor for class weka.gui.visualize.JPEGWriter: initializes the object with the given Component and filename.
JRip - Class in weka.classifiers.rules: This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
JRip() - Constructor for class weka.classifiers.rules.JRip
JRip.Antd - Class in weka.classifiers.rules: The single antecedent in the rule, which is composed of an attribute and the corresponding value.
JRip.NominalAntd - Class in weka.classifiers.rules: The antecedent with nominal attribute
JRip.NumericAntd - Class in weka.classifiers.rules: The antecedent with numeric attribute
JRip.RipperRule - Class in weka.classifiers.rules: This class implements a single rule that predicts specified class.
JTableHelper - Class in weka.gui: A helper class for JTable, e.g.
JTableHelper(JTable) - Constructor for class weka.gui.JTableHelper: initializes the object
JTreePopupMenu(JTree) - Constructor for class weka.gui.GenericObjectEditor.JTreePopupMenu: Constructs a new popup menu.
Jython - Class in weka.core: A helper class for Jython.
Jython() - Constructor for class weka.core.Jython: default constructor, tries to instantiate a Python Interpreter
JythonObject - Interface in weka.core: An indicator interface for Jython objects.
JythonSerializableObject - Interface in weka.core: An indicator interface for serializable Jython objects.

K

K2 - Class in weka.classifiers.bayes.net.search.global: This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.

For more information see:

G.F.
K2() - Constructor for class weka.classifiers.bayes.net.search.global.K2
K2 - Class in weka.classifiers.bayes.net.search.local: This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables.

For more information see:

G.F.
K2() - Constructor for class weka.classifiers.bayes.net.search.local.K2
k_nextNeighbourQuery(int, double, DataObject) - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Emits the k next-neighbours and performs an epsilon-range-query at the parallel.
k_nextNeighbourQuery(int, double, DataObject) - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Emits the k next-neighbours and performs an epsilon-range-query at the parallel.
kappa() - Method in class weka.classifiers.Evaluation: Returns value of kappa statistic if class is nominal.
KBInformation() - Method in class weka.classifiers.Evaluation: Return the total Kononenko & Bratko Information score in bits
KBMeanInformation() - Method in class weka.classifiers.Evaluation: Return the Kononenko & Bratko Information score in bits per instance.
KBRelativeInformation() - Method in class weka.classifiers.Evaluation: Return the Kononenko & Bratko Relative Information score
KDConditionalEstimator - Class in weka.estimators: Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate kernel estimators for each discrete conditioning value).
KDConditionalEstimator(int, double) - Constructor for class weka.estimators.KDConditionalEstimator: Constructor
KDDataGenerator - Class in weka.gui.boundaryvisualizer: KDDataGenerator.
KDDataGenerator() - Constructor for class weka.gui.boundaryvisualizer.KDDataGenerator
KDTree - Class in weka.core.neighboursearch: Class implementing the KDTree search algorithm for nearest neighbour search.
The connection to dataset is only a reference.
KDTree() - Constructor for class weka.core.neighboursearch.KDTree: Creates a new instance of KDTree.
KDTree(Instances) - Constructor for class weka.core.neighboursearch.KDTree: Creates a new instance of KDTree.
KDTreeNode - Class in weka.core.neighboursearch.kdtrees: A class representing a KDTree node.
KDTreeNode() - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNode: Constructor.
KDTreeNode(int, int, int, double[][]) - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNode: Constructor.
KDTreeNode(int, int, int, double[][], double[][]) - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNode
KDTreeNodeSplitter - Class in weka.core.neighboursearch.kdtrees: Class that splits up a KDTreeNode.
KDTreeNodeSplitter() - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: default constructor.
KDTreeNodeSplitter(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Creates a new instance of KDTreeNodeSplitter.
KDTreeTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
Kernel - Class in weka.classifiers.functions.supportVector: Abstract kernel.
Kernel() - Constructor for class weka.classifiers.functions.supportVector.Kernel
KernelEstimator - Class in weka.estimators: Simple kernel density estimator.
KernelEstimator(double) - Constructor for class weka.estimators.KernelEstimator: Constructor that takes a precision argument.
KernelEvaluation - Class in weka.classifiers.functions.supportVector: Class for evaluating Kernels.
KernelEvaluation() - Constructor for class weka.classifiers.functions.supportVector.KernelEvaluation: default constructor
kernelFactorExpressionTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns the tip text for this property
KernelFilter - Class in weka.filters.unsupervised.attribute: Converts the given set of predictor variables into a kernel matrix.
KernelFilter() - Constructor for class weka.filters.unsupervised.attribute.KernelFilter
kernelMatrixFileTipText() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.functions.GaussianProcesses: Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
kernelTipText() - Method in class weka.classifiers.mi.MISVM: Returns the tip text for this property
kernelTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns the tip text for this property
KERNELTYPE_LINEAR - Static variable in class weka.classifiers.functions.LibSVM: kernel type linear: u'*v
KERNELTYPE_POLYNOMIAL - Static variable in class weka.classifiers.functions.LibSVM: kernel type polynomial: (gamma*u'*v + coef0)^degree
KERNELTYPE_RBF - Static variable in class weka.classifiers.functions.LibSVM: kernel type radial basis function: exp(-gamma*|u-v|^2)
KERNELTYPE_SIGMOID - Static variable in class weka.classifiers.functions.LibSVM: kernel type sigmoid: tanh(gamma*u'*v + coef0)
kernelTypeTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
key - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: attribute value
keyFieldNameTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
keyIterator() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Returns an iterator over all the keys
keyIterator() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Returns an iterator over all the keys
keys() - Method in class weka.core.xml.MethodHandler: returns an enumeration over all currently stored custom methods, i.e.
keysTipText() - Method in class weka.core.converters.DatabaseLoader: the tip text for this property
kFoldCV(BayesNet, int) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes network classifier.
KKConditionalEstimator - Class in weka.estimators: Conditional probability estimator for a numeric domain conditional upon a numeric domain.
KKConditionalEstimator(double) - Constructor for class weka.estimators.KKConditionalEstimator: Constructor
KMeansInpiredMethod - Class in weka.core.neighboursearch.kdtrees: The class that splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum.

For more information see also:

Ashraf Masood Kibriya (2007).
KMeansInpiredMethod() - Constructor for class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.BallTree: Returns k nearest instances in the current neighbourhood to the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.CoverTree: Returns k-NNs of a given target instance, from among the previously supplied training instances (supplied through setInstances method) P.S.: May return more than k-NNs if more one instances have the same distance to the target as the kth NN.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.KDTree: Returns the k nearest neighbours of the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.LinearNNSearch: Returns k nearest instances in the current neighbourhood to the supplied instance.
kNearestNeighbours(Instance, int) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns k nearest instances in the current neighbourhood to the supplied instance.
KNNTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property.
KNNTipText() - Method in class weka.classifiers.lazy.LWL: Returns the tip text for this property.
KnowledgeFlow - Class in weka.gui.beans: Startup class for the KnowledgeFlow.
KnowledgeFlow() - Constructor for class weka.gui.beans.KnowledgeFlow
KnowledgeFlowApp - Class in weka.gui.beans: Main GUI class for the KnowledgeFlow.
KnowledgeFlowApp(boolean) - Constructor for class weka.gui.beans.KnowledgeFlowApp: Creates a new KnowledgeFlowApp instance.
KOML - Class in weka.core.xml: This class is a helper class for XML serialization using KOML .
KOML() - Constructor for class weka.core.xml.KOML
komlToBinary(String, String) - Static method in class weka.core.xml.SerialUIDChanger: converts a KOML file into a binary one
KOMLV - Static variable in class weka.gui.beans.SerializedModelSaver
KStar - Class in weka.classifiers.lazy: K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
KStar() - Constructor for class weka.classifiers.lazy.KStar
KStarCache - Class in weka.classifiers.lazy.kstar: A class representing the caching system used to keep track of each attribute value and its corresponding scale factor or stop parameter.
KStarCache() - Constructor for class weka.classifiers.lazy.kstar.KStarCache
KStarCache.CacheTable - Class in weka.classifiers.lazy.kstar: A custom hashtable class to support the caching system.
KStarCache.TableEntry - Class in weka.classifiers.lazy.kstar: Hashtable collision list.
KStarConstants - Interface in weka.classifiers.lazy.kstar
KStarNominalAttribute - Class in weka.classifiers.lazy.kstar: A custom class which provides the environment for computing the transformation probability of a specified test instance nominal attribute to a specified train instance nominal attribute.
KStarNominalAttribute(Instance, Instance, int, Instances, int[][], KStarCache) - Constructor for class weka.classifiers.lazy.kstar.KStarNominalAttribute: Constructor
KStarNumericAttribute - Class in weka.classifiers.lazy.kstar: A custom class which provides the environment for computing the transformation probability of a specified test instance numeric attribute to a specified train instance numeric attribute.
KStarNumericAttribute(Instance, Instance, int, Instances, int[][], KStarCache) - Constructor for class weka.classifiers.lazy.kstar.KStarNumericAttribute: Constructor
KStarWrapper - Class in weka.classifiers.lazy.kstar
KStarWrapper() - Constructor for class weka.classifiers.lazy.kstar.KStarWrapper
kthSmallestValue(Attribute, int) - Method in class weka.core.Instances: Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int, int) - Method in class weka.core.Instances: Returns the kth-smallest attribute value of a numeric attribute.
kthSmallestValue(int[], int) - Static method in class weka.core.Utils: Returns the kth-smallest value in the array.
kthSmallestValue(double[], int) - Static method in class weka.core.Utils: Returns the kth-smallest value in the array
kullback(double[], double[], double[], double[], int) - Method in class weka.classifiers.mi.MINND: This function calculates the Kullback Leibler distance between two normal distributions.
KValueTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property

L

LabeledItemSet - Class in weka.associations: Class for storing a set of items together with a class label.
LabeledItemSet(int, int) - Constructor for class weka.associations.LabeledItemSet: Constructor
labelsTipText() - Method in class weka.filters.unsupervised.attribute.AddValues: Returns the tip text for this property
LADTree - Class in weka.classifiers.trees: Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
LADTree() - Constructor for class weka.classifiers.trees.LADTree
LAGDHillClimber - Class in weka.classifiers.bayes.net.search.local: This Bayes Network learning algorithm uses a Look Ahead Hill Climbing algorithm called LAGD Hill Climbing.
LAGDHillClimber() - Constructor for class weka.classifiers.bayes.net.search.local.LAGDHillClimber
lambdaTipText() - Method in class weka.classifiers.functions.SPegasos: Returns the tip text for this property
lambdaTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the tip text for this property
LaplaceEstimate(double, double, double) - Method in class weka.classifiers.bayes.AODEsr: Returns the probability estimate, using laplace correction
laplaceForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
LaplacePriorImpl - Class in weka.classifiers.bayes.blr: Implementation of the Gaussian Prior update function based on modified CLG Algorithm (CLG-Lasso) with a certain Trust Region Update based on Laplace Priors.
LaplacePriorImpl() - Constructor for class weka.classifiers.bayes.blr.LaplacePriorImpl
laplaceProb(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns relative frequency of class over all bags with Laplace correction.
laplaceProb(int, int) - Method in class weka.classifiers.trees.j48.Distribution: Returns relative frequency of class for given bag.
laplaceUpdate(int, double) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl: This is the CLG-lasso update function described in the
LAPLACIAN - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
last() - Method in class weka.core.neighboursearch.covertrees.Stack: Returns the last element in the stack.
LAST - Static variable in class weka.filters.unsupervised.attribute.ClassAssigner: use the last attribute as class.
lastActionMsg() - Method in class weka.classifiers.bayes.net.EditableBayesNet: get message representing the last action performed on the network
lastElement() - Method in class weka.core.FastVector: Returns the last element of the vector.
lastElement() - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Returns the last component of the list.
lastIndexOf(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Returns the index of the last occurrence of elem.
lastIndexOf(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Searches backwards for elem, starting from the specified index, and returns an index to it.
lastInstance() - Method in class weka.core.Instances: Returns the last instance in the set.
LatentSemanticAnalysis - Class in weka.attributeSelection: Performs latent semantic analysis and transformation of the data.
LatentSemanticAnalysis() - Constructor for class weka.attributeSelection.LatentSemanticAnalysis
launchNext(int, int) - Method in class weka.experiment.RemoteExperiment: Launch a sub experiment on a remote host
layoutCompleted(LayoutCompleteEvent) - Method in class weka.classifiers.bayes.net.GUI: This method is an implementation for LayoutCompleteEventListener class.
layoutCompleted(LayoutCompleteEvent) - Method in class weka.gui.graphvisualizer.GraphVisualizer: This method is an implementation for LayoutCompleteEventListener class.
layoutCompleted(LayoutCompleteEvent) - Method in interface weka.gui.graphvisualizer.LayoutCompleteEventListener
LayoutCompleteEvent - Class in weka.gui.graphvisualizer: This is an event which is fired by a LayoutEngine once a LayoutEngine finishes laying out the graph, so that the Visualizer can repaint the screen to show the changes.
LayoutCompleteEvent(Object) - Constructor for class weka.gui.graphvisualizer.LayoutCompleteEvent
LayoutCompleteEventListener - Interface in weka.gui.graphvisualizer: This interface should be implemented by any class which needs to receive LayoutCompleteEvents from the LayoutEngine.
LayoutEngine - Interface in weka.gui.graphvisualizer: This interface class has been added to facilitate the addition of other layout engines to this package.
layoutGraph(FastVector, FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: set positions of all nodes
layoutGraph() - Method in class weka.gui.graphvisualizer.GraphVisualizer: This method lays out the graph by calling the LayoutEngine's layoutGraph() method.
layoutGraph() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: This method does a complete layout of the graph which includes removing cycles, assigning levels to nodes, reducing edge crossings and laying out the vertices horizontally for better visibility.
layoutGraph() - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method lays out the graph for better visualization
LBR - Class in weka.classifiers.lazy: Lazy Bayesian Rules Classifier.
LBR() - Constructor for class weka.classifiers.lazy.LBR
LBR.Indexes - Class in weka.classifiers.lazy: Class for handling instances and the associated attributes.
LE - Static variable in interface weka.core.mathematicalexpression.sym
LE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
LearningRateResultProducer - Class in weka.experiment: Tells a sub-ResultProducer to reproduce the current run for varying sized subsamples of the dataset.
LearningRateResultProducer() - Constructor for class weka.experiment.LearningRateResultProducer
learningRateTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
leastExplainingColumn(PaceMatrix, IntVector, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the index of the column that has the smallest (squared) response, when the column is moved to become the (ks-1)-th column.
LeastMedSq - Class in weka.classifiers.functions: Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
LeastMedSq() - Constructor for class weka.classifiers.functions.LeastMedSq
leaveOneOut(LBR.Indexes, int[][][], int[], boolean[]) - Method in class weka.classifiers.lazy.LBR: Leave-one-out strategy.
leaveOneOutCV(BayesNet) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: LeaveOneOutCV returns the accuracy calculated using Leave One Out cross validation.
LED24 - Class in weka.datagenerators.classifiers.classification: This generator produces data for a display with 7 LEDs.
LED24() - Constructor for class weka.datagenerators.classifiers.classification.LED24: initializes the generator with default values
LEFT_PARENTHESES - Variable in class weka.experiment.ResultMatrix: the left parentheses for enumerating cols/rows
leftNode() - Method in class weka.classifiers.trees.m5.RuleNode: Get the left child of this node
leftSide(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Prints left side of condition.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.C45Split: Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Prints left side of condition satisfied by instances.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.GraftSplit: Prints left side of condition satisfied by instances.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Does nothing because no condition has to be satisfied.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit: Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.trees.j48.NoSplit: Does nothing because no condition has to be satisfied.
leftSide(Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Returns name of splitting attribute (left side of condition).
legend() - Method in class weka.classifiers.trees.ADTree: Returns the legend of the tree, describing how results are to be interpreted.
legend() - Method in class weka.classifiers.trees.LADTree: Returns the legend of the tree, describing how results are to be interpreted.
LegendPanel - Class in weka.gui.visualize: This panel displays legends for a list of plots.
LegendPanel() - Constructor for class weka.gui.visualize.LegendPanel: Constructor
length - Variable in class weka.core.neighboursearch.covertrees.Stack: The number of elements in the stack.
leverageForRule(AprioriItemSet, AprioriItemSet, int, int) - Method in class weka.associations.AprioriItemSet: Outputs the leverage for a rule.
LFSMethods - Class in weka.attributeSelection
LFSMethods() - Constructor for class weka.attributeSelection.LFSMethods: empty constructor methods are not static because of access to inner class Link2 and LinkedList2
LFSMethods.Link2 - Class in weka.attributeSelection: Class for a node in a linked list.
LFSMethods.LinkedList2 - Class in weka.attributeSelection: Class for handling a linked list.
LibLINEAR - Class in weka.classifiers.functions: A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier).
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008).
LibLINEAR() - Constructor for class weka.classifiers.functions.LibLINEAR
LibSVM - Class in weka.classifiers.functions: A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier).
LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier.
LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool.
LibSVM() - Constructor for class weka.classifiers.functions.LibSVM
LibSVMLoader - Class in weka.core.converters: Reads a source that is in libsvm format.

For more information about libsvm see:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/
LibSVMLoader() - Constructor for class weka.core.converters.LibSVMLoader
LibSVMSaver - Class in weka.core.converters: Writes to a destination that is in libsvm format.

For more information about libsvm see:

http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Valid options are:
LibSVMSaver() - Constructor for class weka.core.converters.LibSVMSaver: Constructor
LIFT_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: Lift
liftForRule(AprioriItemSet, AprioriItemSet, int) - Method in class weka.associations.AprioriItemSet: Outputs the lift for a rule.
likelihoodThresholdTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
LINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
LinearForwardSelection - Class in weka.attributeSelection: LinearForwardSelection:

Extension of BestFirst.
LinearForwardSelection() - Constructor for class weka.attributeSelection.LinearForwardSelection: Constructor
LinearNNSearch - Class in weka.core.neighboursearch: Class implementing the brute force search algorithm for nearest neighbour search.
LinearNNSearch() - Constructor for class weka.core.neighboursearch.LinearNNSearch: Constructor.
LinearNNSearch(Instances) - Constructor for class weka.core.neighboursearch.LinearNNSearch: Constructor that uses the supplied set of instances.
LinearRegression - Class in weka.classifiers.functions: Class for using linear regression for prediction.
LinearRegression() - Constructor for class weka.classifiers.functions.LinearRegression
LinearRegression - Class in weka.core.matrix: Class for performing (ridged) linear regression using Tikhonov regularization.
LinearRegression(Matrix, Matrix, double) - Constructor for class weka.core.matrix.LinearRegression: Performs a (ridged) linear regression.
LinearRegression(Matrix, Matrix, double[], double) - Constructor for class weka.core.matrix.LinearRegression: Performs a weighted (ridged) linear regression.
LinearUnit - Class in weka.classifiers.functions.neural: This can be used by the neuralnode to perform all it's computations (as a Linear unit).
LinearUnit() - Constructor for class weka.classifiers.functions.neural.LinearUnit
lineWrap(String, int) - Static method in class weka.core.Utils: Implements simple line breaking.
Link2(Object[], double) - Constructor for class weka.attributeSelection.BestFirst.Link2: Constructor
Link2(Object[], double) - Constructor for class weka.attributeSelection.LFSMethods.Link2
LinkedList2(int) - Constructor for class weka.attributeSelection.BestFirst.LinkedList2
LinkedList2(int) - Constructor for class weka.attributeSelection.LFSMethods.LinkedList2
LinkedListInverseIterator() - Constructor for class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
LinkedListIterator() - Constructor for class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
linkTypeTipText() - Method in class weka.clusterers.HierarchicalClusterer
LINUX_BROWSERS - Static variable in class weka.gui.BrowserHelper: Linux/Unix binaries to look for
listCapabilities(Capabilities) - Static method in class weka.gui.PropertySheetPanel: returns a comma-separated list of all the capabilities.
listOptions() - Method in class weka.associations.Apriori: Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.CheckAssociator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.FilteredAssociator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.FPGrowth: Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns an enumeration of the available options.
listOptions() - Method in class weka.associations.PredictiveApriori: Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.SingleAssociatorEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.Tertius: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.BestFirst: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.CfsSubsetEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.CheckAttributeSelection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns an enumeration describing the available options
listOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.FilteredAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.FilteredSubsetEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.GeneticSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.GreedyStepwise: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.LinearForwardSelection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.OneRAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.PrincipalComponents: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RaceSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RandomSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.Ranker: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RankSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ScatterSearchV1: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.SVMAttributeEval: Returns an enumeration describing all the available options
listOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.AODE: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.AODEsr: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.BayesNet: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.DMNBtext: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.NaiveBayes: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.BayesNetGenerator: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.K2: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.global.TAN: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.K2: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.local.TAN: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.WAODE: Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.BVDecompose: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.BVDecomposeSegCVSub: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.CheckClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.CheckSource: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.Classifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.GaussianProcesses: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.LeastMedSq: Returns an enumeration of all the available options..
listOptions() - Method in class weka.classifiers.functions.LibLINEAR: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.LibSVM: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.LinearRegression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.Logistic: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.MultilayerPerceptron: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.PaceRegression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.PLSClassifier: Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.RBFNetwork: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.SimpleLogistic: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SMO: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SMOreg: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SPegasos: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.CachedKernel: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.CheckKernel: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.Puk: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.supportVector.RegSMO: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.VotedPerceptron: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.Winnow: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.IBk: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.KStar: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.LWL: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AdaBoostM1: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AdditiveRegression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Bagging: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.ClassificationViaClustering: Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.CVParameterSelection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Dagging: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Decorate: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.FilteredClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.GridSearch: Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.LogitBoost: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MetaCost: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MultiBoostAB: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.MultiClassClassifier: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.MultiScheme: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.RandomSubSpace: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.RotationForest: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Stacking: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.ThresholdSelector: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Vote: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.mi.CitationKNN: Returns an enumeration of all the available options..
listOptions() - Method in class weka.classifiers.mi.MDD: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIBoost: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIDD: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIEMDD: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MILR: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MINND: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIOptimalBall: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.mi.MISMO: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.mi.MISVM: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.mi.MIWrapper: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.mi.SimpleMI: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.misc.SerializedClassifier: Gets an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.misc.VFI: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.MultipleClassifiersCombiner: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.RandomizableClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.DecisionTable: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.DTNB: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.JRip: Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.NNge: Returns an enumeration of all the available options..
listOptions() - Method in class weka.classifiers.rules.OneR: Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.rules.PART: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.Ridor: Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.SingleClassifierEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.ADTree: Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.trees.BFTree: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.FT: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.J48: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.J48graft: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.LADTree: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.LMT: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.m5.M5Base: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.trees.M5P: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.trees.RandomForest: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.RandomTree: Lists the command-line options for this classifier.
listOptions() - Method in class weka.classifiers.trees.REPTree: Lists the command-line options for this classifier.
listOptions() - Method in class weka.classifiers.trees.SimpleCart: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.CheckClusterer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.CLOPE
listOptions() - Method in class weka.clusterers.Cobweb: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.DBSCAN: Returns an enumeration of all the available options..
listOptions() - Method in class weka.clusterers.EM: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.FarthestFirst: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.FilteredClusterer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.HierarchicalClusterer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.clusterers.OPTICS: Returns an enumeration of all the available options.
listOptions() - Method in class weka.clusterers.RandomizableClusterer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.RandomizableDensityBasedClusterer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.sIB: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.SimpleKMeans: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.SingleClustererEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.XMeans: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.Check: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.CheckGOE: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.CheckOptionHandler: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.CheckScheme: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.AbstractFileSaver: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.ArffSaver: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.C45Saver: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.CSVLoader: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.DatabaseLoader: Lists the available options
listOptions() - Method in class weka.core.converters.DatabaseSaver: Lists the available options.
listOptions() - Method in class weka.core.converters.LibSVMSaver: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.SVMLightSaver: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.converters.TextDirectoryLoader: Lists the available options
listOptions() - Method in class weka.core.converters.XRFFSaver: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.FindWithCapabilities: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.Javadoc: Returns an enumeration describing the available options.
ListOptions - Class in weka.core: Lists the options of an OptionHandler
ListOptions() - Constructor for class weka.core.ListOptions
listOptions() - Method in class weka.core.ListOptions: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.BallTree: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.BallSplitter: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.CoverTree: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.KDTree: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.LinearNNSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.NormalizableDistance: Returns an enumeration describing the available options.
listOptions() - Method in interface weka.core.OptionHandler: Returns an enumeration of all the available options..
listOptions() - Method in class weka.core.OptionHandlerJavadoc: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.stemmers.SnowballStemmer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.TechnicalInformationHandlerJavadoc: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.TestInstances: Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer: Returns an enumeration of all the available options..
listOptions() - Method in class weka.core.tokenizers.NGramTokenizer: Returns an enumeration of all the available options..
listOptions() - Method in class weka.core.tokenizers.Tokenizer: Returns an enumeration of all the available options..
listOptions() - Method in class weka.datagenerators.ClassificationGenerator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.LED24: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.regression.Expression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.ClusterDefinition: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.ClusterGenerator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.DataGenerator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.RegressionGenerator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.estimators.CheckEstimator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.estimators.Estimator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.experiment.AveragingResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CrossValidationResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CSVResultListener: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DatabaseResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.experiment.Experiment: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.InstanceQuery: Returns an enumeration describing the available options
listOptions() - Method in class weka.experiment.LearningRateResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.PairedTTester: Lists options understood by this object.
listOptions() - Method in class weka.experiment.RandomSplitResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.RegressionSplitEvaluator: Returns an enumeration describing the available options..
listOptions() - Method in class weka.filters.CheckSource: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.MultiFilter: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.SimpleFilter: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.AddClassification: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.Discretize: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.PLSFilter: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.Resample: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.SMOTE: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Add: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddID: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.AddValues: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.ClassAssigner: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Copy: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MathExpression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NominalToString: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Remove: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Reorder: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Wavelet: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Normalize: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Randomize: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Resample: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.gui.Main: Gets an enumeration describing the available options.
ListSelectorDialog - Class in weka.gui: A dialog to present the user with a list of items, that the user can make a selection from, or cancel the selection.
ListSelectorDialog(Frame, JList) - Constructor for class weka.gui.ListSelectorDialog: Create the list selection dialog.
listStemmers() - Static method in class weka.core.stemmers.SnowballStemmer: returns an enumeration over all currently stored stemmer names.
Literal - Class in weka.associations.tertius
Literal(Predicate, int, int) - Constructor for class weka.associations.tertius.Literal
LiteralSet - Class in weka.associations.tertius: Class representing a set of literals, being either the body or the head of a rule.
LiteralSet() - Constructor for class weka.associations.tertius.LiteralSet: Constructor for a set that does not store its counter-instances.
LiteralSet(Instances) - Constructor for class weka.associations.tertius.LiteralSet: Constructor initializing the set of counter-instances to all the instances.
LMT - Class in weka.classifiers.trees: Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
LMT() - Constructor for class weka.classifiers.trees.LMT: Creates an instance of LMT with standard options
LMTNode - Class in weka.classifiers.trees.lmt: Class for logistic model tree structure.
LMTNode(ModelSelection, int, boolean, boolean, int, double, boolean) - Constructor for class weka.classifiers.trees.lmt.LMTNode: Constructor for logistic model tree node.
lnFactorial(int) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Fast computation of ln(n!) for non-negative ints negative ints are passed on to the general gamma-function based version in weka.core.SpecialFunctions if the current n value is higher than any previous one, the cache is extended and filled to cover it the common case is reduced to a simple array lookup
lnFactorial(double) - Static method in class weka.core.SpecialFunctions: Returns natural logarithm of factorial using gamma function.
lnGamma(double) - Static method in class weka.core.Statistics: Returns natural logarithm of gamma function.
LNormTipText() - Method in class weka.filters.unsupervised.instance.Normalize: Returns the tip text for this property
lnsrch(double[], double[], double[], double, boolean[], double[][], Optimization.DynamicIntArray) - Method in class weka.core.Optimization: Find a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently, the positive definiteness of B matrix (approximation of the inverse of the Hessian) is preserved and no bound constraints are violated.
load(InputStream) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
load(String) - Method in class weka.gui.beans.FlowRunner: Load a serialized KnowledgeFlow (either binary or xml)
loadBinary(String) - Method in class weka.gui.beans.FlowRunner: Load a binary serialized KnowledgeFlow
Loader - Interface in weka.core.converters: Interface to something that can load Instances from an input source in some format.
Loader - Class in weka.gui.beans: Loads data sets using weka.core.converter classes
Loader() - Constructor for class weka.gui.beans.Loader
Loader - Class in weka.gui: This class is for loading resources from a JAR archive.
Loader(String) - Constructor for class weka.gui.Loader: initializes the object
LOADER_DIALOG - Static variable in class weka.gui.ConverterFileChooser: the loader dialog
LoaderBeanInfo - Class in weka.gui.beans: Bean info class for the loader bean
LoaderBeanInfo() - Constructor for class weka.gui.beans.LoaderBeanInfo
LoaderCustomizer - Class in weka.gui.beans: GUI Customizer for the loader bean
LoaderCustomizer() - Constructor for class weka.gui.beans.LoaderCustomizer
loadFile(String) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: loads the specified file
loadFile() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: loads the specified file into the table
loadFromFile(String) - Static method in class weka.core.Debug: deserializes the content of the file and returns it, null if an error occurred.
loadIcons(String, String) - Method in class weka.gui.beans.BeanVisual: Loads static and animated versions of a beans icons.
loadInitialLayout(String) - Method in class weka.gui.beans.KnowledgeFlowApp: Loads the specified file at input Added by Zerbetto
loadModel() - Method in class weka.gui.beans.Classifier
loadModel() - Method in class weka.gui.beans.Clusterer
loadProperties() - Static method in class weka.gui.beans.KnowledgeFlowApp: Loads KnowledgeFlow properties and any plugins (adds jars to the classpath)
loadXML(String) - Method in class weka.gui.beans.FlowRunner: Load an XML serialized KnowledgeFlow
localDistributionForInstance(Instance, LBR.Indexes) - Method in class weka.classifiers.lazy.LBR: Calculates the class membership probabilities.
locallyPredictiveTipText() - Method in class weka.attributeSelection.CfsSubsetEval: Returns the tip text for this property
localNaiveBayes(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR: Class for building and using a simple Naive Bayes classifier.
LocalScoreSearchAlgorithm - Class in weka.classifiers.bayes.net.search.local: The ScoreBasedSearchAlgorithm class supports Bayes net structure search algorithms that are based on maximizing scores (as opposed to for example conditional independence based search algorithms).
LocalScoreSearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: default constructor
LocalScoreSearchAlgorithm(BayesNet, Instances) - Constructor for class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: constructor
locateIndex(int) - Method in class weka.core.SparseInstance: Locates the greatest index that is not greater than the given index.
log(String) - Method in class weka.core.Debug: prints the given message with level INFO
log(Level, String) - Method in class weka.core.Debug: prints the given message with the specified level and an empty sourceclass
log(Level, String, String) - Method in class weka.core.Debug: prints the given message with the specified level
log(Level, String, String, String) - Method in class weka.core.Debug: prints the given message with the specified level
Log() - Constructor for class weka.core.Debug.Log: default constructor, uses only stdout
Log(String) - Constructor for class weka.core.Debug.Log: creates a logger that logs into the specified file, if null then only stdout is used.
Log(String, int, int) - Constructor for class weka.core.Debug.Log: creates a logger that logs into the specified file, if null then only stdout is used.
log(Level, String) - Method in class weka.core.Debug.Log: logs the given message
log(Level, String, String) - Method in class weka.core.Debug.Log: prints the given message with the specified level
log(Level, String, String, String) - Method in class weka.core.Debug.Log: prints the given message with the specified level
log(String) - Method in class weka.core.Debug.SimpleLog: logs the given message to the file
log(Logger.Level, String) - Static method in class weka.core.logging.Logger: Logs the given message under the given level.
log(Logger.Level, Throwable) - Static method in class weka.core.logging.Logger: Logs the given message under the given level.
LOG - Static variable in interface weka.core.mathematicalexpression.sym
LOG - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
LOG2 - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
log2 - Static variable in class weka.core.Utils: The natural logarithm of 2.
log2(double) - Static method in class weka.core.Utils: Returns the logarithm of a for base 2.
log2Binomial(double, double) - Static method in class weka.core.SpecialFunctions: Returns base 2 logarithm of binomial coefficient using gamma function.
log2Multinomial(double, double[]) - Static method in class weka.core.SpecialFunctions: Returns base 2 logarithm of multinomial using gamma function.
log2MultipleHypergeometric(double[][]) - Static method in class weka.core.ContingencyTables: Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.
logbinomialCoefficient(int, int) - Static method in class weka.associations.PriorEstimation: Method that calculates the base 2 logarithm of a binomial coefficient
logDensityForInstance(Instance) - Method in class weka.clusterers.AbstractDensityBasedClusterer: Computes the density for a given instance.
logDensityForInstance(Instance) - Method in interface weka.clusterers.DensityBasedClusterer: Computes the density for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.AbstractDensityBasedClusterer: Computes the log of the conditional density (per cluster) for a given instance.
logDensityPerClusterForInstance(Instance) - Method in interface weka.clusterers.DensityBasedClusterer: Computes the log of the conditional density (per cluster) for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.EM: Computes the log of the conditional density (per cluster) for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.MakeDensityBasedClusterer: Computes the log of the conditional density (per cluster) for a given instance.
logFileTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
logFunc(double) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit: Help method for computing entropy.
Logger - Class in weka.core.logging: Abstract superclass for all loggers.
Logger() - Constructor for class weka.core.logging.Logger: Initializes the logger.
Logger - Interface in weka.gui: Interface for objects that display log (permanent historical) and status (transient) messages.
Logger.Level - Enum in weka.core.logging: The logging level.
Logistic - Class in weka.classifiers.functions: Class for building and using a multinomial logistic regression model with a ridge estimator.

There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992):

If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix.

The probability for class j with the exception of the last class is

Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1)

The last class has probability

1-(sum[j=1..(k-1)]Pj(Xi))
= 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)

The (negative) multinomial log-likelihood is thus:

L = -sum[i=1..n]{
sum[j=1..(k-1)](Yij * ln(Pj(Xi)))
+(1 - (sum[j=1..(k-1)]Yij))
* ln(1 - sum[j=1..(k-1)]Pj(Xi))
} + ridge * (B^2)

In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
Logistic() - Constructor for class weka.classifiers.functions.Logistic
LogisticBase - Class in weka.classifiers.trees.lmt: Base/helper class for building logistic regression models with the LogitBoost algorithm.
LogisticBase() - Constructor for class weka.classifiers.trees.lmt.LogisticBase: Constructor that creates LogisticBase object with standard options.
LogisticBase(int, boolean, boolean) - Constructor for class weka.classifiers.trees.lmt.LogisticBase: Constructor to create LogisticBase object.
logisticLinkFunction(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression: This method computes the values for the logistic link function.
LogitBoost - Class in weka.classifiers.meta: Class for performing additive logistic regression.
LogitBoost() - Constructor for class weka.classifiers.meta.LogitBoost: Constructor.
logJointDensitiesForInstance(Instance) - Method in class weka.clusterers.AbstractDensityBasedClusterer: Returns the logs of the joint densities for a given instance.
logJointDensitiesForInstance(Instance) - Method in interface weka.clusterers.DensityBasedClusterer: Returns the logs of the joint densities for a given instance.
LogLikelihood - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression: Log-likelihood values to be used to choose the best hyperparameter.
logMessage(String) - Method in class weka.gui.beans.FlowRunner.SimpleLogger
logMessage(String) - Method in class weka.gui.beans.LogPanel: Sends the supplied message to the log area.
logMessage(String) - Method in interface weka.gui.Logger: Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.LogPanel: Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.SysErrLog: Sends the supplied message to the log area.
LogPanel - Class in weka.gui.beans: Class for displaying a status area (made up of a variable number of lines) and a log area.
LogPanel() - Constructor for class weka.gui.beans.LogPanel
LogPanel - Class in weka.gui: This panel allows log and status messages to be posted.
LogPanel() - Constructor for class weka.gui.LogPanel: Creates the log panel with no task monitor and the log always visible.
LogPanel(WekaTaskMonitor) - Constructor for class weka.gui.LogPanel: Creates the log panel with a task monitor, where the log is hidden.
LogPanel(WekaTaskMonitor, boolean) - Constructor for class weka.gui.LogPanel: Creates the log panel, possibly with task monitor, where the log is optionally hidden.
LogPanel(WekaTaskMonitor, boolean, boolean, boolean) - Constructor for class weka.gui.LogPanel: Creates the log panel, possibly with task monitor, where the either the log is optionally hidden or the status (having both hidden is not allowed).
logPSI - Static variable in class weka.core.matrix.Maths: The constant - log( sqrt(2 pi) )
logs2probs(double[]) - Static method in class weka.core.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logScore(int, int) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Gets the log score contribution of this distribution
logScore(int) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: logScore returns the log of the quality of a network (e.g.
logScore(int, int) - Method in interface weka.classifiers.bayes.net.search.local.Scoreable: Returns log-score
logSystemInfo() - Method in class weka.core.Debug.Log: a convenience method for dumping the current system info in the log file
logSystemInfo() - Method in class weka.core.Debug.SimpleLog: a convenience method for dumping the current system info in the log file
LogWindow - Class in weka.gui: Frame that shows the output from stdout and stderr.
LogWindow() - Constructor for class weka.gui.LogWindow: creates the frame
LogWriter - Interface in weka.gui.beans: Interface to be implemented by classes that should be able to write their own output to the Weka logger.
LONG - Static variable in class weka.experiment.DatabaseUtils: Type mapping for LONG used for reading experiment results.
LookAndFeel - Class in weka.gui: A little helper class for setting the Look and Feel of the user interface.
LookAndFeel() - Constructor for class weka.gui.LookAndFeel
lookupCacheSizeTipText() - Method in class weka.attributeSelection.BestFirst: Returns the tip text for this property
lookupCacheSizeTipText() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the tip text for this property
lookupCacheSizeTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the tip text for this property
LOSS_STRING - Variable in class weka.experiment.ResultMatrix: loss string
lossFunctionTipText() - Method in class weka.classifiers.functions.SPegasos: Returns the tip text for this property
lossTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
LovinsStemmer - Class in weka.core.stemmers: A stemmer based on the Lovins stemmer, described here:

Julie Beth Lovins (1968).
LovinsStemmer() - Constructor for class weka.core.stemmers.LovinsStemmer
LOW_MEMORY_MINIMUM - Static variable in class weka.core.Memory
lowerBoundMinSupportTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
lowerBoundMinSupportTipText() - Method in class weka.associations.FPGrowth: Returns the tip text for this property
lowerCaseTokensTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
lowerNumericBoundIsOpen() - Method in class weka.core.Attribute: Returns whether the lower numeric bound of the attribute is open.
lowerSizeTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
LPAREN - Static variable in interface weka.core.mathematicalexpression.sym
LPAREN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
lsqr(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: QR transformation for a least squares problem
A x = b
implicitly both A and b are transformed.
lsqrSelection(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: QR transformation for a least squares problem
A x = b
implicitly both A and b are transformed.
LT - Static variable in interface weka.core.mathematicalexpression.sym
LT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
lu() - Method in class weka.core.matrix.Matrix: LU Decomposition
LUDecomposition - Class in weka.core.matrix: LU Decomposition.
LUDecomposition() - Method in class weka.core.Matrix: Deprecated.

Performs a LUDecomposition on the matrix.
LUDecomposition(Matrix) - Constructor for class weka.core.matrix.LUDecomposition: LU Decomposition
LWL - Class in weka.classifiers.lazy: Locally weighted learning.
LWL() - Constructor for class weka.classifiers.lazy.LWL: Constructor.

M

M5Base - Class in weka.classifiers.trees.m5: M5Base.
M5Base() - Constructor for class weka.classifiers.trees.m5.M5Base: Constructor
M5P - Class in weka.classifiers.trees: M5Base.
M5P() - Constructor for class weka.classifiers.trees.M5P: Creates a new M5P instance.
M5Rules - Class in weka.classifiers.rules: Generates a decision list for regression problems using separate-and-conquer.
M5Rules() - Constructor for class weka.classifiers.rules.M5Rules: Constructor
m_ADNodes - Variable in class weka.classifiers.bayes.net.VaryNode: list of ADNode children
m_alpha - Variable in class weka.classifiers.functions.supportVector.RegOptimizer: alpha and alpha* arrays containing weights for solving dual problem
m_alpha - Variable in class weka.classifiers.trees.lmt.LMTNode: Alpha-value (for pruning) at the node
m_alphaStar - Variable in class weka.classifiers.functions.supportVector.RegOptimizer
m_alwaysDisplayPointsOfThisSize - Variable in class weka.gui.visualize.PlotData2D: If the shape size of a point equals this size then always plot it (i.e.
m_AttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes: the array attribute indexes
M_AVERAGE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
m_children - Variable in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
m_ClassIndex - Variable in class weka.classifiers.lazy.LBR.Indexes: the Class Index for the data set
m_col - Variable in class weka.gui.treevisualizer.NamedColor: The actual color object
m_cols - Variable in class weka.gui.treevisualizer.Colors: The array with all the colors input
m_CoordCount - Variable in class weka.core.neighboursearch.PerformanceStats: The number of coordinates looked at for the current/last query.
m_CurrDebugFlag - Variable in class weka.clusterers.XMeans: Flag: I'm debugging.
m_customColour - Variable in class weka.gui.visualize.PlotData2D
m_defaultExpression - Static variable in class weka.filters.unsupervised.attribute.MathExpression: The default modification expression
M_DELETE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants: Missing value handling mode
m_displayAllPoints - Variable in class weka.gui.visualize.PlotData2D: Display all points (ie.
m_Distributions - Variable in class weka.classifiers.bayes.BayesNet: The attribute estimators containing CPTs.
m_End - Variable in class weka.core.neighboursearch.balltrees.BallNode: The end index of the portion of the master index array, which stores indices of the instances/points the node contains.
m_End - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: The end index of the portion of the master index array, which stores indices of the instances/points the node contains.
m_experimentFinished - Variable in class weka.experiment.RemoteExperimentEvent: True if a remote experiment has finished
m_Filter - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
m_indexVal - Variable in class weka.gui.visualize.AttributePanelEvent: The index for the new attribute
m_iNode - Variable in class weka.classifiers.bayes.net.VaryNode: index of the node varied
m_Instances - Variable in class weka.classifiers.bayes.BayesNet: The dataset header for the purposes of printing out a semi-intelligible model
m_Instances - Variable in class weka.classifiers.bayes.net.ADNode: list of Instance children (either m_Instances or m_VaryNodes is instantiated)
m_InstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes: the array instance indexes
m_Left - Variable in class weka.core.neighboursearch.balltrees.BallNode: The left child of the node.
m_Left - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: left subtree; contains instances with smaller or equal to split value.
m_logMessage - Variable in class weka.experiment.RemoteExperimentEvent: A log type message
m_MaxC - Variable in class weka.core.neighboursearch.PerformanceStats: The min and max coordinates(attributes) looked at per query.
M_MAXDIFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
m_MaxP - Variable in class weka.core.neighboursearch.PerformanceStats: The min and max data points looked for a query by the NNS algorithm.
m_messageString - Variable in class weka.experiment.RemoteExperimentEvent: The message
m_MinC - Variable in class weka.core.neighboursearch.PerformanceStats: The min and max coordinates(attributes) looked at per query.
m_MinP - Variable in class weka.core.neighboursearch.PerformanceStats: The min and max data points looked for a query by the NNS algorithm.
m_name - Variable in class weka.gui.treevisualizer.NamedColor: The name of the color
m_nCount - Variable in class weka.classifiers.bayes.net.ADNode: count
m_nMCV - Variable in class weka.classifiers.bayes.net.VaryNode: most common value
m_nNodes - Variable in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode: nodes of the Bayes net in this junction node
m_NodeNumber - Variable in class weka.core.neighboursearch.balltrees.BallNode: The node number/id.
m_NodeNumber - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: node number (only for debug).
m_NodeRanges - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: lowest and highest value and width (= high - low) for each dimension.
m_NodesRectBounds - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: The lo and high bounds of the hyper rectangle described by the node.
M_NORMAL - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
m_nStartNode - Variable in class weka.classifiers.bayes.net.ADNode: first node in VaryNode array
m_NumAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes: the number of attributes "in use" or set to a the original value (true or false)
m_numIncorrectModel - Variable in class weka.classifiers.trees.lmt.LMTNode: Weighted number of training examples currently misclassified by the logistic model at the node
m_numIncorrectTree - Variable in class weka.classifiers.trees.lmt.LMTNode: Weighted number of training examples currently misclassified by the subtree rooted at the node
m_NumInstances - Variable in class weka.core.neighboursearch.balltrees.BallNode: The number of instances/points in the node.
m_NumInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes: the number of instances "in use" or set to a the original value (true or false)
m_numParameters - Variable in class weka.classifiers.trees.m5.RuleNode: the number of paramters in the chosen model for this node---either the subtree model or the linear model.
m_NumSeqAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes: the number of sequential attributes "in use" or set to a the original value (true or false)
m_NumSeqInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes: the number of sequential instances "in use" or set to a the original value (true or false)
m_OutputFormat - Variable in class weka.core.Debug.Clock: the format of the output
m_outputTypes - Variable in class weka.core.Debug.DBO: range of outputtyp
m_PointCount - Variable in class weka.core.neighboursearch.PerformanceStats: The number of data points looked at for the current/last query.
m_Right - Variable in class weka.core.neighboursearch.balltrees.BallNode: The right child of the node.
m_Right - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: right subtree; contains instances with larger than split value.
m_root - Variable in class weka.classifiers.bayes.net.MarginCalculator
m_seed - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: seed for randomizing the instances before CV
m_SequentialAttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes: an array of attribute indexes that are set to either true or false
m_SequentialInstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes: the array of instance indexes that are set to a either true or false
m_SplitAttrib - Variable in class weka.core.neighboursearch.balltrees.BallNode: The attribute that splits this node (not always used).
m_SplitDim - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: attribute to split on.
m_SplitVal - Variable in class weka.core.neighboursearch.balltrees.BallNode: The value of m_SpiltAttrib that splits this node (not always used).
m_SplitValue - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: value to split on.
m_Start - Variable in class weka.core.neighboursearch.balltrees.BallNode: The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.
m_Start - Variable in class weka.core.neighboursearch.kdtrees.KDTreeNode: The start index of the portion of the master index array, which stores the indices of the instances/points the node contains.
m_statusMessage - Variable in class weka.experiment.RemoteExperimentEvent: A status type message
m_SumC - Variable in class weka.core.neighboursearch.PerformanceStats: The sum of coordinates/attributes looked at for all the queries.
m_SumP - Variable in class weka.core.neighboursearch.PerformanceStats: The sum of data points looked at for all the queries.
m_SumSqC - Variable in class weka.core.neighboursearch.PerformanceStats: The squared sum of coordinates/attributes looked at for all the queries.
m_SumSqP - Variable in class weka.core.neighboursearch.PerformanceStats: The squared sum of data points looked at for all the queries.
m_useCustomColour - Variable in class weka.gui.visualize.PlotData2D: Custom colour for this plot
m_UseWordwrap - Variable in class weka.gui.LogWindow: whether the JTextPane has wordwrap or not
m_VaryNodes - Variable in class weka.classifiers.bayes.net.ADNode: list of VaryNode children
m_verboseOn - Variable in class weka.core.Debug.DBO: enables/disables output of debug information
m_xChange - Variable in class weka.gui.visualize.AttributePanelEvent: True if the x selection changed
m_yChange - Variable in class weka.gui.visualize.AttributePanelEvent: True if the y selection changed
MahalanobisEstimator - Class in weka.estimators: Simple probability estimator that places a single normal distribution over the observed values.
MahalanobisEstimator(Matrix, double, double) - Constructor for class weka.estimators.MahalanobisEstimator: Constructor
main(String[]) - Static method in class weka.associations.Apriori: Main method.
main(String[]) - Static method in class weka.associations.AssociatorEvaluation: A test method for this class.
main(String[]) - Static method in class weka.associations.CheckAssociator: Test method for this class
main(String[]) - Static method in class weka.associations.FilteredAssociator: Main method for running this class.
main(String[]) - Static method in class weka.associations.FPGrowth: Main method.
main(String[]) - Static method in class weka.associations.GeneralizedSequentialPatterns: Main method.
main(String[]) - Static method in class weka.associations.PredictiveApriori: Main method.
main(String[]) - Static method in class weka.associations.Tertius: Main method.
main(String[]) - Static method in class weka.attributeSelection.AttributeSelection: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CfsSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CheckAttributeSelection: Test method for this class
main(String[]) - Static method in class weka.attributeSelection.ChiSquaredAttributeEval: Main method.
main(String[]) - Static method in class weka.attributeSelection.ClassifierSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ConsistencySubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CostSensitiveAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CostSensitiveSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.FilteredAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.FilteredSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.GainRatioAttributeEval: Main method.
main(String[]) - Static method in class weka.attributeSelection.InfoGainAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.LatentSemanticAnalysis: Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.OneRAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.PrincipalComponents: Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.ReliefFAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SVMAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.WrapperSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.AODE: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.AODEsr: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNet: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.ComplementNaiveBayes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.DMNBtext: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.HNB: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesMultinomial: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesSimple: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesUpdateable: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.net.ADNode: for testing only
main(String[]) - Static method in class weka.classifiers.bayes.net.BayesNetGenerator: Main method
main(String[]) - Static method in class weka.classifiers.bayes.net.BIFReader: Loads the file specified as first parameter and prints it to stdout.
main(String[]) - Static method in class weka.classifiers.bayes.net.EditableBayesNet
main(String[]) - Static method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorFullBayes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.net.GUI: Main method.
main(String[]) - Static method in class weka.classifiers.bayes.net.MarginCalculator
main(String[]) - Static method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: for testing the class
main(String[]) - Static method in class weka.classifiers.bayes.WAODE: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.BVDecompose: Test method for this class
main(String[]) - Static method in class weka.classifiers.BVDecomposeSegCVSub: Test method for this class
main(String[]) - Static method in class weka.classifiers.CheckClassifier: Test method for this class
main(String[]) - Static method in class weka.classifiers.CheckSource: Executes the tests, use "-h" to list the commandline options.
main(String[]) - Static method in class weka.classifiers.evaluation.CostCurve: Tests the CostCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.Evaluation: A test method for this class.
main(String[]) - Static method in class weka.classifiers.evaluation.MarginCurve: Tests the MarginCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.ThresholdCurve: Tests the ThresholdCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.functions.GaussianProcesses: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.IsotonicRegression: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.LeastMedSq: generate a Linear regression predictor for testing
main(String[]) - Static method in class weka.classifiers.functions.LibLINEAR: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.LibSVM: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.LinearRegression: Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.Logistic: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.MultilayerPerceptron: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.pace.ChisqMixture: Method to test this class
main(String[]) - Static method in class weka.classifiers.functions.pace.DiscreteFunction
main(String[]) - Static method in class weka.classifiers.functions.pace.NormalMixture: Method to test this class
main(String[]) - Static method in class weka.classifiers.functions.pace.PaceMatrix: for testing only
main(String[]) - Static method in class weka.classifiers.functions.PaceRegression: Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.PLSClassifier: Main method for running this classifier from commandline.
main(String[]) - Static method in class weka.classifiers.functions.RBFNetwork: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.SimpleLinearRegression: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.SimpleLogistic: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.SMO: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.SMOreg: Main method for running this classifier.
main(String[]) - Static method in class weka.classifiers.functions.SPegasos: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.supportVector.CheckKernel: Test method for this class
main(String[]) - Static method in class weka.classifiers.functions.supportVector.KernelEvaluation: A test method for this class.
main(String[]) - Static method in class weka.classifiers.functions.VotedPerceptron: Main method.
main(String[]) - Static method in class weka.classifiers.functions.Winnow: Main method.
main(String[]) - Static method in class weka.classifiers.lazy.IB1: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.IBk: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.KStar: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LBR: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LWL: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdaBoostM1: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdditiveRegression: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AttributeSelectedClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Bagging: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaClustering: Runs the classifier with the given options
main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaRegression: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CostSensitiveClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CVParameterSelection: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Dagging: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Decorate: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.END: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.FilteredClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Grading: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.GridSearch: Main method for running this classifier from commandline.
main(String[]) - Static method in class weka.classifiers.meta.LogitBoost: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MetaCost: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiBoostAB: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiClassClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiScheme: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.nestedDichotomies.ND: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.OrdinalClassClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Main method for this class.
main(String[]) - Static method in class weka.classifiers.meta.RandomCommittee: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RandomSubSpace: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RegressionByDiscretization: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RotationForest: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Stacking: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.StackingC: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ThresholdSelector: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Vote: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.CitationKNN: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MDD: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MIBoost: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MIDD: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MIEMDD: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MILR: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MINND: Main method for testing.
main(String[]) - Static method in class weka.classifiers.mi.MIOptimalBall: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MISMO: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MISVM: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.MIWrapper: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.mi.SimpleMI: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.HyperPipes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.SerializedClassifier: Runs the classifier with the given options
main(String[]) - Static method in class weka.classifiers.misc.VFI: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.ConjunctiveRule: Main method.
main(String[]) - Static method in class weka.classifiers.rules.DecisionTable: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.DTNB: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.JRip: Main method.
main(String[]) - Static method in class weka.classifiers.rules.M5Rules: Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.rules.NNge: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.OneR: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.rules.PART: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.Prism: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.rules.Ridor: Main method.
main(String[]) - Static method in class weka.classifiers.rules.ZeroR: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.ADTree: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.BFTree: Main method.
main(String[]) - Static method in class weka.classifiers.trees.DecisionStump: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.FT: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.Id3: Main method.
main(String[]) - Static method in class weka.classifiers.trees.J48: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.J48graft: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.LADTree: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.LMT: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.M5P: Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.trees.NBTree: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.RandomForest: Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.RandomTree: Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.REPTree: Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.SimpleCart: Main method.
main(String[]) - Static method in class weka.classifiers.trees.UserClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.CheckClusterer: Test method for this class
main(String[]) - Static method in class weka.clusterers.CLOPE: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.ClusterEvaluation: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.Cobweb: Main method.
main(String[]) - Static method in class weka.clusterers.DBSCAN: Main Method for testing DBSCAN
main(String[]) - Static method in class weka.clusterers.EM: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.FarthestFirst: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.FilteredClusterer: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer: Displays the GUI.
main(String[]) - Static method in class weka.clusterers.HierarchicalClusterer
main(String[]) - Static method in class weka.clusterers.MakeDensityBasedClusterer: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.OPTICS: Main Method for testing OPTICS
main(String[]) - Static method in class weka.clusterers.sIB
main(String[]) - Static method in class weka.clusterers.SimpleKMeans: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.XMeans: Main method for testing this class.
main(String[]) - Static method in class weka.core.AlgVector: Main method for testing this class, can take an ARFF file as first argument.
main(String[]) - Static method in class weka.core.AllJavadoc: Parses the given commandline parameters and generates the Javadoc.
main(String[]) - Static method in class weka.core.Attribute: Simple main method for testing this class.
main(String[]) - Static method in class weka.core.BinarySparseInstance: Main method for testing this class.
main(String[]) - Static method in class weka.core.Capabilities: loads the given dataset and prints the Capabilities necessary to process it.
main(String[]) - Static method in class weka.core.CheckGOE: Main method for using the CheckGOE.
main(String[]) - Static method in class weka.core.CheckOptionHandler: Main method for using the CheckOptionHandler.
main(String[]) - Static method in class weka.core.ClassDiscovery: Possible calls: weka.core.ClassDiscovery <packages>
Prints all the packages in the current classpath weka.core.ClassDiscovery <classname> <packagename(s)>
Prints the classes it found.
main(String[]) - Static method in class weka.core.ContingencyTables: Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.ArffLoader: Main method.
main(String[]) - Static method in class weka.core.converters.ArffSaver: Main method.
main(String[]) - Static method in class weka.core.converters.C45Loader: Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.C45Saver: Main method.
main(String[]) - Static method in class weka.core.converters.ConverterUtils.DataSink: for testing only - takes a data file as input and a data file for the output.
main(String[]) - Static method in class weka.core.converters.ConverterUtils.DataSource: for testing only - takes a data file as input.
main(String[]) - Static method in class weka.core.converters.CSVLoader: Main method.
main(String[]) - Static method in class weka.core.converters.CSVSaver: Main method.
main(String[]) - Static method in class weka.core.converters.DatabaseLoader: Main method.
main(String[]) - Static method in class weka.core.converters.DatabaseSaver: Main method.
main(String[]) - Static method in class weka.core.converters.LibSVMLoader: Main method.
main(String[]) - Static method in class weka.core.converters.LibSVMSaver: Main method.
main(String[]) - Static method in class weka.core.converters.SerializedInstancesLoader: Main method.
main(String[]) - Static method in class weka.core.converters.SerializedInstancesSaver: Main method.
main(String[]) - Static method in class weka.core.converters.SVMLightLoader: Main method.
main(String[]) - Static method in class weka.core.converters.SVMLightSaver: Main method.
main(String[]) - Static method in class weka.core.converters.TextDirectoryLoader: Main method.
main(String[]) - Static method in class weka.core.converters.XRFFLoader: Main method.
main(String[]) - Static method in class weka.core.converters.XRFFSaver: Main method.
main(String[]) - Static method in class weka.core.Copyright: Only for testing
main(String[]) - Static method in class weka.core.Environment: Main method for testing this class.
main(String[]) - Static method in class weka.core.FindWithCapabilities: Executes the location of classes with parameters from the commandline.
main(String[]) - Static method in class weka.core.GlobalInfoJavadoc: Parses the given commandline parameters and generates the Javadoc.
main(String[]) - Static method in class weka.core.Instance: Main method for testing this class.
main(String[]) - Static method in class weka.core.InstanceComparator: for testing only.
main(String[]) - Static method in class weka.core.Instances: Main method for this class.
main(String[]) - Static method in class weka.core.Jython: If no arguments are given, it just prints the presence of the Jython classes, otherwise it expects a Jython filename to execute.
main(String[]) - Static method in class weka.core.ListOptions: runs the javadoc producer with the given commandline options
main(String[]) - Static method in class weka.core.mathematicalexpression.Parser: Runs the parser from commandline.
main(String[]) - Static method in class weka.core.matrix.DoubleVector
main(String[]) - Static method in class weka.core.matrix.IntVector: Tests the IntVector class
main(String[]) - Static method in class weka.core.Matrix: Deprecated.

Main method for testing this class.
main(String[]) - Static method in class weka.core.matrix.Matrix: Main method for testing this class.
main(String[]) - Static method in class weka.core.Memory: prints only some statistics
main(String[]) - Static method in class weka.core.neighboursearch.CoverTree: Method for testing the class from command line.
main(String[]) - Static method in class weka.core.OptionHandlerJavadoc: Parses the given commandline parameters and generates the Javadoc.
main(String[]) - Static method in class weka.core.pmml.Constant
main(String[]) - Static method in class weka.core.pmml.PMMLFactory
main(String[]) - Static method in class weka.core.PropertyPath: for testing only
main(String[]) - Static method in class weka.core.Queue: Main method for testing this class.
main(String[]) - Static method in class weka.core.RandomVariates: Main method for testing this class.
main(String[]) - Static method in class weka.core.Range: Main method for testing this class.
main(String[]) - Static method in class weka.core.RevisionUtils: For testing only.
main(String[]) - Static method in class weka.core.SerializationHelper: Outputs information about a class on the commandline, takes class name as arguments.
main(String[]) - Static method in class weka.core.SingleIndex: Main method for testing this class.
main(String[]) - Static method in class weka.core.SparseInstance: Main method for testing this class.
main(String[]) - Static method in class weka.core.SpecialFunctions: Main method for testing this class.
main(String[]) - Static method in class weka.core.Statistics: Main method for testing this class.
main(String[]) - Static method in class weka.core.stemmers.IteratedLovinsStemmer: Runs the stemmer with the given options
main(String[]) - Static method in class weka.core.stemmers.LovinsStemmer: Runs the stemmer with the given options
main(String[]) - Static method in class weka.core.stemmers.NullStemmer: Runs the stemmer with the given options
main(String[]) - Static method in class weka.core.stemmers.SnowballStemmer: Runs the stemmer with the given options.
main(String[]) - Static method in class weka.core.Stopwords: Accepts the following parameter:
-i file
loads the stopwords from the given file
-o file
saves the stopwords to the given file
-p
outputs the current stopwords on stdout
Any additional parameters are interpreted as words to test as stopwords.
main(String[]) - Static method in class weka.core.SystemInfo: for printing the system info to stdout.
main(String[]) - Static method in class weka.core.TechnicalInformation: Prints some examples of technical informations if there are no commandline options given.
main(String[]) - Static method in class weka.core.TechnicalInformationHandlerJavadoc: Parses the given commandline parameters and generates the Javadoc.
main(String[]) - Static method in class weka.core.TestInstances: for running the class from commandline, prints the generated data to stdout
main(String[]) - Static method in class weka.core.tokenizers.AlphabeticTokenizer: Runs the tokenizer with the given options and strings to tokenize.
main(String[]) - Static method in class weka.core.tokenizers.NGramTokenizer: Runs the tokenizer with the given options and strings to tokenize.
main(String[]) - Static method in class weka.core.tokenizers.WordTokenizer: Runs the tokenizer with the given options and strings to tokenize.
main(String[]) - Static method in class weka.core.Trie: Only for testing (prints the built Trie).
main(String[]) - Static method in class weka.core.Utils: Main method for testing this class.
main(String[]) - Static method in class weka.core.Version: only for testing
main(String[]) - Static method in class weka.core.xml.SerialUIDChanger: exchanges an old UID for a new one.
main(String[]) - Static method in class weka.core.xml.XMLDocument: for testing only.
main(String[]) - Static method in class weka.core.xml.XMLInstances: takes an XML document as first argument and then outputs the Instances statistics
main(String[]) - Static method in class weka.core.xml.XMLOptions: for testing only.
main(String[]) - Static method in class weka.core.xml.XMLSerialization: for testing only.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.Agrawal: Main method for executing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.BayesNet: Main method for executing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.LED24: Main method for executing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.RandomRBF: Main method for executing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.classification.RDG1: Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.regression.Expression: Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.classifiers.regression.MexicanHat: Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.clusterers.BIRCHCluster: Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.clusterers.SubspaceCluster: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.CheckEstimator: Test method for this class
main(String[]) - Static method in class weka.estimators.DDConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DiscreteEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DKConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DNConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KDConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KernelEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KKConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.MahalanobisEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NDConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NNConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NormalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.PoissonEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.experiment.CrossValidationResultProducer: Quick test of timestamp
main(String[]) - Static method in class weka.experiment.Experiment: Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.InstanceQuery: Test the class from the command line.
main(String[]) - Static method in class weka.experiment.OutputZipper: Main method for testing this class
main(String[]) - Static method in class weka.experiment.PairedCorrectedTTester: Test the class from the command line.
main(String[]) - Static method in class weka.experiment.PairedStats: Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.PairedTTester: Test the class from the command line.
main(String[]) - Static method in class weka.experiment.RemoteEngine: Main method.
main(String[]) - Static method in class weka.experiment.RemoteExperiment: Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.ResultMatrixCSV: for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixGnuPlot: for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixHTML: for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixLatex: for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixPlainText: for testing only
main(String[]) - Static method in class weka.experiment.ResultMatrixSignificance: for testing only
main(String[]) - Static method in class weka.experiment.Stats: Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.xml.XMLExperiment: for testing only.
main(String[]) - Static method in class weka.filters.AllFilter: Main method for testing this class.
main(String[]) - Static method in class weka.filters.CheckSource: Executes the tests, use "-h" to list the commandline options.
main(String[]) - Static method in class weka.filters.Filter: Main method for testing this class.
main(String[]) - Static method in class weka.filters.MultiFilter: Main method for executing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.AddClassification: runs the filter with the given arguments.
main(String[]) - Static method in class weka.filters.supervised.attribute.AttributeSelection: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.ClassOrder: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.Discretize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.NominalToBinary: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.PLSFilter: runs the filter with the given arguments.
main(String[]) - Static method in class weka.filters.supervised.instance.Resample: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.SMOTE: Main method for running this filter.
main(String[]) - Static method in class weka.filters.supervised.instance.SpreadSubsample: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Add: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddCluster: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddExpression: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddID: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddNoise: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddValues: Main method for testing and running this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Center: Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ClassAssigner: Main method for executing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ClusterMembership: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Copy: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Discretize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.FirstOrder: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.InterquartileRange: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.KernelFilter: runs the filter with the given arguments
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MakeIndicator: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MathExpression: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MergeTwoValues: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NominalToBinary: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NominalToString: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Normalize: Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericCleaner: Runs the filter from commandline, use "-h" to see all options.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericToBinary: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericToNominal: Runs the filter with the given parameters.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericTransform: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Obfuscate: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Main method for executing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PKIDiscretize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PrincipalComponents: Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RandomProjection: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RandomSubset: Runs the filter with the given parameters.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RELAGGS: runs the filter with the given arguments
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Remove: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveType: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveUseless: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Reorder: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Standardize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToNominal: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToWordVector: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SwapValues: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesDelta: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Wavelet: runs the filter with the given arguments
main(String[]) - Static method in class weka.filters.unsupervised.instance.NonSparseToSparse: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Normalize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Randomize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFolds: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveMisclassified: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemovePercentage: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveRange: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveWithValues: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Resample: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.ReservoirSample: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.SparseToNonSparse: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.SubsetByExpression: Main method for running this filter.
main(String[]) - Static method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Runs the parser from commandline.
main(String[]) - Static method in class weka.gui.arffviewer.ArffViewer: shows the frame and it tries to load all the arff files that were provided as arguments.
main(String[]) - Static method in class weka.gui.AttributeListPanel: Tests the attribute list panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeSelectionPanel: Tests the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeSummaryPanel: Tests out the attribute summary panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeVisualizationPanel: Main method to test this class from command line
main(String[]) - Static method in class weka.gui.beans.AttributeSummarizer
main(String[]) - Static method in class weka.gui.beans.CostBenefitAnalysis
main(String[]) - Static method in class weka.gui.beans.DataVisualizer
main(String[]) - Static method in class weka.gui.beans.FlowRunner: Main method for testing this class.
main(String[]) - Static method in class weka.gui.beans.KnowledgeFlow: Shows the splash screen, launches the application and then disposes the splash screen.
main(String[]) - Static method in class weka.gui.beans.KnowledgeFlowApp: Main method.
main(String[]) - Static method in class weka.gui.beans.Loader
main(String[]) - Static method in class weka.gui.beans.LogPanel: Main method to test this class.
main(String[]) - Static method in class weka.gui.beans.ModelPerformanceChart
main(String[]) - Static method in class weka.gui.beans.Saver: The main method for testing
main(String[]) - Static method in class weka.gui.beans.ScatterPlotMatrix
main(String[]) - Static method in class weka.gui.beans.StripChart: Tests out the StripChart from the command line
main(String[]) - Static method in class weka.gui.beans.TextViewer
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanel: Main method for testing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Main method for testing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Main method for testing this class
main(String[]) - Static method in class weka.gui.ConverterFileChooser: For testing the file chooser
main(String[]) - Static method in class weka.gui.DatabaseConnectionDialog: for testing only
main(String[]) - Static method in class weka.gui.experiment.AlgorithmListPanel: Tests out the algorithm list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DatasetListPanel: Tests out the dataset list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DistributeExperimentPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.Experimenter: Tests out the experiment environment.
main(String[]) - Static method in class weka.gui.experiment.ExperimenterDefaults: only for testing - prints the content of the props file
main(String[]) - Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.HostListPanel: Tests out the host list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.OutputFormatDialog: for testing only.
main(String[]) - Static method in class weka.gui.experiment.ResultsPanel: Tests out the results panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunNumberPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunPanel: Tests out the run panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.SetupPanel: Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.explorer.AssociationsPanel: Tests out the Associator panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.AttributeSelectionPanel: Tests out the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClassifierPanel: Tests out the classifier panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClustererPanel: Tests out the clusterer panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.Explorer: Tests out the explorer environment.
main(String[]) - Static method in class weka.gui.explorer.ExplorerDefaults: only for testing - prints the content of the props file.
main(String[]) - Static method in class weka.gui.explorer.PreprocessPanel: Tests out the instance-preprocessing panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.VisualizePanel: Tests out the visualize panel from the command line.
main(String[]) - Static method in class weka.gui.GenericArrayEditor: Tests out the array editor from the command line.
main(String[]) - Static method in class weka.gui.GenericObjectEditor: Tests out the Object editor from the command line.
main(String[]) - Static method in class weka.gui.GenericPropertiesCreator: for generating props file: no parameter: see default constructor 1 parameter (i.e., filename): see default constructor + setOutputFilename(String) 2 parameters (i.e, filenames): see constructor with String argument + setOutputFilename(String)
main(String[]) - Static method in class weka.gui.graphvisualizer.GraphVisualizer: Main method to load a text file with the description of a graph from the command line
main(String[]) - Static method in class weka.gui.GUIChooser: Tests out the GUIChooser environment.
main(String[]) - Static method in class weka.gui.HierarchyPropertyParser: Tests out the parser.
main(String[]) - Static method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer: Main method for testing this class.
main(String[]) - Static method in class weka.gui.InstancesSummaryPanel: Tests out the instance summary panel from the command line.
main(String[]) - Static method in class weka.gui.ListSelectorDialog: Tests out the list selector from the command line.
main(String[]) - Static method in class weka.gui.LogPanel: Tests out the log panel from the command line.
main(String[]) - Static method in class weka.gui.LogWindow: for testing only
main(String[]) - Static method in class weka.gui.LookAndFeel: prints all the available LnFs to stdout
Main - Class in weka.gui: Menu-based GUI for Weka, replacement for the GUIChooser.
Main() - Constructor for class weka.gui.Main: default constructor.
main(String[]) - Static method in class weka.gui.Main: starts the application.
main(String[]) - Static method in class weka.gui.PropertySelectorDialog: Tests out the property selector from the command line.
main(String[]) - Static method in class weka.gui.ResultHistoryPanel: Tests out the result history from the command line.
main(String[]) - Static method in class weka.gui.SaveBuffer: Main method for testing this class
main(String[]) - Static method in class weka.gui.SelectedTagEditor: Tests out the selectedtag editor from the command line.
main(String[]) - Static method in class weka.gui.SimpleCLI: Method to start up the simple cli
main(String[]) - Static method in class weka.gui.SimpleCLIPanel: Main method for testing this class.
main(String[]) - Static method in class weka.gui.sql.SqlViewer: starts the SQL-Viewer interface.
main(String[]) - Static method in class weka.gui.sql.SqlViewerDialog: for testing only
main(String[]) - Static method in class weka.gui.treevisualizer.TreeVisualizer: Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.AttributePanel: Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.BMPWriter: for testing only
main(String[]) - Static method in class weka.gui.visualize.ClassPanel: Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.JPEGWriter: for testing only.
main(String[]) - Static method in class weka.gui.visualize.LegendPanel: Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.MatrixPanel: Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.Plot2D: Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.PNGWriter: for testing only
main(String[]) - Static method in class weka.gui.visualize.PostscriptWriter: for testing only
main(String[]) - Static method in class weka.gui.visualize.ThresholdVisualizePanel: Starts the ThresholdVisualizationPanel with parameters from the command line.
main(String[]) - Static method in class weka.gui.visualize.VisualizePanel: Main method for testing this class
main(String[]) - Static method in class weka.gui.WekaTaskMonitor: Main method for testing this class
Main.BackgroundDesktopPane - Class in weka.gui: DesktopPane with background image.
Main.ChildFrameMDI - Class in weka.gui: Specialized JInternalFrame class.
Main.ChildFrameSDI - Class in weka.gui: Specialized JFrame class.
MainMenuExtension - Interface in weka.gui: Classes implementing this interface will be displayed in the "Extensions" menu in the main GUI of Weka.
MAJOR - Static variable in class weka.core.Version: the major version
MAJORITY_VOTING_RULE - Static variable in class weka.classifiers.meta.Vote: combination rule: Majority Voting (only nominal classes)
majorityClassTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
makeADTree(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.net.ADNode: create sub tree
makeADTree(Instances) - Static method in class weka.classifiers.bayes.net.ADNode: create AD tree from set of instances
makeBinaryTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
makeBinaryTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
makeCopies(Associator, int) - Static method in class weka.associations.AbstractAssociator: Creates copies of the current associator.
makeCopies(ASEvaluation, int) - Static method in class weka.attributeSelection.ASEvaluation: Creates copies of the current evaluator.
makeCopies(ASSearch, int) - Static method in class weka.attributeSelection.ASSearch: Creates copies of the current search scheme.
makeCopies(Classifier, int) - Static method in class weka.classifiers.Classifier: Creates a given number of deep copies of the given classifier using serialization.
makeCopies(Kernel, int) - Static method in class weka.classifiers.functions.supportVector.Kernel: Creates a given number of deep or shallow (if the kernel implements Copyable) copies of the given kernel using serialization.
makeCopies(Clusterer, int) - Static method in class weka.clusterers.AbstractClusterer: Creates copies of the current clusterer.
makeCopies(DensityBasedClusterer, int) - Static method in class weka.clusterers.AbstractDensityBasedClusterer: Creates copies of the current clusterer.
makeCopies(Estimator, int) - Static method in class weka.estimators.Estimator: Creates a given number of deep copies of the given estimator using serialization.
makeCopies(Filter, int) - Static method in class weka.filters.Filter: Creates a given number of deep copies of the given filter using serialization.
makeCopy(Associator) - Static method in class weka.associations.AbstractAssociator: Creates a deep copy of the given associator using serialization.
makeCopy(Classifier) - Static method in class weka.classifiers.Classifier: Creates a deep copy of the given classifier using serialization.
makeCopy(Kernel) - Static method in class weka.classifiers.functions.supportVector.Kernel: Creates a shallow copy of the kernel (if it implements Copyable) otherwise a deep copy using serialization.
makeCopy(Clusterer) - Static method in class weka.clusterers.AbstractClusterer: Creates a deep copy of the given clusterer using serialization.
makeCopy(Estimator) - Static method in class weka.estimators.Estimator: Creates a deep copy of the given estimator using serialization.
makeCopy(Filter) - Static method in class weka.filters.Filter: Creates a deep copy of the given filter using serialization.
makeCopy(Object) - Static method in class weka.gui.GenericArrayEditor: Makes a copy of an object using serialization.
makeCopy(Object) - Static method in class weka.gui.GenericObjectEditor: Makes a copy of an object using serialization.
makeData(DataGenerator, String[]) - Static method in class weka.datagenerators.DataGenerator: Calls the data generator.
MakeDecList - Class in weka.classifiers.rules.part: Class for handling a decision list.
MakeDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.MakeDecList: Constructor for unpruned dec list.
MakeDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.MakeDecList: Constructor for dec list pruned using C4.5 pruning.
MakeDecList(ModelSelection, int, int, int) - Constructor for class weka.classifiers.rules.part.MakeDecList: Constructor for dec list pruned using hold-out pruning.
MakeDensityBasedClusterer - Class in weka.clusterers: Class for wrapping a Clusterer to make it return a distribution and density.
MakeDensityBasedClusterer() - Constructor for class weka.clusterers.MakeDensityBasedClusterer: Default constructor.
MakeDensityBasedClusterer(Clusterer) - Constructor for class weka.clusterers.MakeDensityBasedClusterer: Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.
makeDistribution(double, int) - Static method in class weka.classifiers.evaluation.NominalPrediction: Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0.
MakeIndicator - Class in weka.filters.unsupervised.attribute: A filter that creates a new dataset with a boolean attribute replacing a nominal attribute.
MakeIndicator() - Constructor for class weka.filters.unsupervised.attribute.MakeIndicator: Constructor
makeUniformDistribution(int) - Static method in class weka.classifiers.evaluation.NominalPrediction: Creates a uniform probability distribution -- where each of the possible classes is assigned equal probability.
makeVaryNode(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.net.ADNode: create sub tree
makeWeighted(CostMatrix) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Makes a copy of this ConfusionMatrix after applying the supplied CostMatrix to the cells.
ManhattanDataObject - Class in weka.clusterers.forOPTICSAndDBScan.DataObjects: ManhattanDataObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 19, 2004
Time: 5:50:22 PM
$ Revision 1.4 $
ManhattanDataObject(Instance, String, Database) - Constructor for class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Constructs a new DataObject.
ManhattanDistance - Class in weka.core: Implements the Manhattan distance (or Taxicab geometry).
ManhattanDistance() - Constructor for class weka.core.ManhattanDistance: Constructs an Manhattan Distance object, Instances must be still set.
ManhattanDistance(Instances) - Constructor for class weka.core.ManhattanDistance: Constructs an Manhattan Distance object and automatically initializes the ranges.
manualThresholdValueTipText() - Method in class weka.classifiers.meta.ThresholdSelector
map(String, String) - Method in class weka.core.matrix.DoubleVector: Applies a method to the vector
mapClasses(int, int, int[][], int[], double[], double[], int) - Static method in class weka.clusterers.ClusterEvaluation: Finds the minimum error mapping of classes to clusters.
MappingInfo - Class in weka.core.pmml: Class that maintains the mapping between incoming data set structure and that of the mining schema.
MappingInfo(Instances, MiningSchema, Logger) - Constructor for class weka.core.pmml.MappingInfo
mapToMiningSchema(Instances) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Map mining schema to incoming instances.
margin() - Method in class weka.classifiers.evaluation.NominalPrediction: Calculates the prediction margin.
MarginCalculator - Class in weka.classifiers.bayes.net
MarginCalculator() - Constructor for class weka.classifiers.bayes.net.MarginCalculator
MarginCalculator.JunctionTreeNode - Class in weka.classifiers.bayes.net
MarginCalculator.JunctionTreeSeparator - Class in weka.classifiers.bayes.net
MarginCurve - Class in weka.classifiers.evaluation: Generates points illustrating the prediction margin.
MarginCurve() - Constructor for class weka.classifiers.evaluation.MarginCurve
markovBlanketClassifierTipText() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
markovBlanketClassifierTipText() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
maskKeyword(String) - Method in class weka.experiment.DatabaseUtils: If the given string is a keyword, then the mask character will be appended and returned.
Matchable - Interface in weka.core: Interface to something that can be matched with tree matching algorithms.
matchMissingValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
MathematicalExpression - Class in weka.core: Class for evaluating a string adhering the following grammar:
MathematicalExpression() - Constructor for class weka.core.MathematicalExpression
MathExpression - Class in weka.filters.unsupervised.attribute: Modify numeric attributes according to a given expression
Valid options are:
MathExpression() - Constructor for class weka.filters.unsupervised.attribute.MathExpression: Constructor
Maths - Class in weka.core.matrix: Utility class.
Maths() - Constructor for class weka.core.matrix.Maths
matrix() - Method in class weka.classifiers.trees.j48.Distribution: Returns matrix with distribution of class values.
Matrix - Class in weka.core: Deprecated.
Use weka.core.matrix.Matrix instead - only for backwards compatibility.
Matrix(int, int) - Constructor for class weka.core.Matrix: Deprecated.

Constructs a matrix and initializes it with default values.
Matrix(double[][]) - Constructor for class weka.core.Matrix: Deprecated.

Constructs a matrix using a given array.
Matrix(Reader) - Constructor for class weka.core.Matrix: Deprecated.

Reads a matrix from a reader.
Matrix - Class in weka.core.matrix: Jama = Java Matrix class.
Matrix(int, int) - Constructor for class weka.core.matrix.Matrix: Construct an m-by-n matrix of zeros.
Matrix(int, int, double) - Constructor for class weka.core.matrix.Matrix: Construct an m-by-n constant matrix.
Matrix(double[][]) - Constructor for class weka.core.matrix.Matrix: Construct a matrix from a 2-D array.
Matrix(double[][], int, int) - Constructor for class weka.core.matrix.Matrix: Construct a matrix quickly without checking arguments.
Matrix(double[], int) - Constructor for class weka.core.matrix.Matrix: Construct a matrix from a one-dimensional packed array
Matrix(Reader) - Constructor for class weka.core.matrix.Matrix: Reads a matrix from a reader.
MATRIX_ON_DEMAND - Static variable in class weka.attributeSelection.CostSensitiveASEvaluation: load cost matrix on demand
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.CostSensitiveClassifier: load cost matrix on demand
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.MetaCost: load cost matrix on demand
MATRIX_SUPPLIED - Static variable in class weka.attributeSelection.CostSensitiveASEvaluation: use explicit cost matrix
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.CostSensitiveClassifier: use explicit cost matrix
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.MetaCost: use explicit matrix
MatrixPanel - Class in weka.gui.visualize: This panel displays a plot matrix of the user selected attributes of a given data set.
MatrixPanel() - Constructor for class weka.gui.visualize.MatrixPanel: Constructor
max() - Method in class weka.core.matrix.DoubleVector: Returns the maximum value of all elements
MAX - Static variable in class weka.core.neighboursearch.KDTree: The index of MAX value in attributes' range array.
MAX - Static variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Index of max value in an array of attributes' range.
max - Variable in class weka.experiment.Stats: The maximum value seen, or Double.NaN if no values seen
MAX_DIGITS - Static variable in class weka.core.converters.SVMLightSaver: the number of digits after the decimal point.
MAX_ROWS - Static variable in class weka.gui.sql.QueryPanel: the name for the max rows in the history.
MAX_RULE - Static variable in class weka.classifiers.meta.Vote: combination rule: Maximum Probability
MAX_SHAPES - Static variable in class weka.gui.visualize.Plot2D
MAX_SLEEP_TIME - Static variable in class weka.core.Memory
maxAbs() - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the maximum absolute value of all elements
maxAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the maximum absolute value of some elements of a column, that is, the elements of A[i0:i1][j].
maxBag() - Method in class weka.classifiers.trees.j48.Distribution: Returns index of bag containing maximum number of instances.
maxBoostingIterationsTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
maxCardinalityTipText() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
maxCardinalityTipText() - Method in class weka.filters.unsupervised.attribute.RELAGGS: Returns the tip text for this property
maxChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
maxClass() - Method in class weka.classifiers.trees.j48.Distribution: Returns class with highest frequency over all bags.
maxClass(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns class with highest frequency for given bag.
maxClassForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
maxCountTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns the tip text for this property
maxDefaultTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
maxDepthTipText() - Method in class weka.classifiers.trees.RandomForest: Returns the tip text for this property
maxDepthTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property
maxDepthTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
maxGenerationsTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
maxGridExtensionsTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
maxGroupTipText() - Method in class weka.classifiers.meta.RotationForest: Returns the tip text for this property
maxImpurity() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the impurity of this split
maxImpurity() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Returns the impurity of this split
maxImpurity() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the impurity of this split
maximumAttributeNamesTipText() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns the tip text for this property
maximumAttributeNamesTipText() - Method in class weka.attributeSelection.PrincipalComponents: Returns the tip text for this property
maximumAttributeNamesTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Returns the tip text for this property.
maximumAttributesTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Returns the tip text for this property.
maximumVariancePercentageAllowedTipText() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Returns the tip text for this property
maxIndex(double[]) - Static method in class weka.core.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class weka.core.Utils: Returns index of maximum element in a given array of integers.
maxInstancesInLeafTipText() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the tip text for this property.
maxInstInLeafTipText() - Method in class weka.core.neighboursearch.KDTree: Tip text for this property.
maxInstNumTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
maxInstNumTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns the tip text for this property
maxIterations - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Maximum number of iterations
maxIterationsTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
maxIterationsTipText() - Method in class weka.classifiers.mi.MIBoost: Returns the tip text for this property
maxIterationsTipText() - Method in class weka.classifiers.mi.MISVM: Returns the tip text for this property
maxIterationsTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
maxIterationsTipText() - Method in class weka.clusterers.sIB: Returns the tip text for this property.
maxIterationsTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
maxIterationsTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
maxIterationsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
maxItsTipText() - Method in class weka.classifiers.functions.Logistic: Returns the tip text for this property
maxItsTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
maxKMeansForChildrenTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
maxKMeansTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
maxKTipText() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the tip text for this property
maxNrOfParentsTipText() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm
maxNumberOfItemsTipText() - Method in class weka.associations.FPGrowth: Tip text for this property suitable for displaying in the GUI.
maxNumClustersTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
maxParentSetSize(int) - Method in class weka.classifiers.bayes.net.ParentSet: reserve memory for parent set
maxRadiusTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
maxRangeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Returns the tip text for this property
maxRelativeLeafRadiusTipText() - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Returns the tip text for this property.
maxRuleSizeTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the tip text for this property
maxSubsequenceLengthTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the tip text for this property
maxThresholdTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
MAYBE_SUPPORT - Static variable in class weka.gui.GenericObjectEditor.GOETreeNode: color for "maybe support".
MDD - Class in weka.classifiers.mi: Modified Diverse Density algorithm, with collective assumption.

More information about DD:

Oded Maron (1998).
MDD() - Constructor for class weka.classifiers.mi.MDD
MDL - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
mean(double[]) - Static method in class weka.core.Utils: Computes the mean for an array of doubles.
mean - Variable in class weka.experiment.Stats: The mean of values at the last calculateDerived() call
meanAbsoluteError() - Method in class weka.classifiers.Evaluation: Returns the mean absolute error.
meanOrMode(int) - Method in class weka.core.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(Attribute) - Method in class weka.core.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanPriorAbsoluteError() - Method in class weka.classifiers.Evaluation: Returns the mean absolute error of the prior.
meanSquaredTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property.
meanStddevTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns the tip text for this property
measureAICScore() - Method in class weka.classifiers.bayes.BayesNet
measureAttributesUsed() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
measureBayesScore() - Method in class weka.classifiers.bayes.BayesNet
measureBDeuScore() - Method in class weka.classifiers.bayes.BayesNet
measureDivergence() - Method in class weka.classifiers.bayes.BayesNet
measureEntropyScore() - Method in class weka.classifiers.bayes.BayesNet
measureExamplesCounted() - Method in class weka.classifiers.trees.LADTree: Returns the number of examples "counted".
measureExamplesProcessed() - Method in class weka.classifiers.trees.ADTree: Returns the number of examples "counted".
measureExtraArcs() - Method in class weka.classifiers.bayes.BayesNet
measureMaxDepth() - Method in class weka.core.neighboursearch.BallTree: Returns the depth of the tree.
measureMaxDepth() - Method in class weka.core.neighboursearch.CoverTree: Returns the depth of the tree.
measureMaxDepth() - Method in class weka.core.neighboursearch.KDTree: Returns the depth of the tree.
measureMDLScore() - Method in class weka.classifiers.bayes.BayesNet
measureMissingArcs() - Method in class weka.classifiers.bayes.BayesNet
measureNodesExpanded() - Method in class weka.classifiers.trees.ADTree: Returns the number of nodes expanded.
measureNodesExpanded() - Method in class weka.classifiers.trees.LADTree: Returns the number of nodes expanded.
measureNumAttributesSelected() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Additional measure --- number of attributes selected
measureNumIterations() - Method in class weka.classifiers.meta.AdditiveRegression: return the number of iterations (base classifiers) completed
measureNumLeaves() - Method in class weka.classifiers.trees.ADTree: Calls measure function for leaf size - the number of prediction nodes.
measureNumLeaves() - Method in class weka.classifiers.trees.FT: Returns the number of leaves in the tree
measureNumLeaves() - Method in class weka.classifiers.trees.J48: Returns the number of leaves
measureNumLeaves() - Method in class weka.classifiers.trees.J48graft: Returns the number of leaves
measureNumLeaves() - Method in class weka.classifiers.trees.LADTree: Calls measure function for leaf size.
measureNumLeaves() - Method in class weka.classifiers.trees.LMT: Returns the number of leaves in the tree
measureNumLeaves() - Method in class weka.classifiers.trees.NBTree: Returns the number of leaves
measureNumLeaves() - Method in class weka.core.neighboursearch.BallTree: Returns the number of leaves.
measureNumLeaves() - Method in class weka.core.neighboursearch.CoverTree: Returns the number of leaves.
measureNumLeaves() - Method in class weka.core.neighboursearch.KDTree: Returns the number of leaves.
measureNumPredictionLeaves() - Method in class weka.classifiers.trees.ADTree: Calls measure function for prediction leaf size - the number of prediction nodes without children.
measureNumPredictionLeaves() - Method in class weka.classifiers.trees.LADTree: Calls measure function for leaf size.
measureNumRules() - Method in class weka.classifiers.rules.DecisionTable: Returns the number of rules
measureNumRules() - Method in class weka.classifiers.rules.PART: Return the number of rules.
measureNumRules() - Method in class weka.classifiers.trees.J48: Returns the number of rules (same as number of leaves)
measureNumRules() - Method in class weka.classifiers.trees.J48graft: Returns the number of rules (same as number of leaves)
measureNumRules() - Method in class weka.classifiers.trees.m5.M5Base: return the number of rules
measureNumRules() - Method in class weka.classifiers.trees.NBTree: Returns the number of rules (same as number of leaves)
measureOutOfBagError() - Method in class weka.classifiers.meta.Bagging: Gets the out of bag error that was calculated as the classifier was built.
measureOutOfBagError() - Method in class weka.classifiers.trees.RandomForest: Gets the out of bag error that was calculated as the classifier was built.
measurePercentAttsUsedByDT() - Method in class weka.classifiers.rules.DTNB: Returns the number of rules
measurePerformanceTipText() - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns the tip text for this property.
measureReversedArcs() - Method in class weka.classifiers.bayes.BayesNet
measureSelectionTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Additional measure --- time taken (milliseconds) to select the attributes
measureTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Additional measure --- time taken (milliseconds) to select attributes and build the classifier
measureTipText() - Method in class weka.classifiers.meta.ThresholdSelector: Tooltip for this property.
measureTreeSize() - Method in class weka.classifiers.trees.ADTree: Calls measure function for tree size - the total number of nodes.
measureTreeSize() - Method in class weka.classifiers.trees.BFTree: Return number of tree size.
measureTreeSize() - Method in class weka.classifiers.trees.FT: Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.J48: Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.J48graft: Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.LADTree: Calls measure function for tree size.
measureTreeSize() - Method in class weka.classifiers.trees.LMT: Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.NBTree: Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.SimpleCart: Return number of tree size.
measureTreeSize() - Method in class weka.core.neighboursearch.BallTree: Returns the size of the tree.
measureTreeSize() - Method in class weka.core.neighboursearch.CoverTree: Returns the size of the tree.
measureTreeSize() - Method in class weka.core.neighboursearch.KDTree: Returns the size of the tree.
MEDIAN_RULE - Static variable in class weka.classifiers.meta.Vote: combination rule: Median Probability (only numeric class)
MedianDistanceFromArbitraryPoint - Class in weka.core.neighboursearch.balltrees: Class that splits a BallNode of a ball tree using Uhlmann's described method.

For information see:

Jeffrey K.
MedianDistanceFromArbitraryPoint() - Constructor for class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Constructor.
MedianDistanceFromArbitraryPoint(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Constructor.
MedianOfWidestDimension - Class in weka.core.neighboursearch.balltrees: Class that splits a BallNode of a ball tree based on the median value of the widest dimension of the points in the ball.
MedianOfWidestDimension() - Constructor for class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Constructor.
MedianOfWidestDimension(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Constructor.
MedianOfWidestDimension - Class in weka.core.neighboursearch.kdtrees: The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.

For more information see also:

Jerome H.
MedianOfWidestDimension() - Constructor for class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension
Memory - Class in weka.core: A little helper class for Memory management.
Memory() - Constructor for class weka.core.Memory: initializes the memory management without GUI support
Memory(boolean) - Constructor for class weka.core.Memory: initializes the memory management
memoryIsLow() - Method in class weka.core.Memory: Checks to see if memory is running low.
MemoryUsagePanel - Class in weka.gui: A panel for displaying the memory usage.
MemoryUsagePanel() - Constructor for class weka.gui.MemoryUsagePanel: default constructor.
merge(Element, Element) - Static method in class weka.associations.gsp.Element: Merges two Elements into one.
merge(SimpleLinkedList, Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
merge(ADTree) - Method in class weka.classifiers.trees.ADTree: Merges two trees together.
merge(PredictionNode, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode: Merges this node with another.
merge(LADTree) - Method in class weka.classifiers.trees.LADTree: Merges two trees together.
mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.AprioriItemSet: Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet: Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.LabeledItemSet: Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeInstance(Instance) - Method in class weka.core.BinarySparseInstance: Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.Instance: Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.SparseInstance: Merges this instance with the given instance and returns the result.
mergeInstances(Instances, Instances) - Static method in class weka.core.Instances: Merges two sets of Instances together.
MergeTwoValues - Class in weka.filters.unsupervised.attribute: Merges two values of a nominal attribute into one value.
MergeTwoValues() - Constructor for class weka.filters.unsupervised.attribute.MergeTwoValues
Messages - Class in weka.associations.gsp: Messages.
Messages() - Constructor for class weka.associations.gsp.Messages
Messages - Class in weka.associations: Messages.
Messages() - Constructor for class weka.associations.Messages
Messages - Class in weka.gui.arffviewer: Messages.
Messages() - Constructor for class weka.gui.arffviewer.Messages
Messages - Class in weka.gui.beans: Messages.
Messages() - Constructor for class weka.gui.beans.Messages
Messages - Class in weka.gui.beans.xml: Messages.
Messages() - Constructor for class weka.gui.beans.xml.Messages
Messages - Class in weka.gui.boundaryvisualizer: Messages.
Messages() - Constructor for class weka.gui.boundaryvisualizer.Messages
Messages - Class in weka.gui.experiment: Messages.
Messages() - Constructor for class weka.gui.experiment.Messages
Messages - Class in weka.gui.explorer: Messages.
Messages() - Constructor for class weka.gui.explorer.Messages
Messages - Class in weka.gui.graphvisualizer: Messages.
Messages() - Constructor for class weka.gui.graphvisualizer.Messages
Messages - Class in weka.gui.hierarchyvisualizer: Messages.
Messages() - Constructor for class weka.gui.hierarchyvisualizer.Messages
Messages - Class in weka.gui: Messages.
Messages() - Constructor for class weka.gui.Messages
Messages - Class in weka.gui.sql.event: Messages.
Messages() - Constructor for class weka.gui.sql.event.Messages
Messages - Class in weka.gui.sql: Messages.
Messages() - Constructor for class weka.gui.sql.Messages
Messages - Class in weka.gui.streams: Messages.
Messages() - Constructor for class weka.gui.streams.Messages
Messages - Class in weka.gui.treevisualizer: Messages.
Messages() - Constructor for class weka.gui.treevisualizer.Messages
Messages - Class in weka.gui.visualize: Messages.
Messages() - Constructor for class weka.gui.visualize.Messages
MEstimate(double, double, double) - Method in class weka.classifiers.bayes.AODEsr: Returns the probability estimate, using m-estimate
mestWeightTipText() - Method in class weka.classifiers.bayes.AODEsr: Returns the tip text for this property
MetaBean - Class in weka.gui.beans: A meta bean that encapsulates several other regular beans, useful for grouping large KnowledgeFlows.
MetaBean() - Constructor for class weka.gui.beans.MetaBean
metaClassifierTipText() - Method in class weka.classifiers.meta.Stacking: Returns the tip text for this property
MetaCost - Class in weka.classifiers.meta: This metaclassifier makes its base classifier cost-sensitive using the method specified in

Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.
MetaCost() - Constructor for class weka.classifiers.meta.MetaCost
METHOD_1_AGAINST_1 - Static variable in class weka.classifiers.meta.MultiClassClassifier: 1-against-1
METHOD_1_AGAINST_ALL - Static variable in class weka.classifiers.meta.MultiClassClassifier: 1-against-all
METHOD_ERROR_EXHAUSTIVE - Static variable in class weka.classifiers.meta.MultiClassClassifier: exhaustive correction code
METHOD_ERROR_RANDOM - Static variable in class weka.classifiers.meta.MultiClassClassifier: random correction code
MethodHandler - Class in weka.core.xml: This class handles relationships between display names of properties (or classes) and Methods that are associated with them.
MethodHandler() - Constructor for class weka.core.xml.MethodHandler: initializes the handler
methodNameTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the tip text for this property
methodTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
methodTipText() - Method in class weka.classifiers.mi.MIWrapper: Returns the tip text for this property
metricString() - Method in class weka.associations.Apriori: Returns the metric string for the chosen metric type
metricString() - Method in interface weka.associations.CARuleMiner: Gets name of the scoring metric used for car mining
metricString() - Method in class weka.associations.PredictiveApriori: Returns the metric string for the chosen metric type.
metricTypeTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
metricTypeTipText() - Method in class weka.associations.FPGrowth: Tip text for this property suitable for displaying in the GUI.
MexicanHat - Class in weka.datagenerators.classifiers.regression: A data generator for the simple 'Mexian Hat' function:
y = sin|x| / |x|
In addition to this simple function, the amplitude can be changed and gaussian noise can be added.
MexicanHat() - Constructor for class weka.datagenerators.classifiers.regression.MexicanHat: initializes the generator
MIBoost - Class in weka.classifiers.mi: MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.

For more information about Adaboost, see:

Yoav Freund, Robert E.
MIBoost() - Constructor for class weka.classifiers.mi.MIBoost
MIDD - Class in weka.classifiers.mi: Re-implement the Diverse Density algorithm, changes the testing procedure.

Oded Maron (1998).
MIDD() - Constructor for class weka.classifiers.mi.MIDD
MiddleOutConstructor - Class in weka.core.neighboursearch.balltrees: The class that builds a BallTree middle out.

For more information see also:

Andrew W.
MiddleOutConstructor() - Constructor for class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Creates a new instance of MiddleOutConstructor.
midPoint(double, int) - Method in class weka.associations.PriorEstimation: calculates the mid point of an interval
MidPointOfWidestDimension - Class in weka.core.neighboursearch.kdtrees: The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.

For more information see also:

Andrew Moore (1991).
MidPointOfWidestDimension() - Constructor for class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension
midPoints() - Method in class weka.associations.PriorEstimation: split the interval [0,1] into a predefined number of intervals and calculates their mid points
MIEMDD - Class in weka.classifiers.mi: EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
MIEMDD() - Constructor for class weka.classifiers.mi.MIEMDD
MILR - Class in weka.classifiers.mi: Uses either standard or collective multi-instance assumption, but within linear regression.
MILR() - Constructor for class weka.classifiers.mi.MILR
MIN - Static variable in class weka.core.neighboursearch.KDTree: The index of MIN value in attributes' range array.
MIN - Static variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Index of min value in an array of attributes' range.
min - Variable in class weka.experiment.Stats: The minimum value seen, or Double.NaN if no values seen
MIN_RULE - Static variable in class weka.classifiers.meta.Vote: combination rule: Minimum Probability
minAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the minimum absolute value of some elements of a column, that is, the elements of A[i0:i1][j].
minBagDistance(Instance, Instance) - Method in class weka.classifiers.mi.MIOptimalBall: Calculate the distance from one data point to a bag
minBoxRelWidthTipText() - Method in class weka.core.neighboursearch.KDTree: Tip text for this property.
minBucketSizeTipText() - Method in class weka.classifiers.rules.OneR: Returns the tip text for this property
minChangeTipText() - Method in class weka.clusterers.sIB: Returns the tip text for this property.
minChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
minDataDLIfDeleted(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats: Compute the minimal data description length of the ruleset if the rule in the given position is deleted.
The min_data_DL_if_deleted = data_DL_if_deleted - potential
minDataDLIfExists(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats: Compute the minimal data description length of the ruleset if the rule in the given position is NOT deleted.
The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
minDefaultTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
mineCARs(Instances) - Method in class weka.associations.Apriori: Method that mines all class association rules with minimum support and with a minimum confidence.
mineCARs(Instances) - Method in interface weka.associations.CARuleMiner: Method for mining class association rules.
mineCARs(Instances) - Method in class weka.associations.PredictiveApriori: Method that mines the n best class association rules.
minGroupTipText() - Method in class weka.classifiers.meta.RotationForest: Returns the tip text for this property
minimax(Instances, int) - Static method in class weka.classifiers.mi.SimpleMI: Get the minimal and maximal value of a certain attribute in a certain data
minimaxTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
minimizeExpectedCostTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
minimizeWindows() - Method in class weka.gui.Main: minimizes all windows.
minimumBucketSizeTipText() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string for this option suitable for display in the gui as a tip text
minIndex(int[]) - Static method in class weka.core.Utils: Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class weka.core.Utils: Returns index of minimum element in a given array of doubles.
MiningFieldMetaInfo - Class in weka.core.pmml: Class encapsulating information about a MiningField.
MiningFieldMetaInfo(Element) - Constructor for class weka.core.pmml.MiningFieldMetaInfo: Constructs a new MiningFieldMetaInfo object.
MiningSchema - Class in weka.core.pmml: This class encapsulates the mining schema from a PMML xml file.
MiningSchema(Element, Instances, TransformationDictionary) - Constructor for class weka.core.pmml.MiningSchema: Constructor for MiningSchema.
minInstNumTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
minInstNumTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns the tip text for this property
minMetricTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
minMetricTipText() - Method in class weka.associations.FPGrowth: Returns the tip text for this property
MINND - Class in weka.classifiers.mi: Multiple-Instance Nearest Neighbour with Distribution learner.

It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
MINND() - Constructor for class weka.classifiers.mi.MINND
minNoTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
minNumClustersTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
minNumInstancesTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
minNumInstancesTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
minNumInstancesTipText() - Method in class weka.classifiers.trees.m5.M5Base: Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.BFTree: Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.J48graft: Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.SimpleCart: Returns the tip text for this property
minNumTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property
minNumTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
MINOR - Static variable in class weka.core.Version: the minor version
minPointsTipText() - Method in class weka.clusterers.DBSCAN: Returns the tip text for this property
minPointsTipText() - Method in class weka.clusterers.OPTICS: Returns the tip text for this property
minProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the smallest transformation probability
minRadiusTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
minRangeTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Returns the tip text for this property
minRuleSizeTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the tip text for this property
minsAndMaxs(Instances, double[][], int) - Method in class weka.classifiers.trees.j48.C45Split: Returns the minsAndMaxs of the index.th subset.
minStdDevTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
minStdDevTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
minStdDevTipText() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns the tip text for this property
minSupportTipText() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns the minimum support option tip text for the Weka GUI.
minTermFreqTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
minThresholdTipText() - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Returns the tip text for this property
MINUS - Static variable in interface weka.core.mathematicalexpression.sym
minus(double) - Method in class weka.core.matrix.DoubleVector: Subtracts a value
minus(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Subtracts another DoubleVector element by element
minus(Matrix) - Method in class weka.core.matrix.Matrix: C = A - B
MINUS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
minusEquals(double) - Method in class weka.core.matrix.DoubleVector: Subtracts a value in place
minusEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Subtracts another DoubleVector element by element in place
minusEquals(Matrix) - Method in class weka.core.matrix.Matrix: A = A - B
minVariancePropTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
MIOptimalBall - Class in weka.classifiers.mi: This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
MIOptimalBall() - Constructor for class weka.classifiers.mi.MIOptimalBall
MIPolyKernel - Class in weka.classifiers.mi.supportVector: The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
MIPolyKernel() - Constructor for class weka.classifiers.mi.supportVector.MIPolyKernel: default constructor - does nothing.
MIPolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.mi.supportVector.MIPolyKernel: Creates a new MIPolyKernel instance.
MIRBFKernel - Class in weka.classifiers.mi.supportVector: The RBF kernel.
MIRBFKernel() - Constructor for class weka.classifiers.mi.supportVector.MIRBFKernel: default constructor - does nothing.
MIRBFKernel(Instances, int, double) - Constructor for class weka.classifiers.mi.supportVector.MIRBFKernel: Constructor.
MISMO - Class in weka.classifiers.mi: Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
MISMO() - Constructor for class weka.classifiers.mi.MISMO
MISSING_SHAPE - Static variable in class weka.gui.visualize.Plot2D
MISSING_VALUE - Static variable in interface weka.classifiers.evaluation.Prediction: Constant representing a missing value.
missingArcs(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader: Count nr of arcs missing from other network compared to current network Note that an arc is not 'missing' if it is reversed.
missingCount - Variable in class weka.core.AttributeStats: The number of missing values
missingMergeTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns the tip text for this property
missingModeTipText() - Method in class weka.classifiers.lazy.KStar: Returns the tip text for this property
missingSeparateTipText() - Method in class weka.attributeSelection.CfsSubsetEval: Returns the tip text for this property
missingValue() - Static method in class weka.core.Instance: Returns the double that codes "missing".
missingValuesTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
missingValueTipText() - Method in class weka.core.converters.CSVLoader: Returns the tip text for this property.
MISVM - Class in weka.classifiers.mi: Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
MISVM() - Constructor for class weka.classifiers.mi.MISVM
MIWrapper - Class in weka.classifiers.mi: A simple Wrapper method for applying standard propositional learners to multi-instance data.

For more information see:

E.
MIWrapper() - Constructor for class weka.classifiers.mi.MIWrapper
MixtureDistribution - Class in weka.classifiers.functions.pace: Abtract class for manipulating mixture distributions.
MixtureDistribution() - Constructor for class weka.classifiers.functions.pace.MixtureDistribution
MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClassifierPanel: The filename extension that should be used for model files
MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClustererPanel: The filename extension that should be used for model files
MODEL_FT - Static variable in class weka.classifiers.trees.FT: model types
MODEL_FTInner - Static variable in class weka.classifiers.trees.FT
MODEL_FTLeaves - Static variable in class weka.classifiers.trees.FT
modelBuilt() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: flag to indicate whether the model was built yet
modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTtree: Returns the class probabilities for an instance according to the logistic model at the node.
modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the class probabilities for an instance according to the logistic model at the node.
modelErrors() - Method in class weka.classifiers.trees.lmt.LMTNode: Updates the numIncorrectModel field for all nodes.
modelErrors() - Method in class weka.classifiers.trees.SimpleCart: Updates the numIncorrectModel field for all nodes when subtree (to be pruned) is rooted.
modelFileTipText() - Method in class weka.classifiers.misc.SerializedClassifier: Returns the tip text for this property
ModelPerformanceChart - Class in weka.gui.beans: Bean that can be used for displaying threshold curves (e.g.
ModelPerformanceChart() - Constructor for class weka.gui.beans.ModelPerformanceChart
ModelPerformanceChartBeanInfo - Class in weka.gui.beans: Bean info class for the model performance chart
ModelPerformanceChartBeanInfo() - Constructor for class weka.gui.beans.ModelPerformanceChartBeanInfo
ModelSelection - Class in weka.classifiers.trees.j48: Abstract class for model selection criteria.
ModelSelection() - Constructor for class weka.classifiers.trees.j48.ModelSelection
modelsToString() - Method in class weka.classifiers.trees.ft.FTtree: Returns a string describing the logistic regression function at the node.
modelsToString() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns a string describing the logistic regression function at the node.
modelTypeTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
modifyHeaderTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns the tip text for this property
modifyHeaderTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
momentumTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
moralize(BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator: moralize DAG and calculate adjacency matrix representation for a Bayes Network, effecively converting the directed acyclic graph to an undirected graph.
mostExplainingColumn(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the index of the column that has the largest (squared) response, when each of columns pvt[ks:] is moved to become the ks-th column.
mouseClicked(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel: Invoked when a mouse button has been pressed and released on a component
mouseClicked(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Does nothing.
mouseDragged(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Performs intermediate updates to what the user wishes to do.
mouseEntered(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel: Invoked when the mouse enters a component.
mouseEntered(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Does nothing.
mouseExited(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel: Invoked when the mouse exits a component
mouseExited(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Does nothing.
mouseMoved(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Does nothing.
mousePressed(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel: Invoked when a mouse button has been pressed on a component
mousePressed(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Determines what action the user wants to perform.
mouseReleased(MouseEvent) - Method in class weka.gui.arffviewer.ArffPanel: Invoked when a mouse button has been released on a component.
mouseReleased(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Performs the final stages of what the user wants to perform.
MOVE_DOWN - Static variable in class weka.gui.JListHelper: moves items down
MOVE_UP - Static variable in class weka.gui.JListHelper: moves items up
moveBottom(JList) - Static method in class weka.gui.JListHelper: moves the selected items to the end
moveDown(JList) - Static method in class weka.gui.JListHelper: moves the selected item down by 1
MoveInstanceToBestCluster(Instance) - Method in class weka.clusterers.CLOPE: Move instance to best cluster
moveTop(JList) - Static method in class weka.gui.JListHelper: moves the selected items to the top
moveUp(JList) - Static method in class weka.gui.JListHelper: moves the selected items up by 1
MultiBoostAB - Class in weka.classifiers.meta: Class for boosting a classifier using the MultiBoosting method.

MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
MultiBoostAB() - Constructor for class weka.classifiers.meta.MultiBoostAB
MultiClassClassifier - Class in weka.classifiers.meta: A metaclassifier for handling multi-class datasets with 2-class classifiers.
MultiClassClassifier() - Constructor for class weka.classifiers.meta.MultiClassClassifier: Constructor.
MultiFilter - Class in weka.filters: Applies several filters successively.
MultiFilter() - Constructor for class weka.filters.MultiFilter
MultiInstanceCapabilitiesHandler - Interface in weka.core: Multi-Instance classifiers can specify an additional Capabilities object for the data in the relational attribute, since the format of multi-instance data is fixed to "bag/NOMINAL,data/RELATIONAL,class".
MultiInstanceToPropositional - Class in weka.filters.unsupervised.attribute: Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.
Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.
MultiInstanceToPropositional() - Constructor for class weka.filters.unsupervised.attribute.MultiInstanceToPropositional
MultilayerPerceptron - Class in weka.classifiers.functions: A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both.
MultilayerPerceptron() - Constructor for class weka.classifiers.functions.MultilayerPerceptron: The constructor.
MultiNomialBMAEstimator - Class in weka.classifiers.bayes.net.estimate: Multinomial BMA Estimator.
MultiNomialBMAEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator
multinomialWordTipText() - Method in class weka.classifiers.bayes.DMNBtext: Returns the tip text for this property
MultipleClassifiersCombiner - Class in weka.classifiers: Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
MultipleClassifiersCombiner() - Constructor for class weka.classifiers.MultipleClassifiersCombiner
multiply(Matrix) - Method in class weka.core.Matrix: Deprecated.

Returns the multiplication of two matrices
multiResultsetFull(int, int) - Method in class weka.experiment.PairedTTester: Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetFull(int, int) - Method in interface weka.experiment.Tester: Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetRanking(int) - Method in class weka.experiment.PairedTTester: returns a ranking of the resultsets
multiResultsetRanking(int) - Method in interface weka.experiment.Tester
multiResultsetSummary(int) - Method in class weka.experiment.PairedTTester: Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetSummary(int) - Method in interface weka.experiment.Tester: Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetWins(int, int[][]) - Method in class weka.experiment.PairedTTester: Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetWins(int, int[][]) - Method in interface weka.experiment.Tester: Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
MultiScheme - Class in weka.classifiers.meta: Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
MultiScheme() - Constructor for class weka.classifiers.meta.MultiScheme
mutationProbTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property

N

NaiveBayes - Class in weka.classifiers.bayes: Class for a Naive Bayes classifier using estimator classes.
NaiveBayes() - Constructor for class weka.classifiers.bayes.NaiveBayes
NaiveBayes - Class in weka.classifiers.bayes.net.search.fixed: The NaiveBayes class generates a fixed Bayes network structure with arrows from the class variable to each of the attribute variables.
NaiveBayes() - Constructor for class weka.classifiers.bayes.net.search.fixed.NaiveBayes
NaiveBayesMultinomial - Class in weka.classifiers.bayes: Class for building and using a multinomial Naive Bayes classifier.
NaiveBayesMultinomial() - Constructor for class weka.classifiers.bayes.NaiveBayesMultinomial
NaiveBayesMultinomialUpdateable - Class in weka.classifiers.bayes: Class for building and using a multinomial Naive Bayes classifier.
NaiveBayesMultinomialUpdateable() - Constructor for class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable
NaiveBayesSimple - Class in weka.classifiers.bayes: Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution.

For more information, see

Richard Duda, Peter Hart (1973).
NaiveBayesSimple() - Constructor for class weka.classifiers.bayes.NaiveBayesSimple
NaiveBayesUpdateable - Class in weka.classifiers.bayes: Class for a Naive Bayes classifier using estimator classes.
NaiveBayesUpdateable() - Constructor for class weka.classifiers.bayes.NaiveBayesUpdateable
name() - Method in class weka.core.Attribute: Returns the attribute's name.
name() - Method in class weka.core.Option: Returns the option's name.
NAME_CLASSFIRST - Static variable in class weka.experiment.xml.XMLExperiment: the name of the classFirst property
NAME_PROPERTYNODE_PARENTCLASS - Static variable in class weka.experiment.xml.XMLExperiment: PropertyNode member
NAME_PROPERTYNODE_PROPERTY - Static variable in class weka.experiment.xml.XMLExperiment: PropertyNode member
NAME_PROPERTYNODE_VALUE - Static variable in class weka.experiment.xml.XMLExperiment: PropertyNode member
NamedColor - Class in weka.gui.treevisualizer: This class contains a color name and the rgb values of that color
NamedColor(String, int, int, int) - Constructor for class weka.gui.treevisualizer.NamedColor
nameTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the tip text for this property
NBconditionalProb(Instance, int) - Method in class weka.classifiers.bayes.AODE: Calculates the probability of the specified class for the given test instance, using naive Bayes.
NBconditionalProb(Instance, int) - Method in class weka.classifiers.bayes.AODEsr: Calculates the probability of the specified class for the given test instance, using naive Bayes.
NBTree - Class in weka.classifiers.trees: Class for generating a decision tree with naive Bayes classifiers at the leaves.

For more information, see

Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
NBTree() - Constructor for class weka.classifiers.trees.NBTree
NBTreeClassifierTree - Class in weka.classifiers.trees.j48: Class for handling a naive bayes tree structure used for classification.
NBTreeClassifierTree(ModelSelection) - Constructor for class weka.classifiers.trees.j48.NBTreeClassifierTree
NBTreeModelSelection - Class in weka.classifiers.trees.j48: Class for selecting a NB tree split.
NBTreeModelSelection(int, Instances) - Constructor for class weka.classifiers.trees.j48.NBTreeModelSelection: Initializes the split selection method with the given parameters.
NBTreeNoSplit - Class in weka.classifiers.trees.j48: Class implementing a "no-split"-split (leaf node) for naive bayes trees.
NBTreeNoSplit() - Constructor for class weka.classifiers.trees.j48.NBTreeNoSplit
NBTreeSplit - Class in weka.classifiers.trees.j48: Class implementing a NBTree split on an attribute.
NBTreeSplit(int, int, double) - Constructor for class weka.classifiers.trees.j48.NBTreeSplit: Initializes the split model.
ND - Class in weka.classifiers.meta.nestedDichotomies: A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.

For more info, check

Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
ND() - Constructor for class weka.classifiers.meta.nestedDichotomies.ND: Constructor.
NDConditionalEstimator - Class in weka.estimators: Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate normal estimators for each discrete conditioning value).
NDConditionalEstimator(int, double) - Constructor for class weka.estimators.NDConditionalEstimator: Constructor
nearestNeighborsTipText() - Method in class weka.filters.supervised.instance.SMOTE: Returns the tip text for this property.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.BallTree: Returns the nearest instance in the current neighbourhood to the supplied instance.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.CoverTree: Returns the NN instance of a given target instance, from among the previously supplied training instances.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.KDTree: Returns the nearest neighbour of the supplied target instance.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.LinearNNSearch: Returns the nearest instance in the current neighbourhood to the supplied instance.
nearestNeighbour(Instance) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Returns the nearest instance in the current neighbourhood to the supplied instance.
NearestNeighbourSearch - Class in weka.core.neighboursearch: Abstract class for nearest neighbour search.
NearestNeighbourSearch() - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch: Constructor.
NearestNeighbourSearch(Instances) - Constructor for class weka.core.neighboursearch.NearestNeighbourSearch: Constructor.
nearestNeighbourSearchAlgorithmTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property.
nearestNeighbourSearchAlgorithmTipText() - Method in class weka.classifiers.lazy.LWL: Returns the tip text for this property.
needExponentialFormat(double) - Method in class weka.core.matrix.FlexibleDecimalFormat
needsUID(String) - Static method in class weka.core.SerializationHelper: checks whether a class needs to declare a serialVersionUID, i.e., it implements the java.io.Serializable interface but doesn't declare a serialVersionUID.
needsUID(Class) - Static method in class weka.core.SerializationHelper: checks whether a class needs to declare a serialVersionUID, i.e., it implements the java.io.Serializable interface but doesn't declare a serialVersionUID.
NEG - Static variable in class weka.associations.tertius.Literal
negationIncludedIn(LiteralSet) - Method in class weka.associations.tertius.LiteralSet: Test if the negation of this LiteralSet is included in another LiteralSet.
negationSatisfies(Instance) - Method in class weka.associations.tertius.AttributeValueLiteral
negationSatisfies(Instance) - Method in class weka.associations.tertius.Literal
negationTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
negative() - Method in class weka.associations.tertius.Literal
nestedEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Returns the optimal nested model estimate of a vector.
NeuralConnection - Class in weka.classifiers.functions.neural: Abstract unit in a NeuralNetwork.
NeuralConnection(String) - Constructor for class weka.classifiers.functions.neural.NeuralConnection: Constructs The unit with the basic connection information prepared for use.
NeuralMethod - Interface in weka.classifiers.functions.neural: This is an interface used to create classes that can be used by the neuralnode to perform all it's computations.
NeuralNetwork - Class in weka.classifiers.pmml.consumer: Class implementing import of PMML Neural Network model.
NeuralNetwork(Element, Instances, MiningSchema) - Constructor for class weka.classifiers.pmml.consumer.NeuralNetwork
NeuralNode - Class in weka.classifiers.functions.neural: This class is used to represent a node in the neuralnet.
NeuralNode(String, Random, NeuralMethod) - Constructor for class weka.classifiers.functions.neural.NeuralNode
NEW_BATCH - Static variable in class weka.gui.beans.IncrementalClassifierEvent
newClock() - Static method in class weka.core.Debug: returns a new instance of a clock
newDataFormat(DataSetEvent) - Method in class weka.gui.beans.ClassAssignerCustomizer
newDataFormat(DataSetEvent) - Method in interface weka.gui.beans.DataFormatListener: Recieve a DataSetEvent that encapsulates a new data format.
newDocument(String, String) - Method in class weka.core.xml.XMLDocument: creates a new Document with the given information.
newEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit: Computes entropy of distribution after splitting.
Newick - Static variable in interface weka.core.Drawable
newInstance(File, Class) - Static method in class weka.core.Jython: loads the module and returns a new instance of it as instance of the provided Java class template.
newInstance(File, Class, File[]) - Static method in class weka.core.Jython: loads the module and returns a new instance of it as instance of the provided Java class template.
newInterpreter() - Static method in class weka.core.Jython: initializes and returns a Python Interpreter
newLog(String, int, int) - Static method in class weka.core.Debug: returns a new Log instance
newNominalRule(Attribute, Instances, int[]) - Method in class weka.classifiers.rules.OneR: Create a rule branching on this nominal attribute.
newNumericRule(Attribute, Instances, int[]) - Method in class weka.classifiers.rules.OneR: Create a rule branching on this numeric attribute
newRandom() - Static method in class weka.core.Debug: returns a default debug random object, with no particular seed and debugging enabled.
newRandom(int) - Static method in class weka.core.Debug: returns a debug random object with the specified seed and debugging enabled.
newRule(Attribute, Instances) - Method in class weka.classifiers.rules.OneR: Create a rule branching on this attribute.
newTimestamp() - Static method in class weka.core.Debug: returns a default timestamp for the current date/time
next() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
next(int) - Method in interface weka.classifiers.IterativeClassifier: Performs one iteration.
next - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: next table entry (separate chaining)
next(int) - Method in class weka.classifiers.trees.ADTree: Performs one iteration.
next(int) - Method in class weka.classifiers.trees.LADTree
next() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue: Returns the element with the highest priority
next() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue: Returns the element with the lowest priority
next() - Method in class weka.core.Trie.TrieIterator: Returns the next element in the iteration.
next_token() - Method in class weka.core.mathematicalexpression.Scanner: Resumes scanning until the next regular expression is matched, the end of input is encountered or an I/O-Error occurs.
next_token() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Resumes scanning until the next regular expression is matched, the end of input is encountered or an I/O-Error occurs.
nextBoolean() - Method in class weka.core.Debug.Random: Returns the next pseudorandom, uniformly distributed boolean value from this random number generator's sequence.
nextBytes(byte[]) - Method in class weka.core.Debug.Random: Generates random bytes and places them into a user-supplied byte array.
nextDouble() - Method in class weka.core.Debug.Random: Returns the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence.
nextElement(Instances) - Method in class weka.core.converters.ConverterUtils.DataSource: returns the next element and sets the specified dataset, null if none available.
nextElement() - Method in class weka.core.FastVector.FastVectorEnumeration: Returns the next element.
nextElement() - Method in class weka.core.tokenizers.AlphabeticTokenizer: returns the next element
nextElement() - Method in class weka.core.tokenizers.NGramTokenizer: Returns N-grams and also (N-1)-grams and ....
nextElement() - Method in class weka.core.tokenizers.Tokenizer: Returns the next element of this enumeration if this enumeration object has at least one more element to provide.
nextElement() - Method in class weka.core.tokenizers.WordTokenizer: Returns the next element of this enumeration if this enumeration object has at least one more element to provide.
nextErlang(int) - Method in class weka.core.RandomVariates: Generate a value of a variate following standard Erlang distribution.
nextExponential() - Method in class weka.core.RandomVariates: Generate a value of a variate following standard exponential distribution using simple inverse method.
nextFloat() - Method in class weka.core.Debug.Random: Returns the next pseudorandom, uniformly distributed float value between 0.0 and 1.0 from this random number generator's sequence.
nextGamma(double) - Method in class weka.core.RandomVariates: Generate a value of a variate following standard Gamma distribution with shape parameter a.
nextGaussian() - Method in class weka.core.Debug.Random: Returns the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence.
nextInt() - Method in class weka.core.Debug.Random: Returns the next pseudorandom, uniformly distributed int value from this random number generator's sequence.
nextInt(int) - Method in class weka.core.Debug.Random: Returns a pseudorandom, uniformly distributed int value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence.
nextIteration() - Method in class weka.experiment.Experiment: Carries out the next iteration of the experiment.
nextIteration() - Method in class weka.experiment.RemoteExperiment: Overides the one in Experiment
nextLong() - Method in class weka.core.Debug.Random: Returns the next pseudorandom, uniformly distributed long value from this random number generator's sequence.
nextSplitAddedOrder() - Method in class weka.classifiers.trees.ADTree: Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.
NGramMaxSizeTipText() - Method in class weka.core.tokenizers.NGramTokenizer: Returns the tip text for this property.
NGramMinSizeTipText() - Method in class weka.core.tokenizers.NGramTokenizer: Returns the tip text for this property.
NGramTokenizer - Class in weka.core.tokenizers: Splits a string into an n-gram with min and max grams.
NGramTokenizer() - Constructor for class weka.core.tokenizers.NGramTokenizer
NNConditionalEstimator - Class in weka.estimators: Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).
NNConditionalEstimator() - Constructor for class weka.estimators.NNConditionalEstimator
NNge - Class in weka.classifiers.rules: Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
NNge() - Constructor for class weka.classifiers.rules.NNge
nnls(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Solves the nonnegative linear squares problem.
nnlse(PaceMatrix, PaceMatrix, PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Solves the nonnegative least squares problem with equality constraint.
nnlse1(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Solves the nonnegative least squares problem with equality constraint.
NNMMethod - Static variable in class weka.classifiers.functions.pace.MixtureDistribution: The nonnegative-measure-based method
NO_CLASS - Static variable in class weka.associations.CheckAssociator: a "dummy" class type
NO_CLASS - Static variable in class weka.core.TestInstances: can be used to avoid generating a class attribute
NO_COMMAND - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
NO_SUPPORT - Static variable in class weka.gui.GenericObjectEditor.GOETreeNode: color for "no support".
Node - Class in weka.gui.treevisualizer: This class records all the data about a particular node for displaying.
Node(String, String, int, int, Color, String) - Constructor for class weka.gui.treevisualizer.Node: This will setup all the values of the node except for its top and center.
NodePlace - Interface in weka.gui.treevisualizer: This is an interface for classes that wish to take a node structure and arrange them
nodeSplitterTipText() - Method in class weka.core.neighboursearch.KDTree: Returns the tip text for this property.
nodeToString() - Method in class weka.classifiers.trees.m5.RuleNode: Returns a description of this node (debugging purposes)
nodeType - Variable in class weka.gui.graphvisualizer.GraphNode: Type of node.
NOISE - Static variable in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
noisePercentTipText() - Method in class weka.datagenerators.classifiers.classification.LED24: Returns the tip text for this property
noiseRateTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Returns the tip text for this property
noiseRateTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
noiseRateTipText() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Returns the tip text for this property
noiseThresholdTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
noiseTipText() - Method in class weka.classifiers.functions.GaussianProcesses: Returns the tip text for this property
noiseVarianceTipText() - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Returns the tip text for this property
NOMINAL - Static variable in class weka.core.Attribute: Constant set for nominal attributes.
NominalAntd(Attribute) - Constructor for class weka.classifiers.rules.JRip.NominalAntd: Constructor
nominalAttributesTipText() - Method in class weka.core.converters.CSVLoader: Returns the tip text for this property.
nominalColsTipText() - Method in class weka.datagenerators.ClusterGenerator: Returns the tip text for this property
nominalCounts - Variable in class weka.core.AttributeStats: Counts of each nominal value
nominalIndicesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
nominalLabelsTipText() - Method in class weka.filters.unsupervised.attribute.Add: Returns the tip text for this property.
NominalPrediction - Class in weka.classifiers.evaluation: Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value.
NominalPrediction(double, double[]) - Constructor for class weka.classifiers.evaluation.NominalPrediction: Creates the NominalPrediction object with a default weight of 1.0.
NominalPrediction(double, double[], double) - Constructor for class weka.classifiers.evaluation.NominalPrediction: Creates the NominalPrediction object.
NominalToBinary - Class in weka.filters.supervised.attribute: Converts all nominal attributes into binary numeric attributes.
NominalToBinary() - Constructor for class weka.filters.supervised.attribute.NominalToBinary
NominalToBinary - Class in weka.filters.unsupervised.attribute: Converts all nominal attributes into binary numeric attributes.
NominalToBinary() - Constructor for class weka.filters.unsupervised.attribute.NominalToBinary: Constructor - initialises the filter
nominalToBinaryFilterTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
NominalToString - Class in weka.filters.unsupervised.attribute: Converts a nominal attribute (i.e.
NominalToString() - Constructor for class weka.filters.unsupervised.attribute.NominalToString
NON_NUMERIC - Static variable in class weka.filters.unsupervised.attribute.InterquartileRange: indicator for non-numeric attributes
NONE - Static variable in interface weka.core.converters.Loader: The retrieval modes
NONE - Static variable in interface weka.core.converters.Saver: The retrieval modes
NONE - Static variable in class weka.gui.visualize.VisualizePanelEvent: No longer used
NonSparseToSparse - Class in weka.filters.unsupervised.instance: An instance filter that converts all incoming instances into sparse format.
NonSparseToSparse() - Constructor for class weka.filters.unsupervised.instance.NonSparseToSparse
noPruningTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
noReplacementTipText() - Method in class weka.filters.supervised.instance.Resample: Returns the tip text for this property.
noReplacementTipText() - Method in class weka.filters.unsupervised.instance.Resample: Returns the tip text for this property
norm() - Method in class weka.core.AlgVector: Returns the norm of the vector
norm1() - Method in class weka.core.matrix.DoubleVector: Returns the L1-norm of the vector
norm1() - Method in class weka.core.matrix.Matrix: One norm
norm2() - Method in class weka.core.matrix.DoubleVector: Returns the L2-norm of the vector
norm2() - Method in class weka.core.matrix.Matrix: Two norm
norm2() - Method in class weka.core.matrix.SingularValueDecomposition: Two norm
NORM_BASED - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression: Methods for selecting the hyperparameter value
NORM_EXPECTED_COST_NAME - Static variable in class weka.classifiers.evaluation.CostCurve: attribute name: Normalized Expected Cost
NORMAL - Static variable in interface weka.gui.graphvisualizer.GraphConstants: NORMAL node - node actually contained in graphs description
normalDistribution - Static variable in class weka.core.matrix.Maths: Distribution type: noraml
NormalEstimator - Class in weka.estimators: Simple probability estimator that places a single normal distribution over the observed values.
NormalEstimator(double) - Constructor for class weka.estimators.NormalEstimator: Constructor that takes a precision argument.
normalInverse(double) - Static method in class weka.core.Statistics: Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).
NormalizableDistance - Class in weka.core: Represents the abstract ancestor for normalizable distance functions, like Euclidean or Manhattan distance.
NormalizableDistance() - Constructor for class weka.core.NormalizableDistance: Invalidates the distance function, Instances must be still set.
NormalizableDistance(Instances) - Constructor for class weka.core.NormalizableDistance: Initializes the distance function and automatically initializes the ranges.
normalize() - Method in class weka.classifiers.CostMatrix: Normalizes the matrix so that the diagonal contains zeros.
normalize() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Normalizes the function values with L1-norm.
normalize(double[]) - Static method in class weka.core.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class weka.core.Utils: Normalizes the doubles in the array using the given value.
Normalize - Class in weka.filters.unsupervised.attribute: Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
Normalize() - Constructor for class weka.filters.unsupervised.attribute.Normalize
Normalize - Class in weka.filters.unsupervised.instance: An instance filter that normalize instances considering only numeric attributes and ignoring class index.
Normalize() - Constructor for class weka.filters.unsupervised.instance.Normalize
normalizeAttributesTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
NormalizeData - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Choose whether to normalize data or not
normalizeDataTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
normalizeDimWidthsTipText() - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Returns the tip text for this property.
normalizedKernel(char[], char[]) - Method in class weka.classifiers.functions.supportVector.StringKernel: evaluates the normalized kernel between s and t.
normalizeDocLengthTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
NormalizedPolyKernel - Class in weka.classifiers.functions.supportVector: The normalized polynomial kernel.
K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
Valid options are:
NormalizedPolyKernel() - Constructor for class weka.classifiers.functions.supportVector.NormalizedPolyKernel: default constructor - does nothing
NormalizedPolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.NormalizedPolyKernel: Creates a new NormalizedPolyKernel instance.
normalizeNodeWidthTipText() - Method in class weka.core.neighboursearch.KDTree: Tip text for this property.
normalizeNumericClassTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
normalizeTipText() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns the tip text for this property
normalizeTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
normalizeTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
normalizeWordWeightsTipText() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns the tip text for this property
NormalMixture - Class in weka.classifiers.functions.pace: Class for manipulating normal mixture distributions.
NormalMixture() - Constructor for class weka.classifiers.functions.pace.NormalMixture: Contructs an empty NormalMixture
normalProbability(double) - Static method in class weka.core.Statistics: Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).
normBasedHyperParameter() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: This function computes the norm-based hyperparameters and stores them in the m_Hyperparameters.
NormContinuous - Class in weka.core.pmml: Class encapsulating a NormContinuous Expression.
NormContinuous(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.NormContinuous
NormDiscrete - Class in weka.core.pmml: Class encapsulating a NormDiscrete Expression.
NormDiscrete(Element, FieldMetaInfo.Optype, ArrayList<Attribute>) - Constructor for class weka.core.pmml.NormDiscrete: Constructor.
normF() - Method in class weka.core.matrix.Matrix: Frobenius norm
normInf() - Method in class weka.core.matrix.Matrix: Infinity norm
normTipText() - Method in class weka.filters.unsupervised.instance.Normalize: Returns the tip text for this property
normVector() - Method in class weka.core.AlgVector: Norms this vector to length 1.0
NORTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
NoSplit - Class in weka.classifiers.trees.j48: Class implementing a "no-split"-split.
NoSplit(Distribution) - Constructor for class weka.classifiers.trees.j48.NoSplit: Creates "no-split"-split for given distribution.
NoSupportForMissingValuesException - Exception in weka.core: Exception that is raised by an object that is unable to process data with missing values.
NoSupportForMissingValuesException() - Constructor for exception weka.core.NoSupportForMissingValuesException: Creates a new NoSupportForMissingValuesException with no message.
NoSupportForMissingValuesException(String) - Constructor for exception weka.core.NoSupportForMissingValuesException: Creates a new NoSupportForMissingValuesException.
NOT - Static variable in interface weka.core.mathematicalexpression.sym
NOT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
NOT_DRAWABLE - Static variable in interface weka.core.Drawable
notCoveredInstances() - Method in class weka.classifiers.trees.m5.Rule: Get the instances not covered by this rule
notifyCapabilitiesFilterListener(Capabilities) - Method in class weka.gui.explorer.Explorer: notifies all the listeners of a change
notifyListener() - Method in class weka.gui.arffviewer.ArffPanel: notfies all listener of the change
notifyListener(TableModelEvent) - Method in class weka.gui.arffviewer.ArffSortedTableModel: notfies all listener of the change of the model
notifyListener(TableModelEvent) - Method in class weka.gui.arffviewer.ArffTableModel: notfies all listener of the change of the model
notUnifyNormTipText() - Method in class weka.clusterers.sIB: Returns the tip text for this property.
nrOfGoodOperationsTipText() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
nrOfLookAheadStepsTipText() - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber
NullStemmer - Class in weka.core.stemmers: A dummy stemmer that performs no stemming at all.
NullStemmer() - Constructor for class weka.core.stemmers.NullStemmer
NUM_RAND_COLS - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
numAllConditions(Instances) - Static method in class weka.classifiers.rules.RuleStats: Compute the number of all possible conditions that could appear in a rule of a given data.
numAntdsTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
numArcsTipText() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Returns the tip text for this property
numArguments() - Method in class weka.core.Option: Returns the option's number of arguments.
numAttemptsOfGeneOptionTipText() - Method in class weka.classifiers.rules.NNge: Returns the tip text for this property
numAttributes() - Method in class weka.core.Instance: Returns the number of attributes.
numAttributes() - Method in class weka.core.Instances: Returns the number of attributes.
numAttributes() - Method in class weka.core.SparseInstance: Returns the number of attributes.
numAttributesTipText() - Method in class weka.datagenerators.classifiers.classification.BayesNet: Returns the tip text for this property
numAttributesTipText() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Returns the tip text for this property
numAttributesTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the tip text for this property
numAttributesTipText() - Method in class weka.datagenerators.clusterers.SubspaceCluster: Returns the tip text for this property
numAttributesTipText() - Method in class weka.datagenerators.ClusterGenerator: Returns the tip text for this property
numAttributesTipText() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Returns the tip text for this property.
numBags() - Method in class weka.classifiers.trees.j48.Distribution: Returns number of bags.
NUMBER - Static variable in interface weka.core.mathematicalexpression.sym
NUMBER - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
numberAttributesSelected() - Method in class weka.attributeSelection.AttributeSelection: Return the number of attributes selected from the most recent run of attribute selection
numberLiteralsTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
numberOfAttributesTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
numberOfClusters() - Method in class weka.clusterers.AbstractClusterer: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.CLOPE
numberOfClusters() - Method in interface weka.clusterers.Clusterer: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.Cobweb: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.DBSCAN: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.EM: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.FarthestFirst: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.HierarchicalClusterer
numberOfClusters() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.OPTICS: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.sIB: Get the number of clusters
numberOfClusters() - Method in class weka.clusterers.SimpleKMeans: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.SingleClustererEnhancer: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.XMeans: Returns the number of clusters.
NumberOfClustersRequestable - Interface in weka.clusterers: Interface to a clusterer that can generate a requested number of clusters
numberOfGroupsTipText() - Method in class weka.classifiers.meta.RotationForest: Returns the tip text for this property
numberOfLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode: Get the number of linear models in the tree
numBinsTipText() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns the tip text for this property
numBoostingIterationsTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
numBoostingIterationsTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
numBoostingIterationsTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
numCacheHits() - Method in class weka.classifiers.functions.supportVector.CachedKernel: Returns the number of cache hits on dot products.
numCacheHits() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns the number of dot product cache hits.
numCacheHits() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Returns the number of dot product cache hits.
numCacheHits() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the number of dot product cache hits.
numCentroidsTipText() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Returns the tip text for this property
numChildren() - Method in class weka.gui.HierarchyPropertyParser: The number of the children nodes.
numCitersTipText() - Method in class weka.classifiers.mi.CitationKNN: Returns the tip text for this property
numClassAttributeValues() - Method in class weka.classifiers.functions.SMO
numClassAttributeValues() - Method in class weka.classifiers.mi.MISMO: Returns the number of values of the class attribute.
numClasses() - Method in class weka.classifiers.trees.j48.Distribution: Returns number of classes.
numClasses() - Method in class weka.core.Instance: Returns the number of class labels.
numClasses() - Method in class weka.core.Instances: Returns the number of class labels.
numClassesTipText() - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Returns the tip text for this property
numClassesTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the tip text for this property
numClustersTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.FarthestFirst: Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.HierarchicalClusterer
numClustersTipText() - Method in class weka.clusterers.sIB: Returns the tip text for this property.
numClustersTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
numClustersTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
numColumns() - Method in class weka.classifiers.CostMatrix: Same as size
numColumns() - Method in class weka.core.Matrix: Deprecated.

Returns the number of columns in the matrix.
numComponentsTipText() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns the tip text for this property
numCorrect() - Method in class weka.classifiers.trees.j48.Distribution: Returns perClass(maxClass()).
numCorrect(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns perClassPerBag(index,maxClass(index)).
numCyclesTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
numDistinctValues(int) - Method in class weka.core.Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(Attribute) - Method in class weka.core.Instances: Returns the number of distinct values of a given attribute.
numElements() - Method in class weka.classifiers.functions.supportVector.SMOset: Returns the number of elements in the set.
numElements() - Method in class weka.classifiers.trees.RandomTree: Returns the number of elements in the partition.
numElements() - Method in class weka.core.AlgVector: Returns the number of elements in the vector.
NUMERIC - Static variable in class weka.core.Attribute: Constant set for numeric attributes.
NumericAntd(Attribute) - Constructor for class weka.classifiers.rules.JRip.NumericAntd: Constructor
NumericCleaner - Class in weka.filters.unsupervised.attribute: A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value (e.g., 0) and sets these values to a pre-defined default.
NumericCleaner() - Constructor for class weka.filters.unsupervised.attribute.NumericCleaner
NumericPrediction - Class in weka.classifiers.evaluation: Encapsulates an evaluatable numeric prediction: the predicted class value plus the actual class value.
NumericPrediction(double, double) - Constructor for class weka.classifiers.evaluation.NumericPrediction: Creates the NumericPrediction object with a default weight of 1.0.
NumericPrediction(double, double, double) - Constructor for class weka.classifiers.evaluation.NumericPrediction: Creates the NumericPrediction object.
numericStats - Variable in class weka.core.AttributeStats: Stats on numeric value distributions
numericTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
NumericToBinary - Class in weka.filters.unsupervised.attribute: Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
NumericToBinary() - Constructor for class weka.filters.unsupervised.attribute.NumericToBinary
NumericToNominal - Class in weka.filters.unsupervised.attribute: A filter for turning numeric attributes into nominal ones.
NumericToNominal() - Constructor for class weka.filters.unsupervised.attribute.NumericToNominal
NumericTransform - Class in weka.filters.unsupervised.attribute: Transforms numeric attributes using a given transformation method.
NumericTransform() - Constructor for class weka.filters.unsupervised.attribute.NumericTransform: Default constructor -- sets the default transform method to java.lang.Math.abs().
numEvals() - Method in class weka.classifiers.functions.supportVector.CachedKernel: Returns the number of time Eval has been called.
numEvals() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns the number of kernel evaluation performed.
numEvals() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Returns the number of kernel evaluation performed.
numEvals() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the number of kernel evaluation performed.
numExamplesTipText() - Method in class weka.datagenerators.ClassificationGenerator: Returns the tip text for this property
numExamplesTipText() - Method in class weka.datagenerators.RegressionGenerator: Returns the tip text for this property
numFalseNegatives(int) - Method in class weka.classifiers.Evaluation: Calculate number of false negatives with respect to a particular class.
numFalsePositives(int) - Method in class weka.classifiers.Evaluation: Calculate number of false positives with respect to a particular class.
numFeaturesTipText() - Method in class weka.classifiers.trees.RandomForest: Returns the tip text for this property
numFoldersMIOptionTipText() - Method in class weka.classifiers.rules.NNge: Returns the tip text for this property
NumFolds - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: NumFolds for CV based Hyperparameters selection
numFoldsPruningTipText() - Method in class weka.classifiers.trees.BFTree: Returns the tip text for this property
numFoldsPruningTipText() - Method in class weka.classifiers.trees.SimpleCart: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.CVParameterSelection: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.Dagging: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.Stacking: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
numIncorrect() - Method in class weka.classifiers.trees.j48.Distribution: Returns total-numCorrect().
numIncorrect(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns perBag(index)-numCorrect(index).
numInnerNodes() - Method in class weka.classifiers.trees.SimpleCart: Method to count the number of inner nodes in the tree.
numInstances() - Method in class weka.classifiers.Evaluation: Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
numInstances() - Method in class weka.core.Instances: Returns the number of instances in the dataset.
numInstances() - Method in class weka.core.neighboursearch.balltrees.BallNode: Returns the number of instances in the hyper-spherical region of this node.
numInstances() - Method in class weka.core.neighboursearch.kdtrees.KDTreeNode: Returns the number of Instances in the rectangular region defined by this node.
numIrrelevantTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.bayes.DMNBtext: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.meta.Decorate: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
numLeaves() - Method in class weka.classifiers.trees.BFTree: Compute number of leaf nodes.
numLeaves() - Method in class weka.classifiers.trees.ft.FTtree: Returns the number of leaves (normal count).
numLeaves() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns number of leaves in tree structure.
numLeaves() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the number of leaves (normal count).
numLeaves(int) - Method in class weka.classifiers.trees.m5.RuleNode: Sets the leaves' numbers
numLeaves() - Method in class weka.classifiers.trees.SimpleCart: Compute number of leaf nodes.
numLiterals() - Method in class weka.associations.tertius.LiteralSet: Give the number of literals in this set.
numLiterals() - Method in class weka.associations.tertius.Predicate
numLiterals() - Method in class weka.associations.tertius.Rule: Give the number of literals in this rule.
numNeighboursTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
numNeighboursTipText() - Method in class weka.classifiers.mi.MINND: Returns the tip text for this property
numNodes() - Method in class weka.classifiers.trees.BFTree: Compute size of the tree.
numNodes() - Method in class weka.classifiers.trees.ft.FTtree: Returns the number of nodes.
numNodes() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns number of nodes in tree structure.
numNodes() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the number of nodes.
numNodes() - Method in class weka.classifiers.trees.REPTree: Computes size of the tree.
numNodes() - Method in class weka.classifiers.trees.SimpleCart: Compute size of the tree.
numNumericTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the tip text for this property
numOfBoostingIterationsTipText() - Method in class weka.classifiers.trees.ADTree
numOfBoostingIterationsTipText() - Method in class weka.classifiers.trees.LADTree
numParameters() - Method in class weka.classifiers.functions.LinearRegression: Get the number of coefficients used in the model
numParameters() - Method in class weka.classifiers.functions.PaceRegression: Get the number of coefficients used in the model
numParameters() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Return the number of parameters (coefficients) in the linear model
numPendingOutput() - Method in class weka.filters.Filter: Returns the number of instances pending output
numPendingOutput() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns the number of instances pending output
numReferencesTipText() - Method in class weka.classifiers.mi.CitationKNN: Returns the tip text for this property
numRestartsTipText() - Method in class weka.clusterers.sIB: Returns the tip text for this property.
numRows() - Method in class weka.classifiers.CostMatrix: Same as size
numRows() - Method in class weka.core.Matrix: Deprecated.

Returns the number of rows in the matrix.
numRules() - Method in class weka.classifiers.rules.part.MakeDecList: Outputs the number of rules in the classifier.
numRulesTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
numRulesTipText() - Method in class weka.associations.PredictiveApriori: Returns the tip text for this property
numRulesToFindTipText() - Method in class weka.associations.FPGrowth: Tip text for this property suitable for displaying in the GUI.
numRunsTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
numSubCmtysTipText() - Method in class weka.classifiers.meta.MultiBoostAB: Returns the tip text for this property
numSubsets() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns the number of created subsets for the split.
numSubsetSizeCVFoldsTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the tip text for this property
numTestingNoisesTipText() - Method in class weka.classifiers.mi.MINND: Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.GreedyStepwise: Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
numTrainingNoisesTipText() - Method in class weka.classifiers.mi.MINND: Returns the tip text for this property
numTreesTipText() - Method in class weka.classifiers.trees.RandomForest: Returns the tip text for this property
numTrueNegatives(int) - Method in class weka.classifiers.Evaluation: Calculate the number of true negatives with respect to a particular class.
numTruePositives(int) - Method in class weka.classifiers.Evaluation: Calculate the number of true positives with respect to a particular class.
numUsedAttributesTipText() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the tip text for this property
numUsedAttributesTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the tip text for this property
numValues() - Method in class weka.core.Attribute: Returns the number of attribute values.
numValues() - Method in class weka.core.Instance: Returns the number of values present.
numValues() - Method in class weka.core.SparseInstance: Returns the number of values in the sparse vector.
numValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns the tip text for this property
numXValFoldsTipText() - Method in class weka.classifiers.meta.ThresholdSelector
nuTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property

O

Obfuscate - Class in weka.filters.unsupervised.attribute: A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
Obfuscate() - Constructor for class weka.filters.unsupervised.attribute.Obfuscate
ObjectCellRenderer() - Constructor for class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
observedComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their observed number of counter-instances.
obtainVotes(Instance) - Method in class weka.classifiers.functions.SMO: Returns an array of votes for the given instance.
OFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
OFF - Static variable in class weka.core.Debug: the log level Off - i.e., no logging
oldEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit: Computes entropy of distribution before splitting.
omegaTipText() - Method in class weka.classifiers.functions.supportVector.Puk: Returns the tip text for this property
ON - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants: Some usefull constants
onDemandDirectoryTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation
onDemandDirectoryTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
onDemandDirectoryTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
onDemandDirectoryTipText() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns the tip text for this property
oneElementsToSequences(FastVector) - Static method in class weka.associations.gsp.Sequence: Converts a set of 1-Elements into a set of 1-Sequences.
OneR - Class in weka.classifiers.rules: Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
OneR() - Constructor for class weka.classifiers.rules.OneR
OneRAttributeEval - Class in weka.attributeSelection: OneRAttributeEval :

Evaluates the worth of an attribute by using the OneR classifier.

Valid options are:
OneRAttributeEval() - Constructor for class weka.attributeSelection.OneRAttributeEval: Constructor
onUnit(Graphics, int, int, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to determine if the point at x,y is on the unit.
openFrame(String) - Method in class weka.gui.ResultHistoryPanel: Opens the named result in a separate frame.
openURL(String) - Static method in class weka.gui.BrowserHelper: opens the URL in a browser.
openURL(Component, String) - Static method in class weka.gui.BrowserHelper: opens the URL in a browser.
openURL(Component, String, boolean) - Static method in class weka.gui.BrowserHelper: opens the URL in a browser.
OPTICS - Class in weka.clusterers: Basic implementation of OPTICS clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported.
OPTICS() - Constructor for class weka.clusterers.OPTICS
OPTICS_Visualizer - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI: Start the OPTICS Visualizer from command-line:
java weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer [file.ser]
OPTICS_Visualizer(SERObject, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer
optimisticComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their optimistic estimate.
optimisticThenObservedComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their optimistic estimate and then their observed number of counter-instances.
Optimization - Class in weka.core: Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions.
Optimization() - Constructor for class weka.core.Optimization
optimizationsTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
optimize() - Method in class weka.classifiers.functions.supportVector.RegSMO: finds alpha and alpha* parameters that optimize the SVM target function
OPTIMIZE_0 - Static variable in class weka.classifiers.meta.ThresholdSelector: first class value
OPTIMIZE_1 - Static variable in class weka.classifiers.meta.ThresholdSelector: second class value
OPTIMIZE_LFREQ - Static variable in class weka.classifiers.meta.ThresholdSelector: least frequent class value
OPTIMIZE_MFREQ - Static variable in class weka.classifiers.meta.ThresholdSelector: most frequent class value
OPTIMIZE_POS_NAME - Static variable in class weka.classifiers.meta.ThresholdSelector: class value name, either 'yes' or 'pos(itive)'
Option - Class in weka.core: Class to store information about an option.
Option(String, String, int, String) - Constructor for class weka.core.Option: Creates new option with the given parameters.
OptionHandler - Interface in weka.core: Interface to something that understands options.
OptionHandlerJavadoc - Class in weka.core: Generates Javadoc comments from the OptionHandler's options.
OptionHandlerJavadoc() - Constructor for class weka.core.OptionHandlerJavadoc: default constructor
OPTIONS_ENDTAG - Static variable in class weka.core.OptionHandlerJavadoc: the end comment tag for inserting the generated Javadoc
OPTIONS_STARTTAG - Static variable in class weka.core.OptionHandlerJavadoc: the start comment tag for inserting the generated Javadoc
or(Capabilities) - Method in class weka.core.Capabilities: performs an OR conjunction with the capabilities of the given Capabilities object and updates itself
OR - Static variable in interface weka.core.mathematicalexpression.sym
OR - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
orderAdded - Variable in class weka.classifiers.trees.adtree.Splitter: The number this node was in the order of nodes added to the tree
ORDERED - Static variable in class weka.datagenerators.clusterers.BIRCHCluster: Constant set for input order (option O)
ordering() - Method in class weka.core.Attribute: Returns the ordering of the attribute.
ORDERING_MODULO - Static variable in class weka.core.Attribute: Constant set for modulo-ordered attributes.
ORDERING_ORDERED - Static variable in class weka.core.Attribute: Constant set for ordered attributes.
ORDERING_SYMBOLIC - Static variable in class weka.core.Attribute: Constant set for symbolic attributes.
OrdinalClassClassifier - Class in weka.classifiers.meta: Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.

For more information see:

Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification.
OrdinalClassClassifier() - Constructor for class weka.classifiers.meta.OrdinalClassClassifier: Default constructor.
originalValue(double) - Method in class weka.filters.supervised.attribute.ClassOrder: Return the original internal class value given the randomized class value, i.e.
OUT_OF_MEMORY_THRESHOLD - Static variable in class weka.core.Memory
outlierFactorTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns the tip text for this property
OUTPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is an output unit.
output() - Method in class weka.filters.Filter: Output an instance after filtering and remove from the output queue.
output() - Method in class weka.filters.unsupervised.attribute.RemoveType: Output an instance after filtering and remove from the output queue.
outputCenterFileTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
outputClassificationTipText() - Method in class weka.filters.supervised.attribute.AddClassification: Returns the tip text for this property.
outputDistributionTipText() - Method in class weka.filters.supervised.attribute.AddClassification: Returns the tip text for this property.
outputErrorFlagTipText() - Method in class weka.filters.supervised.attribute.AddClassification: Returns the tip text for this property.
outputFileName() - Method in class weka.experiment.CSVResultListener: Get the value of OutputFileName.
outputFilenameTipText() - Method in class weka.core.converters.TextDirectoryLoader: the tip text for this property
outputFileTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.CSVResultListener: Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
outputFormat() - Method in class weka.gui.streams.InstanceJoiner: Gets the format of the output instances.
outputFormat() - Method in class weka.gui.streams.InstanceLoader
outputFormat() - Method in interface weka.gui.streams.InstanceProducer
OutputFormatDialog - Class in weka.gui.experiment: A dialog for setting various output format parameters.
OutputFormatDialog(Frame) - Constructor for class weka.gui.experiment.OutputFormatDialog: initializes the dialog with the given parent frame.
outputItemSetsTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
OutputLogger - Class in weka.core.logging: A logger that logs all output on stdout and stderr to a file.
OutputLogger() - Constructor for class weka.core.logging.OutputLogger
OutputLogger.OutputPrintStream - Class in weka.core.logging: A print stream class to capture all data from stdout and stderr.
outputOffsetMultiplierTipText() - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Returns the tip text for this property
outputPeek() - Method in class weka.filters.Filter: Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.filters.unsupervised.attribute.RemoveType: Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.gui.streams.InstanceJoiner: Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.gui.streams.InstanceLoader
outputPeek() - Method in interface weka.gui.streams.InstanceProducer
outputPerClassInfoRetrievalStatsTipText() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Return a tip text string for this property
OutputPrintStream(OutputLogger, PrintStream) - Constructor for class weka.core.logging.OutputLogger.OutputPrintStream: Default constructor.
outputs(Vector) - Static method in class weka.gui.beans.BeanConnection: Returns a vector of BeanInstances that can be considered as outputs (or the right-hand side of a sub-flow)
outputsContains(BeanInstance) - Method in class weka.gui.beans.MetaBean
outputTipText() - Method in class weka.datagenerators.DataGenerator: Returns the tip text for this property
outputTypeSet(int) - Method in class weka.core.Debug.DBO: Return true if the outputtype is set
outputValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit: This function calculates what the output value should be.
outputValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to get the output value of this unit.
outputValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod: This function calculates what the output value should be.
outputValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to get the output value of this unit.
outputValue(NeuralNode) - Method in class weka.classifiers.functions.neural.SigmoidUnit: This function calculates what the output value should be.
outputWordCountsTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
OutputZipper - Class in weka.experiment: OutputZipper writes output to either gzipped files or to a multi entry zip file.
OutputZipper(File) - Constructor for class weka.experiment.OutputZipper: Constructor.
OVAL - Static variable in class weka.gui.visualize.VisualizePanelEvent
overFrequencyThreshold(double) - Method in class weka.associations.tertius.LiteralSet: Test if this LiteralSet has more counter-instances than the threshold.
overFrequencyThreshold(double) - Method in class weka.associations.tertius.Rule: Test if this rule is over the frequency threshold.

P

p(String) - Static method in class weka.core.Debug.DBO: prints out text.
p() - Method in class weka.core.neighboursearch.CoverTree.CoverTreeNode: Returns the instance represented by the node.
p1evl(double, double[], int) - Static method in class weka.core.Statistics: Evaluates the given polynomial of degree N at x.
pace2(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the pace2 estimate of a vector.
pace4(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the pace4 estimate of a vector.
pace6(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the pace6 estimate of a single value.
pace6(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the pace6 estimate of a vector.
PaceMatrix - Class in weka.classifiers.functions.pace: Class for matrix manipulation used for pace regression.
PaceMatrix(int, int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct an m-by-n PACE matrix of zeros.
PaceMatrix(int, int, double) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct an m-by-n constant PACE matrix.
PaceMatrix(double[][]) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PACE matrix from a 2-D array.
PaceMatrix(double[][], int, int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PACE matrix quickly without checking arguments.
PaceMatrix(double[], int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PaceMatrix from a one-dimensional packed array
PaceMatrix(DoubleVector) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PaceMatrix with a single column from a DoubleVector
PaceMatrix(Matrix) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PaceMatrix from a Matrix
PaceRegression - Class in weka.classifiers.functions: Class for building pace regression linear models and using them for prediction.
PaceRegression() - Constructor for class weka.classifiers.functions.PaceRegression
PACKAGE - Static variable in class weka.core.stemmers.SnowballStemmer: the package name for snowball.
PACKAGE_EXT - Static variable in class weka.core.stemmers.SnowballStemmer: the package name where the stemmers are located.
pad(String, String, int, boolean) - Static method in class weka.core.pmml.PMMLUtils: Utility method to left or right pad strings with arbitrary characters.
PADDING_ZERO - Static variable in class weka.filters.unsupervised.attribute.Wavelet: the type of padding: Zero padding
paddingTipText() - Method in class weka.filters.unsupervised.attribute.Wavelet: Returns the tip text for this property
padLeft(String, int) - Static method in class weka.core.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padRight(String, int) - Static method in class weka.core.Utils: Pads a string to a specified length, inserting spaces on the right as required.
paint(Graphics) - Method in class weka.gui.SplashWindow: Paints the image on the window.
paintComponent(Graphics) - Method in class weka.gui.AttributeVisualizationPanel: Paints this component
paintComponent(Graphics) - Method in class weka.gui.beans.BeanVisual
paintComponent(Graphics) - Method in class weka.gui.hierarchyvisualizer.HierarchyVisualizer: Updates the screen contents.
paintComponent(Graphics) - Method in class weka.gui.Main.BackgroundDesktopPane: draws the background image.
paintComponent(Graphics) - Method in class weka.gui.MemoryUsagePanel: draws the background image.
paintComponent(Graphics) - Method in class weka.gui.PropertyPanel: Paints the component, using the property editor's paint method.
paintComponent(Graphics) - Method in class weka.gui.treevisualizer.TreeVisualizer: Updates the screen contents.
paintComponent(Graphics) - Method in class weka.gui.visualize.ClassPanel: Renders this component
paintComponent(Graphics) - Method in class weka.gui.visualize.Plot2D: Renders this component
paintConnections(Graphics) - Static method in class weka.gui.beans.BeanConnection: Renders the connections and their names on the supplied graphics context
paintLabels(Graphics) - Static method in class weka.gui.beans.BeanInstance: Renders the textual labels for the beans.
paintValue(Graphics, Rectangle) - Method in class weka.gui.CostMatrixEditor: Paints a graphical representation of the object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.FileEditor: Paints a representation of the current Object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericArrayEditor: Paints a representation of the current classifier.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericObjectEditor: Paints a representation of the current Object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.SimpleDateFormatEditor: Paints a graphical representation of the object.
PairedCorrectedTTester - Class in weka.experiment: Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic.
For more information see:
Claude Nadeau, Yoshua Bengio (2001).
PairedCorrectedTTester() - Constructor for class weka.experiment.PairedCorrectedTTester
PairedStats - Class in weka.experiment: A class for storing stats on a paired comparison (t-test and correlation)
PairedStats(double) - Constructor for class weka.experiment.PairedStats: Creates a new PairedStats object with the supplied significance level.
PairedStatsCorrected - Class in weka.experiment: A class for storing stats on a paired comparison.
PairedStatsCorrected(double, double) - Constructor for class weka.experiment.PairedStatsCorrected: Creates a new PairedStatsCorrected object with the supplied significance level and train/test ratio.
PairedTTester - Class in weka.experiment: Calculates T-Test statistics on data stored in a set of instances.
PairedTTester() - Constructor for class weka.experiment.PairedTTester
pairwiseCoupling(double[][], double[][]) - Static method in class weka.classifiers.meta.MultiClassClassifier: Implements pairwise coupling.
pairwiseCoupling(double[][], double[][]) - Method in class weka.classifiers.mi.MISMO: Implements pairwise coupling.
parentClass - Variable in class weka.experiment.PropertyNode: The class of the object with this property
parentNode() - Method in class weka.classifiers.trees.m5.RuleNode: Get the parent of this node
ParentSet - Class in weka.classifiers.bayes.net: Helper class for Bayes Network classifiers.
ParentSet() - Constructor for class weka.classifiers.bayes.net.ParentSet: default constructor
ParentSet(int) - Constructor for class weka.classifiers.bayes.net.ParentSet: constructor
ParentSet(ParentSet) - Constructor for class weka.classifiers.bayes.net.ParentSet: copy constructor
parentTipText() - Method in class weka.datagenerators.ClusterDefinition: Returns the tip text for this property
parentValue() - Method in class weka.gui.HierarchyPropertyParser: The value in the parent node.
parse() - Method in class weka.gui.graphvisualizer.BIFParser: This method parses the string or the InputStream that we passed in through the constructor and builds up the m_nodes and m_edges vectors
parse() - Method in class weka.gui.graphvisualizer.DotParser: This method parses the string or the InputStream that we passed in through the constructor and builds up the m_nodes and m_edges vectors
parseDate(String) - Method in class weka.core.Attribute: Parses the given String as Date, according to the current format and returns the corresponding amount of milliseconds.
parseMatlab(String) - Static method in class weka.classifiers.CostMatrix: creates a matrix from the given Matlab string.
parseMatlab(String) - Static method in class weka.core.matrix.Matrix: creates a matrix from the given Matlab string.
parseMatlab(String) - Static method in class weka.core.Matrix: Deprecated.

creates a matrix from the given Matlab string.
parsePath(String) - Static method in class weka.core.PropertyPath.Path: returns a path object based on the given path string
Parser - Class in weka.core.mathematicalexpression: CUP v0.11a beta 20060608 generated parser.
Parser() - Constructor for class weka.core.mathematicalexpression.Parser: Default constructor.
Parser(Scanner) - Constructor for class weka.core.mathematicalexpression.Parser: Constructor which sets the default scanner.
Parser(Scanner, SymbolFactory) - Constructor for class weka.core.mathematicalexpression.Parser: Constructor which sets the default scanner.
Parser - Class in weka.filters.unsupervised.instance.subsetbyexpression: CUP v0.11a beta 20060608 generated parser.
Parser() - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Default constructor.
Parser(Scanner) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Constructor which sets the default scanner.
Parser(Scanner, SymbolFactory) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Constructor which sets the default scanner.
PART - Class in weka.classifiers.rules: Class for generating a PART decision list.
PART() - Constructor for class weka.classifiers.rules.PART
PART_PROPERTY - Static variable in class weka.associations.tertius.IndividualLiteral
partFileTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
partition(Instances, int) - Static method in class weka.classifiers.rules.RuleStats: Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
PartitionedMultiFilter - Class in weka.filters.unsupervised.attribute: A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
PartitionedMultiFilter() - Constructor for class weka.filters.unsupervised.attribute.PartitionedMultiFilter
partitionOptions(String[]) - Static method in class weka.classifiers.bayes.BayesNet: Returns the secondary set of options (if any) contained in the supplied options array.
partitionOptions(String[]) - Static method in class weka.core.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
passesTest(Instance) - Method in class weka.datagenerators.Test: Determines whether an instance passes the test.
passwordTipText() - Method in class weka.core.converters.DatabaseLoader: the tip text for this property
passwordTipText() - Method in class weka.core.converters.DatabaseSaver: Returns the tip text for this property.
passwordTipText() - Method in class weka.experiment.DatabaseUtils: Returns the tip text for this property.
paste(String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Apply paste operation with XMLBIF fragment.
Path(String) - Constructor for class weka.core.PropertyPath.Path: uses the given dot-path
Path(Vector) - Constructor for class weka.core.PropertyPath.Path: uses the vector with PathElement objects to initialize with
Path(String[]) - Constructor for class weka.core.PropertyPath.Path: uses the given array as elements for the path
PathElement(String) - Constructor for class weka.core.PropertyPath.PathElement: initializes the path element with the given property
pattern(int, int) - Static method in class weka.core.matrix.FloatingPointFormat
patternTipText() - Method in class weka.datagenerators.clusterers.BIRCHCluster: Returns the tip text for this property
pchisq(double) - Static method in class weka.core.matrix.Maths: Returns the cumulative probability of the Chi-squared distribution
pchisq(double, double) - Static method in class weka.core.matrix.Maths: Returns the cumulative probability of the noncentral Chi-squared distribution.
pchisq(double, DoubleVector) - Static method in class weka.core.matrix.Maths: Returns the cumulative probability of a set of noncentral Chi-squared distributions.
pctCorrect() - Method in class weka.classifiers.Evaluation: Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctIncorrect() - Method in class weka.classifiers.Evaluation: Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctUnclassified() - Method in class weka.classifiers.Evaluation: Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
peek() - Method in class weka.core.Queue: Gets object from the front of the queue.
perBag(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns number of (possibly fractional) instances in given bag.
percentageTipText() - Method in class weka.filters.supervised.instance.SMOTE: Returns the tip text for this property.
percentageTipText() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Returns the tip text for this property
percentAttributesUsed() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
percentThresholdTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
percentTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the tip text for this property
percentTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
percentToEliminatePerIterationTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
perClass(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns number of (possibly fractional) instances of given class.
perClassPerBag(int, int) - Method in class weka.classifiers.trees.j48.Distribution: Returns number of (possibly fractional) instances of given class in given bag.
PerformanceStats - Class in weka.core.neighboursearch: The class that measures the performance of a nearest neighbour search (NNS) algorithm.
PerformanceStats() - Constructor for class weka.core.neighboursearch.PerformanceStats: default constructor.
performPredictionTipText() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns the tip text for this property
performRankingTipText() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the tip text for this property
performRankingTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the tip text for this property
performRequest(String) - Method in class weka.gui.beans.Associator: Perform a particular request
performRequest(String) - Method in class weka.gui.beans.AttributeSummarizer: Perform a named user request
performRequest(String) - Method in class weka.gui.beans.Classifier: Perform a particular request
performRequest(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Perform the named request
performRequest(String) - Method in class weka.gui.beans.Clusterer: Perform a particular request
performRequest(String) - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Perform the named request
performRequest(String) - Method in class weka.gui.beans.CostBenefitAnalysis
performRequest(String) - Method in class weka.gui.beans.CrossValidationFoldMaker: Perform the named request
performRequest(String) - Method in class weka.gui.beans.DataVisualizer: Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.Filter: Perform the named request
performRequest(String) - Method in class weka.gui.beans.GraphViewer: Perform the named request
performRequest(String) - Method in class weka.gui.beans.MetaBean: Perform a particular request
performRequest(String) - Method in class weka.gui.beans.ModelPerformanceChart: Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.ScatterPlotMatrix: Perform a named user request
performRequest(String) - Method in class weka.gui.beans.StripChart: Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.TextViewer: Perform the named request
performRequest(String) - Method in class weka.gui.beans.TrainTestSplitMaker: Perform the named request
performRequest(String) - Method in interface weka.gui.beans.UserRequestAcceptor: Perform the named request
periodicPruningTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
perturbationFractionTipText() - Method in class weka.datagenerators.classifiers.classification.Agrawal: Returns the tip text for this property
phaseIID(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: See Sugiyama et al.
phaseIIU(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: See Sugiyama et al.
phaseIU(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: See Sugiyama et al.
PI - Static variable in class weka.core.xml.XMLDocument: the parsing instructions "<?xml version=\"1.0\" encoding=\"utf-8\"?>" (may not show up in Javadoc due to tags!).
PKIDiscretize - Class in weka.filters.unsupervised.attribute: Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values.

For more information, see:

Ying Yang, Geoffrey I.
PKIDiscretize() - Constructor for class weka.filters.unsupervised.attribute.PKIDiscretize
place(Node) - Method in interface weka.gui.treevisualizer.NodePlace: The function to call to postion the tree that starts at Node r
place(Node) - Method in class weka.gui.treevisualizer.PlaceNode1: Call this function to have each node in the tree starting at 'r' placed in a visual (not logical, they already are) tree position.
place(Node) - Method in class weka.gui.treevisualizer.PlaceNode2: The Funtion to call to have the nodes arranged.
PlaceNode1 - Class in weka.gui.treevisualizer: This class will place the Nodes of a tree.
PlaceNode1() - Constructor for class weka.gui.treevisualizer.PlaceNode1
PlaceNode2 - Class in weka.gui.treevisualizer: This class will place the Nodes of a tree.
PlaceNode2() - Constructor for class weka.gui.treevisualizer.PlaceNode2
PLAINTEXT_ENDTAG - Static variable in class weka.core.TechnicalInformationHandlerJavadoc: the end comment tag for inserting the generated BibTex
PLAINTEXT_STARTTAG - Static variable in class weka.core.TechnicalInformationHandlerJavadoc: the start comment tag for inserting the generated BibTex
pln(String) - Static method in class weka.core.Debug.DBO: prints out text + endofline.
Plot2D - Class in weka.gui.visualize: This class plots datasets in two dimensions.
Plot2D() - Constructor for class weka.gui.visualize.Plot2D: Constructor
Plot2DCompanion - Interface in weka.gui.visualize: Interface for classes that need to draw to the Plot2D panel *before* Plot2D renders anything (eg.
PlotData2D - Class in weka.gui.visualize: This class is a container for plottable data.
PlotData2D(Instances) - Constructor for class weka.gui.visualize.PlotData2D: Construct a new PlotData2D using the supplied instances
plotTrainingData() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Render the training points on-screen.
plotTrainingData() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Plots the training data on-screen.
PLSClassifier - Class in weka.classifiers.functions: A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
PLSClassifier() - Constructor for class weka.classifiers.functions.PLSClassifier
PLSFilter - Class in weka.filters.supervised.attribute: Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction.
By default it replaces missing values and centers the data.

For more information see:

Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
PLSFilter() - Constructor for class weka.filters.supervised.attribute.PLSFilter: default constructor
PLURAL_DUMMY - Static variable in interface weka.gui.graphvisualizer.GraphConstants: PLURAL_DUMMY node - node with more than one outgoing edge i.e.
plus(DiscreteFunction) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns the combined of two discrete functions
PLUS - Static variable in interface weka.core.mathematicalexpression.sym
plus(double) - Method in class weka.core.matrix.DoubleVector: Adds a value to all the elements
plus(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Adds another vector element by element
plus(Matrix) - Method in class weka.core.matrix.Matrix: C = A + B
PLUS - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
PLUS_SHAPE - Static variable in class weka.gui.visualize.Plot2D
plusEquals(DiscreteFunction) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns the combined of two discrete functions.
plusEquals(double) - Method in class weka.core.matrix.DoubleVector: Adds a value to all the elements in place
plusEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Adds another vector in place element by element
plusEquals(Matrix) - Method in class weka.core.matrix.Matrix: A = A + B
pmiss - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: transformation probability to missing value
PMMethod - Static variable in class weka.classifiers.functions.pace.MixtureDistribution: The probability-measure-based method
PMML_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClassifierPanel: The filename extension that should be used for PMML xml files
PMMLClassifier - Class in weka.classifiers.pmml.consumer: Abstract base class for all PMML classifiers.
PMMLFactory - Class in weka.core.pmml: This class is a factory class for reading/writing PMML models
PMMLFactory() - Constructor for class weka.core.pmml.PMMLFactory
PMMLModel - Interface in weka.core.pmml: Interface for all PMML models
PMMLUtils - Class in weka.core.pmml: Utility routines.
PMMLUtils() - Constructor for class weka.core.pmml.PMMLUtils
PNGWriter - Class in weka.gui.visualize: This class takes any JComponent and outputs it to a PNG-file.
PNGWriter() - Constructor for class weka.gui.visualize.PNGWriter: initializes the object
PNGWriter(JComponent) - Constructor for class weka.gui.visualize.PNGWriter: initializes the object with the given Component
PNGWriter(JComponent, File) - Constructor for class weka.gui.visualize.PNGWriter: initializes the object with the given Component and filename
pnorm(double) - Static method in class weka.core.matrix.Maths: Returns the cumulative probability of the standard normal.
pnorm(double, double, double) - Static method in class weka.core.matrix.Maths: Returns the cumulative probability of a normal distribution.
pnorm(double, DoubleVector, double) - Static method in class weka.core.matrix.Maths: Returns the cumulative probability of a set of normal distributions with different means.
PointsClosestToFurthestChildren - Class in weka.core.neighboursearch.balltrees: Implements the Moore's method to split a node of a ball tree.

For more information please see section 2 of the 1st and 3.2.3 of the 2nd:

Andrew W.
PointsClosestToFurthestChildren() - Constructor for class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren: Constructor.
PointsClosestToFurthestChildren(int[], Instances, EuclideanDistance) - Constructor for class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren: Constructor.
PoissonEstimator - Class in weka.estimators: Simple probability estimator that places a single Poisson distribution over the observed values.
PoissonEstimator() - Constructor for class weka.estimators.PoissonEstimator
POLYGON - Static variable in class weka.gui.visualize.VisualizePanelEvent
PolyKernel - Class in weka.classifiers.functions.supportVector: The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
Valid options are:
PolyKernel() - Constructor for class weka.classifiers.functions.supportVector.PolyKernel: default constructor - does nothing.
PolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.PolyKernel: Creates a new PolyKernel instance.
pop() - Method in class weka.core.neighboursearch.covertrees.Stack: Pops (removes) the first (last added) element in the stack.
pop() - Method in class weka.core.Queue: Pops an object from the front of the queue.
populationSizeTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
populationSizeTipText() - Method in class weka.attributeSelection.ScatterSearchV1: Returns the tip text for this property
populationSizeTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
populationSizeTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
POS - Static variable in class weka.associations.tertius.Literal
position() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the position of the split in the sorted values.
position() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Returns the position of the split in the sorted values.
position() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the position of the split in the sorted values.
positive() - Method in class weka.associations.tertius.Literal
positiveDiagonal(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Sets all diagonal elements to be positive (or nonnegative) without changing the least squares solution
positiveIndexTipText() - Method in class weka.associations.FPGrowth: Tip text for this property suitable for displaying in the GUI.
positives(int) - Method in class weka.classifiers.trees.j48.GraftSplit
positivesForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
postProcess(int[]) - Method in class weka.attributeSelection.ASEvaluation: Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.
postProcess(int[]) - Method in class weka.attributeSelection.CfsSubsetEval: Calls locallyPredictive in order to include locally predictive attributes (if requested).
postProcess(int[]) - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.
postProcess(int[]) - Method in class weka.attributeSelection.GainRatioAttributeEval
postProcess(int[]) - Method in class weka.attributeSelection.OneRAttributeEval
postProcess(int[]) - Method in class weka.attributeSelection.ReliefFAttributeEval
postProcess(ResultProducer) - Method in class weka.experiment.AveragingResultProducer: When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess() - Method in class weka.experiment.AveragingResultProducer: When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.CrossValidationResultProducer: Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.CSVResultListener: Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultListener: Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer: When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess() - Method in class weka.experiment.DatabaseResultProducer: When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.Experiment: Signals that the experiment is finished running, so that cleanup can be done.
postProcess(ResultProducer) - Method in class weka.experiment.InstancesResultListener: Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer: When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess() - Method in class weka.experiment.LearningRateResultProducer: When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.RandomSplitResultProducer: Perform any postprocessing.
postProcess() - Method in class weka.experiment.RemoteExperiment: overides the one in Experiment
postProcess(ResultProducer) - Method in interface weka.experiment.ResultListener: Perform any postprocessing.
postProcess() - Method in interface weka.experiment.ResultProducer: Perform any postprocessing.
postProcessDistances(double[]) - Method in interface weka.core.DistanceFunction: Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).
postProcessDistances(double[]) - Method in class weka.core.EuclideanDistance: Does post processing of the distances (if necessary) returned by distance(distance(Instance first, Instance second, double cutOffValue).
postProcessDistances(double[]) - Method in class weka.core.NormalizableDistance: Does nothing, derived classes may override it though.
PostProcessor() - Constructor for class weka.core.CheckScheme.PostProcessor
PostProcessor() - Constructor for class weka.estimators.CheckEstimator.PostProcessor
PostscriptGraphics - Class in weka.gui.visualize: The PostscriptGraphics class extends the Graphics2D class to produce an encapsulated postscript file rather than on-screen display.
PostscriptGraphics(int, int, OutputStream) - Constructor for class weka.gui.visualize.PostscriptGraphics: Constructor Creates a new PostscriptGraphics object, given dimensions and output file.
PostscriptWriter - Class in weka.gui.visualize: This class takes any Component and outputs it to a Postscript file.
PostscriptWriter() - Constructor for class weka.gui.visualize.PostscriptWriter: initializes the object
PostscriptWriter(JComponent) - Constructor for class weka.gui.visualize.PostscriptWriter: initializes the object with the given Component
PostscriptWriter(JComponent, File) - Constructor for class weka.gui.visualize.PostscriptWriter: initializes the object with the given Component and filename
potential(int, double, double[], double[], boolean) - Method in class weka.classifiers.rules.RuleStats: Calculate the potential to decrease DL of the ruleset, i.e.
PotentialClassIgnorer - Class in weka.filters.unsupervised.attribute: This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
PotentialClassIgnorer() - Constructor for class weka.filters.unsupervised.attribute.PotentialClassIgnorer
POW - Static variable in interface weka.core.mathematicalexpression.sym
POW - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
precision(int) - Method in class weka.classifiers.Evaluation: Calculate the precision with respect to a particular class.
PRECISION - Static variable in class weka.classifiers.meta.ThresholdSelector: precision
PRECISION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: Precision
PrecomputedKernelMatrixKernel - Class in weka.classifiers.functions.supportVector: This kernel is based on a static kernel matrix that is read from a file.
PrecomputedKernelMatrixKernel() - Constructor for class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel
PreConstructedLinearModel - Class in weka.classifiers.trees.m5: This class encapsulates a linear regression function.
PreConstructedLinearModel(double[], double) - Constructor for class weka.classifiers.trees.m5.PreConstructedLinearModel: Constructor
Predicate - Class in weka.associations.tertius
Predicate(String, int, boolean) - Constructor for class weka.associations.tertius.Predicate
predicted() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the predicted class value.
predicted() - Method in class weka.classifiers.evaluation.NumericPrediction: Gets the predicted class value.
predicted() - Method in interface weka.classifiers.evaluation.Prediction: Gets the predicted class value.
predictInterval(Instance, double) - Method in class weka.classifiers.functions.GaussianProcesses: Predicts a confidence interval for the given instance and confidence level.
predictInterval(Instance, double) - Method in interface weka.classifiers.IntervalEstimator: Returns an N*2 array, where N is the number of possible classes, that estimate the boundaries for the confidence interval with a confidence level specified by the second parameter.
Prediction - Interface in weka.classifiers.evaluation: Encapsulates a single evaluatable prediction: the predicted value plus the actual class value.
PredictionAppender - Class in weka.gui.beans: Bean that can can accept batch or incremental classifier events and produce dataset or instance events which contain instances with predictions appended.
PredictionAppender() - Constructor for class weka.gui.beans.PredictionAppender: Creates a new PredictionAppender instance.
PredictionAppenderBeanInfo - Class in weka.gui.beans: Bean info class for PredictionAppender.
PredictionAppenderBeanInfo() - Constructor for class weka.gui.beans.PredictionAppenderBeanInfo
PredictionAppenderCustomizer - Class in weka.gui.beans: GUI Customizer for the prediction appender bean
PredictionAppenderCustomizer() - Constructor for class weka.gui.beans.PredictionAppenderCustomizer
PredictionNode - Class in weka.classifiers.trees.adtree: Class representing a prediction node in an alternating tree.
PredictionNode(double) - Constructor for class weka.classifiers.trees.adtree.PredictionNode: Creates a new prediction node.
predictions() - Method in class weka.classifiers.Evaluation: Returns the predictions that have been collected.
PredictiveApriori - Class in weka.associations: Class implementing the predictive apriori algorithm to mine association rules.
It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value.

For more information see:

Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence.
PredictiveApriori() - Constructor for class weka.associations.PredictiveApriori: Constructor that allows to sets default values for the minimum confidence and the maximum number of rules the minimum confidence.
predictiveError(Instances) - Method in class weka.classifiers.trees.LADTree
prefix() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns tree in prefix order.
prefix() - Method in class weka.classifiers.trees.J48: Returns tree in prefix order.
prefix() - Method in class weka.classifiers.trees.J48graft: Returns tree in prefix order.
prefix() - Method in interface weka.core.Matchable: Returns a string that describes a tree representing the object in prefix order.
PREFIX_CLASSIFIER - Static variable in class weka.classifiers.meta.GridSearch: the prefix to indicate that the option is for the classifier
PREFIX_FILTER - Static variable in class weka.classifiers.meta.GridSearch: the prefix to indicate that the option is for the filter
premise() - Method in class weka.associations.RuleItem: Gets the premise of a rule
prePlot(Graphics) - Method in interface weka.gui.visualize.Plot2DCompanion: Something to be drawn before the plot itself
preprocess(Instances, int) - Method in class weka.classifiers.mi.MINND: Pre-process the given exemplar according to the other exemplars in the given exemplars.
preProcess(ResultProducer) - Method in class weka.experiment.AveragingResultProducer: Prepare for the results to be received.
preProcess() - Method in class weka.experiment.AveragingResultProducer: Prepare to generate results.
preProcess() - Method in class weka.experiment.CrossValidationResultProducer: Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.CSVResultListener: Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultListener: Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer: Prepare for the results to be received.
preProcess() - Method in class weka.experiment.DatabaseResultProducer: Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.InstancesResultListener: Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer: Prepare for the results to be received.
preProcess() - Method in class weka.experiment.LearningRateResultProducer: Prepare to generate results.
preProcess() - Method in class weka.experiment.RandomSplitResultProducer: Prepare to generate results.
preProcess(ResultProducer) - Method in interface weka.experiment.ResultListener: Prepare for the results to be received.
preProcess() - Method in interface weka.experiment.ResultProducer: Prepare to generate results.
preprocessData() - Method in class weka.classifiers.mi.CitationKNN: Calculates the normalization of each attribute.
PREPROCESSING_CENTER - Static variable in class weka.filters.supervised.attribute.PLSFilter: the type of preprocessing: Center
PREPROCESSING_NONE - Static variable in class weka.filters.supervised.attribute.PLSFilter: the type of preprocessing: None
PREPROCESSING_STANDARDIZE - Static variable in class weka.filters.supervised.attribute.PLSFilter: the type of preprocessing: Standardize
preprocessingTipText() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns the tip text for this property
preprocessingTipText() - Method in class weka.filters.unsupervised.attribute.KernelFilter: Returns the tip text for this property
PreprocessPanel - Class in weka.gui.explorer: This panel controls simple preprocessing of instances.
PreprocessPanel() - Constructor for class weka.gui.explorer.PreprocessPanel: Creates the instances panel with no initial instances.
preserveInstancesOrderTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
previous() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
PrincipalComponents - Class in weka.attributeSelection: Performs a principal components analysis and transformation of the data.
PrincipalComponents() - Constructor for class weka.attributeSelection.PrincipalComponents
PrincipalComponents - Class in weka.filters.unsupervised.attribute: Performs a principal components analysis and transformation of the data.
Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%).
Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
PrincipalComponents() - Constructor for class weka.filters.unsupervised.attribute.PrincipalComponents
print() - Method in class weka.classifiers.bayes.net.ADNode: print is used for debugging only and shows the ADTree in ASCII graphics
print(String) - Method in class weka.classifiers.bayes.net.VaryNode: print is used for debugging only, called from ADNode
print(int) - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints the given int to the streams.
print(boolean) - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints the given boolean to the streams.
print(String) - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints the given string to the streams.
print(Object) - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints the given object to the streams.
print(int, int) - Method in class weka.core.matrix.Matrix: Print the matrix to stdout.
print(PrintWriter, int, int) - Method in class weka.core.matrix.Matrix: Print the matrix to the output stream.
print(NumberFormat, int) - Method in class weka.core.matrix.Matrix: Print the matrix to stdout.
print(PrintWriter, NumberFormat, int) - Method in class weka.core.matrix.Matrix: Print the matrix to the output stream.
print(int) - Method in class weka.core.Tee: prints the given int to the streams.
print(long) - Method in class weka.core.Tee: prints the given long to the streams.
print(float) - Method in class weka.core.Tee: prints the given float to the streams.
print(double) - Method in class weka.core.Tee: prints the given double to the streams.
print(boolean) - Method in class weka.core.Tee: prints the given boolean to the streams.
print(char) - Method in class weka.core.Tee: prints the given char to the streams.
print(char[]) - Method in class weka.core.Tee: prints the given char array to the streams.
print(String) - Method in class weka.core.Tee: prints the given string to the streams.
print(Object) - Method in class weka.core.Tee: prints the given object to the streams.
print() - Method in class weka.core.xml.XMLDocument: prints the current DOM document to standard out.
print_hash_code() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Prints the hash code
print_hash_code() - Method in class weka.classifiers.rules.DecisionTableHashKey: Prints the hash code
PrintableComponent - Class in weka.gui.visualize: This class extends the component which is handed over in the constructor by a print dialog.
PrintableComponent(JComponent) - Constructor for class weka.gui.visualize.PrintableComponent: initializes the panel.
PrintableHandler - Interface in weka.gui.visualize: This interface is for all JComponent classes that provide the ability to print itself to a file.
PrintablePanel - Class in weka.gui.visualize: This Panel enables the user to print the panel to various file formats.
PrintablePanel() - Constructor for class weka.gui.visualize.PrintablePanel: initializes the panel
printAllModels() - Method in class weka.classifiers.trees.m5.RuleNode: Print all the linear models at the learf (debugging purposes)
printClassifications(Classifier, Instances, ConverterUtils.DataSource, int, Range, StringBuffer) - Static method in class weka.classifiers.Evaluation: Prints the predictions for the given dataset into a String variable.
printClassifications(Classifier, Instances, ConverterUtils.DataSource, int, Range, boolean, StringBuffer) - Static method in class weka.classifiers.Evaluation: Prints the predictions for the given dataset into a supplied StringBuffer
printElements() - Method in class weka.classifiers.functions.supportVector.SMOset: Prints all the current elements in the set.
printFeatures() - Method in class weka.classifiers.rules.DecisionTable: Returns a string description of the features selected
printInsts(int, int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: For printing indices in some given portion of the master index array.
printLeafModels() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree: Print the models at the leaves
printLeafModels() - Method in class weka.classifiers.trees.m5.RuleNode: print all leaf models
printList(MiddleOutConstructor.MyIdxList) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: For printing indices in a given point list.
println() - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints a new line to the streams.
println(int) - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints the given int to the streams.
println(boolean) - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints the given boolean to the streams.
println(String) - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints the given string to the streams.
println(Object) - Method in class weka.core.logging.OutputLogger.OutputPrintStream: prints the given object to the streams (for Throwables we print the stack trace).
println() - Method in class weka.core.Tee: prints a new line to the streams.
println(int) - Method in class weka.core.Tee: prints the given int to the streams.
println(long) - Method in class weka.core.Tee: prints the given long to the streams.
println(float) - Method in class weka.core.Tee: prints the given float to the streams.
println(double) - Method in class weka.core.Tee: prints the given double to the streams.
println(boolean) - Method in class weka.core.Tee: prints the given boolean to the streams.
println(char) - Method in class weka.core.Tee: prints the given char to the streams.
println(char[]) - Method in class weka.core.Tee: prints the given char array to the streams.
println(String) - Method in class weka.core.Tee: prints the given string to the streams.
println(Object) - Method in class weka.core.Tee: prints the given object to the streams (for Throwables we print the stack trace).
printNewickTipText() - Method in class weka.clusterers.HierarchicalClusterer
printNodeLinearModel() - Method in class weka.classifiers.trees.m5.RuleNode: print the linear model at this node
printSetOfSequences(FastVector) - Static method in class weka.associations.gsp.Sequence: Prints a set of Sequences as String output.
printStackTrace() - Method in class weka.core.Debug.Random: prints the current stacktrace
printSubset(ScatterSearchV1.Subset) - Method in class weka.attributeSelection.ScatterSearchV1
Prior - Class in weka.classifiers.bayes.blr: This is an interface to plug various priors into the Bayesian Logistic Regression Model.
Prior() - Constructor for class weka.classifiers.bayes.blr.Prior
PriorClass - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Distribution Prior class
priorClassTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
priorEntropy() - Method in class weka.classifiers.Evaluation: Calculate the entropy of the prior distribution
PriorEstimation - Class in weka.associations: Class implementing the prior estimattion of the predictive apriori algorithm for mining association rules.
PriorEstimation(Instances, int, int, boolean) - Constructor for class weka.associations.PriorEstimation: Constructor
PriorityQueue - Class in weka.clusterers.forOPTICSAndDBScan.Utils: PriorityQueue.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 27, 2004
Time: 5:36:35 PM
$ Revision 1.4 $
PriorityQueue() - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue: Creates a new PriorityQueue backed on a binary heap.
PriorityQueueElement - Class in weka.clusterers.forOPTICSAndDBScan.Utils: PriorityQueueElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 31, 2004
Time: 6:43:18 PM
$ Revision 1.4 $
PriorityQueueElement(double, Object) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueueElement
Prism - Class in weka.classifiers.rules: Class for building and using a PRISM rule set for classification.
Prism() - Constructor for class weka.classifiers.rules.Prism
prob(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns relative frequency of class over all bags.
prob(int, int) - Method in class weka.classifiers.trees.j48.Distribution: Returns relative frequency of class for given bag.
PROB_COST_FUNC_NAME - Static variable in class weka.classifiers.evaluation.CostCurve: attribute name: Probability Cost Function
probabilityEstimatesTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
probabilityEstimatesTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.ChisqMixture: Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.NormalMixture: Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probRound(double, Random) - Static method in class weka.core.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g.
probToLogOdds(double) - Static method in class weka.core.Utils: Returns the log-odds for a given probabilitiy.
process(boolean[][], BayesNet) - Method in class weka.classifiers.bayes.net.MarginCalculator
process(Instances) - Method in class weka.core.CheckScheme.PostProcessor: Provides a hook for derived classes to further modify the data.
processClassifierPrediction(Instance, Classifier, Evaluation, Instances, FastVector, FastVector) - Static method in class weka.gui.explorer.ClassifierPanel: Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info.
processColour(String, Color) - Static method in class weka.gui.visualize.VisualizeUtils: Parses a string containing either a named colour or r,g,b values.
processFile(String) - Method in class weka.classifiers.bayes.net.BIFReader: processFile reads a BIFXML file and initializes a Bayes Net
processFilename(String) - Method in class weka.gui.Loader: returns the processed filename, i.e.
PROCESSING - Static variable in class weka.experiment.TaskStatusInfo
processKeyString(String) - Static method in class weka.experiment.DatabaseUtils: processes the string in such a way that it can be stored in the database, i.e., it changes backslashes into slashes and doubles single quotes.
processString(String) - Method in class weka.classifiers.bayes.net.BIFReader
PRODUCT_RULE - Static variable in class weka.classifiers.meta.Vote: combination rule: Product of Probabilities (only nominal classes)
production_table() - Method in class weka.core.mathematicalexpression.Parser: Access to production table.
production_table() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Access to production table.
projectionFilterTipText() - Method in class weka.classifiers.meta.RotationForest: Returns the tip text for this property
PROPERTIES_FILE - Static variable in class weka.core.Capabilities: the properties file for managing the tests
PROPERTIES_FILE - Static variable in class weka.core.logging.Logger: the properties file.
PROPERTIES_FILE - Static variable in class weka.gui.treevisualizer.TreeVisualizer: the props file.
property - Variable in class weka.experiment.PropertyNode: Other info about the property
PROPERTY_FILE - Static variable in class weka.core.Copyright: the copyright file
PROPERTY_FILE - Static variable in class weka.experiment.DatabaseUtils: The name of the properties file.
PROPERTY_FILE - Static variable in class weka.gui.experiment.ExperimenterDefaults: The name of the properties file
PROPERTY_FILE - Static variable in class weka.gui.explorer.ExplorerDefaults: The name of the properties file.
PROPERTY_FILE - Static variable in class weka.gui.LookAndFeel: The name of the properties file
propertyChange(PropertyChangeEvent) - Method in class weka.gui.beans.KnowledgeFlowApp: Accept property change events
propertyChange(PropertyChangeEvent) - Method in class weka.gui.PropertySheetPanel: Updates the property sheet panel with a changed property and also passed the event along.
PropertyDialog - Class in weka.gui: Support for PropertyEditors with custom editors: puts the editor into a separate frame.
PropertyDialog(PropertyEditor, int, int) - Constructor for class weka.gui.PropertyDialog: Deprecated.
instead of this constructor, one should use the constructors with an explicit owner (either derived from java.awt.Dialog or from java.awt.Frame) or, if none available, using (Frame) null as owner.
PropertyDialog(Dialog, PropertyEditor) - Constructor for class weka.gui.PropertyDialog: Creates the (screen-centered) editor dialog.
PropertyDialog(Dialog, PropertyEditor, int, int) - Constructor for class weka.gui.PropertyDialog: Creates the editor dialog at the given position.
PropertyDialog(Frame, PropertyEditor) - Constructor for class weka.gui.PropertyDialog: Creates the (screen-centered) editor dialog.
PropertyDialog(Frame, PropertyEditor, int, int) - Constructor for class weka.gui.PropertyDialog: Creates the editor dialog at the given position.
PropertyHandler - Class in weka.core.xml: This class stores information about properties to ignore or properties that are allowed for a certain class.
PropertyHandler() - Constructor for class weka.core.xml.PropertyHandler: initializes the handling
PropertyNode - Class in weka.experiment: Stores information on a property of an object: the class of the object with the property; the property descriptor, and the current value.
PropertyNode(Object) - Constructor for class weka.experiment.PropertyNode: Creates a mostly empty property.
PropertyNode(Object, PropertyDescriptor, Class) - Constructor for class weka.experiment.PropertyNode: Creates a fully specified property node.
PropertyPanel - Class in weka.gui: Support for drawing a property value in a component.
PropertyPanel(PropertyEditor) - Constructor for class weka.gui.PropertyPanel: Create the panel with the supplied property editor.
PropertyPanel(PropertyEditor, boolean) - Constructor for class weka.gui.PropertyPanel: Create the panel with the supplied property editor, optionally ignoring any custom panel the editor can provide.
PropertyPath - Class in weka.core: A helper class for accessing properties in nested objects, e.g., accessing the "getRidge" method of a LinearRegression classifier part of MultipleClassifierCombiner, e.g., Vote.
PropertyPath() - Constructor for class weka.core.PropertyPath
PropertyPath.Path - Class in weka.core: Contains a (property) path structure
PropertyPath.PathElement - Class in weka.core: Represents a single element of a property path
PropertySelectorDialog - Class in weka.gui: Allows the user to select any (supported) property of an object, including properties that any of it's property values may have.
PropertySelectorDialog(Frame, Object) - Constructor for class weka.gui.PropertySelectorDialog: Create the property selection dialog.
PropertySheetPanel - Class in weka.gui: Displays a property sheet where (supported) properties of the target object may be edited.
PropertySheetPanel() - Constructor for class weka.gui.PropertySheetPanel: Creates the property sheet panel.
PropositionalToMultiInstance - Class in weka.filters.unsupervised.attribute: Converts a propositional dataset into a multi-instance dataset (with relational attribute).
PropositionalToMultiInstance() - Constructor for class weka.filters.unsupervised.attribute.PropositionalToMultiInstance
ProtectedProperties - Class in weka.core: Simple class that extends the Properties class so that the properties are unable to be modified.
ProtectedProperties(Properties) - Constructor for class weka.core.ProtectedProperties: Creates a set of protected properties from a set of normal ones.
prune(Instances, boolean) - Method in class weka.classifiers.rules.JRip.RipperRule: Prune all the possible final sequences of the rule using the pruning data.
prune() - Method in class weka.classifiers.trees.ft.FTInnerNode: Prunes a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTLeavesNode: Prunes a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTNode: Method for prunning a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTtree: Abstract Method that prunes a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree: Prunes a tree using C4.5's pruning procedure.
prune() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Prunes a tree using C4.5's pruning procedure.
prune() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree: Prunes a tree.
prune(double) - Method in class weka.classifiers.trees.lmt.LMTNode: Prunes a logistic model tree using the CART pruning scheme, given a cost-complexity parameter alpha.
prune(double[], double[], Instances) - Method in class weka.classifiers.trees.lmt.LMTNode: Method for performing one fold in the cross-validation of the cost-complexity parameter.
prune() - Method in class weka.classifiers.trees.m5.RuleNode: Recursively prune the tree
prune(double) - Method in class weka.classifiers.trees.SimpleCart: Prunes the original tree using the CART pruning scheme, given a cost-complexity parameter alpha.
prune(double[], double[], Instances) - Method in class weka.classifiers.trees.SimpleCart: Method for performing one fold in the cross-validation of minimal cost-complexity pruning.
PruneableClassifierTree - Class in weka.classifiers.trees.j48: Class for handling a tree structure that can be pruned using a pruning set.
PruneableClassifierTree(ModelSelection, boolean, int, boolean, int) - Constructor for class weka.classifiers.trees.j48.PruneableClassifierTree: Constructor for pruneable tree structure.
PruneableDecList - Class in weka.classifiers.rules.part: Class for handling a partial tree structure that can be pruned using a pruning set.
PruneableDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.PruneableDecList: Constructor for pruneable partial tree structure.
pruneItemSets(FastVector, Hashtable) - Static method in class weka.associations.ItemSet: Prunes a set of (k)-item sets using the given (k-1)-item sets.
pruneItemSets(FastVector, Hashtable) - Static method in class weka.associations.LabeledItemSet: Prunes a set of (k)-item sets using the given (k-1)-item sets.
pruneRules(List<FPGrowth.AssociationRule>, ArrayList<Attribute>, boolean) - Static method in class weka.associations.FPGrowth.AssociationRule
pruneRules(FastVector[], double) - Static method in class weka.associations.ItemSet: Prunes a set of rules.
pruneToK(Instances, double[], int) - Method in class weka.classifiers.lazy.IBk: Prunes the list to contain the k nearest neighbors.
PRUNETYPE_LOGLIKELIHOOD - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost: log likelihood pruning
PRUNETYPE_NONE - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost: no pruning
PRUNING_LAMBDA - Static variable in class weka.classifiers.functions.supportVector.StringKernel: Pruning method: Lambda See [2] for details.
PRUNING_NONE - Static variable in class weka.classifiers.functions.supportVector.StringKernel: Pruning method: No Pruning
PRUNING_POSTPRUNING - Static variable in class weka.classifiers.trees.BFTree: pruning strategy: post-pruning
PRUNING_PREPRUNING - Static variable in class weka.classifiers.trees.BFTree: pruning strategy: pre-pruning
PRUNING_UNPRUNED - Static variable in class weka.classifiers.trees.BFTree: pruning strategy: un-pruned
pruningMethodTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the tip text for this property
pruningStrategyTipText() - Method in class weka.classifiers.trees.BFTree: Returns the tip text for this property
pruningTypeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
PSI - Static variable in class weka.core.matrix.Maths: The constant 1 / sqrt(2 pi)
Puk - Class in weka.classifiers.functions.supportVector: The Pearson VII function-based universal kernel.

For more information see:

B.
Puk() - Constructor for class weka.classifiers.functions.supportVector.Puk: default constructor - does nothing.
Puk(Instances, int, double, double) - Constructor for class weka.classifiers.functions.supportVector.Puk: Constructor.
PURE_INPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is a pure input unit.
PURE_OUTPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is a pure output unit.
push(T) - Method in class weka.core.neighboursearch.covertrees.Stack: Pushes the given element to the stack.
push(Stack<T>, T) - Method in class weka.core.neighboursearch.covertrees.Stack: Pushes the given element onto the given stack.
push(Object) - Method in class weka.core.Queue: Appends an object to the back of the queue.
put(Object, Object) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
putAll(Map) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
putResultInTable(String, ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseUtils: Executes a database query to insert a result for the supplied key into the database.

Q

qr() - Method in class weka.core.matrix.Matrix: QR Decomposition
QRDecomposition - Class in weka.core.matrix: QR Decomposition.
QRDecomposition(Matrix) - Constructor for class weka.core.matrix.QRDecomposition: QR Decomposition, computed by Householder reflections.
queryExecuted(QueryExecuteEvent) - Method in interface weka.gui.sql.event.QueryExecuteListener: This method gets called when a query has been executed.
queryExecuted(QueryExecuteEvent) - Method in class weka.gui.sql.ResultPanel: This method gets called when a query has been executed.
queryExecuted(QueryExecuteEvent) - Method in class weka.gui.sql.SqlViewer: This method gets called when a query has been executed.
QueryExecuteEvent - Class in weka.gui.sql.event: An event that is generated when a query is executed.
QueryExecuteEvent(Object, DbUtils, String, int, ResultSet, Exception) - Constructor for class weka.gui.sql.event.QueryExecuteEvent: constructs the event
QueryExecuteListener - Interface in weka.gui.sql.event: A listener for executing queries.
QueryPanel - Class in weka.gui.sql: Represents a panel for entering an SQL query.
QueryPanel(JFrame) - Constructor for class weka.gui.sql.QueryPanel: initializes the panel.
queryTipText() - Method in class weka.core.converters.DatabaseLoader: the tip text for this property
queryTipText() - Method in class weka.experiment.InstanceQuery: Returns the tip text for this property
Queue - Class in weka.core: Class representing a FIFO queue.
Queue() - Constructor for class weka.core.Queue
quickSort(double[], double[], int, int) - Static method in class weka.core.neighboursearch.NearestNeighbourSearch: performs quicksort.
quote(String) - Static method in class weka.core.Utils: Quotes a string if it contains special characters.

R

R - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: R(i)= BetaVector X x(i) X y(i).
R_HIGH - Static variable in class weka.clusterers.XMeans: Index in ranges for HIGH.
R_LOW - Static variable in class weka.clusterers.XMeans: Index in ranges for LOW.
R_MAX - Static variable in class weka.core.NormalizableDistance: Index in ranges for MAX.
R_MIN - Static variable in class weka.core.NormalizableDistance: Index in ranges for MIN.
R_WIDTH - Static variable in class weka.clusterers.XMeans: Index in ranges for WIDTH.
R_WIDTH - Static variable in class weka.core.NormalizableDistance: Index in ranges for WIDTH.
RacedIncrementalLogitBoost - Class in weka.classifiers.meta: Classifier for incremental learning of large datasets by way of racing logit-boosted committees.

For more information see:

Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets.
RacedIncrementalLogitBoost() - Constructor for class weka.classifiers.meta.RacedIncrementalLogitBoost: Constructor.
RaceSearch - Class in weka.attributeSelection: Races the cross validation error of competing attribute subsets.
RaceSearch() - Constructor for class weka.attributeSelection.RaceSearch
raceTypeTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
randEntropy - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the random entropy
Random() - Constructor for class weka.core.Debug.Random: Creates a new random number generator.
Random(long) - Constructor for class weka.core.Debug.Random: Creates a new random number generator using a single long seed.
Random(boolean) - Constructor for class weka.core.Debug.Random: Creates a new random number generator.
Random(long, boolean) - Constructor for class weka.core.Debug.Random: Creates a new random number generator using a single long seed.
random(int) - Static method in class weka.core.matrix.DoubleVector: Returns a random vector of uniform distribution
random(int, int) - Static method in class weka.core.matrix.Matrix: Generate matrix with random elements
RANDOM - Static variable in class weka.datagenerators.clusterers.BIRCHCluster: Constant set for choice of pattern.
RANDOM - Static variable in class weka.filters.supervised.attribute.ClassOrder: The class values are sorted in random order
randomCARule(int, int, Random) - Method in class weka.associations.PriorEstimation: Constructs an item set of certain length randomly.
RandomCommittee - Class in weka.classifiers.meta: Class for building an ensemble of randomizable base classifiers.
RandomCommittee() - Constructor for class weka.classifiers.meta.RandomCommittee: Constructor.
RandomForest - Class in weka.classifiers.trees: Class for constructing a forest of random trees.

For more information see:

Leo Breiman (2001).
RandomForest() - Constructor for class weka.classifiers.trees.RandomForest
Randomizable - Interface in weka.core: Interface to something that has random behaviour that is able to be seeded with an integer.
RandomizableClassifier - Class in weka.classifiers: Abstract utility class for handling settings common to randomizable classifiers.
RandomizableClassifier() - Constructor for class weka.classifiers.RandomizableClassifier
RandomizableClusterer - Class in weka.clusterers: Abstract utility class for handling settings common to randomizable clusterers.
RandomizableClusterer() - Constructor for class weka.clusterers.RandomizableClusterer
RandomizableDensityBasedClusterer - Class in weka.clusterers: Abstract utility class for handling settings common to randomizable clusterers.
RandomizableDensityBasedClusterer() - Constructor for class weka.clusterers.RandomizableDensityBasedClusterer
RandomizableIteratedSingleClassifierEnhancer - Class in weka.classifiers: Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
RandomizableIteratedSingleClassifierEnhancer() - Constructor for class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
RandomizableMultipleClassifiersCombiner - Class in weka.classifiers: Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.
RandomizableMultipleClassifiersCombiner() - Constructor for class weka.classifiers.RandomizableMultipleClassifiersCombiner
RandomizableSingleClassifierEnhancer - Class in weka.classifiers: Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
RandomizableSingleClassifierEnhancer() - Constructor for class weka.classifiers.RandomizableSingleClassifierEnhancer
RandomizableSingleClustererEnhancer - Class in weka.clusterers: Abstract utility class for handling settings common to randomizable clusterers.
RandomizableSingleClustererEnhancer() - Constructor for class weka.clusterers.RandomizableSingleClustererEnhancer
randomize(int[], Random) - Method in class weka.classifiers.BVDecomposeSegCVSub: Accepts an array of ints and randomises the values in the array, using the random seed.
randomize(Random) - Method in class weka.core.Instances: Shuffles the instances in the set so that they are ordered randomly.
Randomize - Class in weka.filters.unsupervised.instance: Randomly shuffles the order of instances passed through it.
Randomize() - Constructor for class weka.filters.unsupervised.instance.Randomize
RANDOMIZED - Static variable in class weka.datagenerators.clusterers.BIRCHCluster: Constant set for input order (default)
randomizeDataTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
randomizeTipText() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Returns the tip text for this property
randomNormal(int, int) - Static method in class weka.classifiers.functions.pace.PaceMatrix: Generate matrix with standard-normally distributed random elements
randomOrderTipText() - Method in class weka.classifiers.bayes.net.search.global.K2
randomOrderTipText() - Method in class weka.classifiers.bayes.net.search.local.K2
RandomProjection - Class in weka.filters.unsupervised.attribute: Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (i.e.
RandomProjection() - Constructor for class weka.filters.unsupervised.attribute.RandomProjection
RandomRBF - Class in weka.datagenerators.classifiers.classification: RandomRBF data is generated by first creating a random set of centers for each class.
RandomRBF() - Constructor for class weka.datagenerators.classifiers.classification.RandomRBF: initializes the generator with default values
randomRule(int, int, Random) - Method in class weka.associations.PriorEstimation: Constructs an item set of certain length randomly.
RandomSearch - Class in weka.attributeSelection: RandomSearch :

Performs a Random search in the space of attribute subsets.
RandomSearch() - Constructor for class weka.attributeSelection.RandomSearch: Constructor
randomSeedTipText() - Method in class weka.classifiers.functions.LeastMedSq: Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.trees.ADTree
randomSeedTipText() - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Returns the tip text for this property.
randomSeedTipText() - Method in class weka.filters.supervised.instance.Resample: Returns the tip text for this property.
randomSeedTipText() - Method in class weka.filters.supervised.instance.SMOTE: Returns the tip text for this property.
randomSeedTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.instance.Randomize: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.instance.Resample: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Returns the tip text for this property
RandomSplitResultProducer - Class in weka.experiment: Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
RandomSplitResultProducer() - Constructor for class weka.experiment.RandomSplitResultProducer
RandomSubset - Class in weka.filters.unsupervised.attribute: Chooses a random subset of attributes, either an absolute number or a percentage.
RandomSubset() - Constructor for class weka.filters.unsupervised.attribute.RandomSubset
RandomSubSpace - Class in weka.classifiers.meta: This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
RandomSubSpace() - Constructor for class weka.classifiers.meta.RandomSubSpace: Constructor.
randomTipText() - Method in class weka.datagenerators.DataGenerator: Returns the tip text for this property
RandomTree - Class in weka.classifiers.trees: Class for constructing a tree that considers K randomly chosen attributes at each node.
RandomTree() - Constructor for class weka.classifiers.trees.RandomTree
RandomVariates - Class in weka.core: Class implementing some simple random variates generator.
RandomVariates() - Constructor for class weka.core.RandomVariates: Simply the constructor of super class
RandomVariates(long) - Constructor for class weka.core.RandomVariates: Simply the constructor of super class
randomWidthFactorTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
Range - Class in weka.core: Class representing a range of cardinal numbers.
Range() - Constructor for class weka.core.Range: Default constructor.
Range(String) - Constructor for class weka.core.Range: Constructor to set initial range.
RANGE_BOUNDS - Static variable in class weka.classifiers.meta.ThresholdSelector: Correct based on min/max observed
RANGE_NONE - Static variable in class weka.classifiers.meta.ThresholdSelector: no range correction
rangeCorrectionTipText() - Method in class weka.classifiers.meta.ThresholdSelector
rangesSet() - Method in class weka.core.NormalizableDistance: Check if ranges are set.
rangesTipText() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Returns the tip text for this property.
rank() - Method in class weka.core.matrix.Matrix: Matrix rank
rank() - Method in class weka.core.matrix.SingularValueDecomposition: Effective numerical matrix rank
rankAttributes(Instances, SubsetEvaluator, boolean) - Method in class weka.attributeSelection.LFSMethods
RankEachAttribute() - Method in class weka.attributeSelection.ScatterSearchV1: Rank all the attributes individually acording to their merits
rankedAttributes() - Method in class weka.attributeSelection.AttributeSelection: get the final ranking of the attributes.
rankedAttributes() - Method in class weka.attributeSelection.GreedyStepwise: Produces a ranked list of attributes.
rankedAttributes() - Method in class weka.attributeSelection.RaceSearch
rankedAttributes() - Method in interface weka.attributeSelection.RankedOutputSearch: Returns a X by 2 list of attribute indexes and corresponding evaluations from best (highest) to worst.
rankedAttributes() - Method in class weka.attributeSelection.Ranker: Sorts the evaluated attribute list
RankedOutputSearch - Interface in weka.attributeSelection: Interface for search methods capable of producing a ranked list of attributes.
Ranker - Class in weka.attributeSelection: Ranker :

Ranks attributes by their individual evaluations.
Ranker() - Constructor for class weka.attributeSelection.Ranker: Constructor
RankSearch - Class in weka.attributeSelection: RankSearch :

Uses an attribute/subset evaluator to rank all attributes.
RankSearch() - Constructor for class weka.attributeSelection.RankSearch: Constructor
rankTipText() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
RBFKernel - Class in weka.classifiers.functions.supportVector: The RBF kernel.
RBFKernel() - Constructor for class weka.classifiers.functions.supportVector.RBFKernel: default constructor - does nothing.
RBFKernel(Instances, int, double) - Constructor for class weka.classifiers.functions.supportVector.RBFKernel: Constructor.
RBFNetwork - Class in weka.classifiers.functions: Class that implements a normalized Gaussian radial basisbasis function network.
It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that.
RBFNetwork() - Constructor for class weka.classifiers.functions.RBFNetwork
rbind(PaceMatrix) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns a new matrix which binds two matrices together with rows.
rchisq(int, double, Random) - Static method in class weka.core.matrix.Maths: Generates a sample of a Chi-square distribution.
RDG1 - Class in weka.datagenerators.classifiers.classification: A data generator that produces data randomly by producing a decision list.
The decision list consists of rules.
Instances are generated randomly one by one.
RDG1() - Constructor for class weka.datagenerators.classifiers.classification.RDG1: initializes the generator with default values
read(String) - Static method in class weka.core.converters.ConverterUtils.DataSource: convencience method for loading a dataset in batch mode.
read(InputStream) - Static method in class weka.core.converters.ConverterUtils.DataSource: convencience method for loading a dataset in batch mode from a stream.
read(Loader) - Static method in class weka.core.converters.ConverterUtils.DataSource: convencience method for loading a dataset in batch mode.
read(BufferedReader) - Static method in class weka.core.matrix.Matrix: Read a matrix from a stream.
read(String) - Static method in class weka.core.SerializationHelper: deserializes the given file and returns the object from it.
read(InputStream) - Static method in class weka.core.SerializationHelper: deserializes from the given stream and returns the object from it.
read(String) - Method in class weka.core.Stopwords: Generates a new Stopwords object from the given file
read(File) - Method in class weka.core.Stopwords: Generates a new Stopwords object from the given file
read(BufferedReader) - Method in class weka.core.Stopwords: Generates a new Stopwords object from the reader.
read(String) - Static method in class weka.core.xml.KOML: reads the XML-serialized object from the given file
read(File) - Static method in class weka.core.xml.KOML: reads the XML-serialized object from the given file
read(InputStream) - Static method in class weka.core.xml.KOML: reads the XML-serialized object from a stream
read(String) - Method in class weka.core.xml.XMLDocument: parses the given XML string (can be XML or a filename) and returns a DOM Document.
read(File) - Method in class weka.core.xml.XMLDocument: parses the given file and returns a DOM document.
read(InputStream) - Method in class weka.core.xml.XMLDocument: parses the given stream and returns a DOM document.
read(Reader) - Method in class weka.core.xml.XMLDocument: parses the given reader and returns a DOM document.
read(String) - Method in class weka.core.xml.XMLSerialization: parses the given XML string (can be XML or a filename) and returns an Object generated from the representation
read(File) - Method in class weka.core.xml.XMLSerialization: parses the given file and returns a DOM document
read(InputStream) - Method in class weka.core.xml.XMLSerialization: parses the given stream and returns a DOM document
read(Reader) - Method in class weka.core.xml.XMLSerialization: parses the given reader and returns a DOM document
read() - Method in class weka.core.xml.XMLSerializationMethodHandler: returns the handler for read methods
read(String) - Static method in class weka.core.xml.XStream: reads the XML-serialized object from the given file
read(File) - Static method in class weka.core.xml.XStream: reads the XML-serialized object from the given file
read(InputStream) - Static method in class weka.core.xml.XStream: reads the XML-serialized object from the given input stream
read(Reader) - Static method in class weka.core.xml.XStream: reads the XML-serialized object from the given Reader
read(String) - Static method in class weka.experiment.Experiment: Loads an experiment from a file.
readAll(String) - Static method in class weka.core.SerializationHelper: deserializes the given file and returns the objects from it.
readAll(InputStream) - Static method in class weka.core.SerializationHelper: deserializes from the given stream and returns the object from it.
readBeanConnection(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the BeanConnection from the given DOM node.
readBeanInstance(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the BeanInstance from the given DOM node.
readBeanVisual(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the BeanVisual from the given DOM node.
readBIF(String) - Method in class weka.gui.graphvisualizer.GraphVisualizer: BIF reader
Reads a graph description in XMLBIF03 from a string
readBIF(InputStream) - Method in class weka.gui.graphvisualizer.GraphVisualizer: BIF reader
Reads a graph description in XMLBIF03 from an InputStrem
readBIFFromFile(String) - Method in class weka.classifiers.bayes.net.GUI: BIF reader
Reads a graph description in XMLBIF03 from an file with name sFileName
readBooleanFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the primitive from the given DOM node.
readByteFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the primitive from the given DOM node.
readCharFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the primitive from the given DOM node.
readCollection(Element) - Method in class weka.core.xml.XMLBasicSerialization: builds the Collection from the given DOM node.
readColor(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the Color from the given DOM node.
readColorUIResource(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the ColorUIResource from the given DOM node.
readCostMatrixOld(Element) - Method in class weka.core.xml.XMLBasicSerialization: builds the Matrix (old) from the given DOM node.
readDefaultListModel(Element) - Method in class weka.core.xml.XMLBasicSerialization: builds the DefaultListModel from the given DOM node.
readDimension(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the Dimension from the given DOM node.
readDOT(Reader) - Method in class weka.gui.graphvisualizer.GraphVisualizer: Dot reader
Reads a graph description in DOT format from a string
readDoubleFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the primitive from the given DOM node.
readFloatFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the primitive from the given DOM node.
readFont(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the Font from the given DOM node.
readFontUIResource(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the FontUIResource from the given DOM node.
readFromXML(Object, String, Element) - Method in class weka.core.xml.XMLSerialization: adds the specific node to the object via a set method
readFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the object from the given DOM node.
readInstance(Instances) - Method in class weka.core.converters.ArffLoader.ArffReader: Reads a single instance using the tokenizer and returns it.
readInstance(Instances, boolean) - Method in class weka.core.converters.ArffLoader.ArffReader: Reads a single instance using the tokenizer and returns it.
readInstance(Reader) - Method in class weka.core.Instances: Deprecated.
instead of using this method in conjunction with the readInstance(Reader) method, one should use the ArffLoader or DataSource class instead.
readIntFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the primitive from the given DOM node.
readLoader(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the Loader from the given DOM node.
readLongFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the primitive from the given DOM node.
readMap(Element) - Method in class weka.core.xml.XMLBasicSerialization: builds the Map from the given DOM node.
readMatrix(Element) - Method in class weka.core.xml.XMLBasicSerialization: builds the Matrix from the given DOM node.
readMatrixOld(Element) - Method in class weka.core.xml.XMLBasicSerialization: builds the Matrix (old) from the given DOM node.
readMetaBean(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the MetaBean from the given DOM node.
readOldFormat(Reader) - Method in class weka.classifiers.CostMatrix: Loads a cost matrix in the old format from a reader.
readPoint(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the Point from the given DOM node.
readProperties(String) - Static method in class weka.core.Utils: Reads properties that inherit from three locations.
readPropertyNode(Element) - Method in class weka.experiment.xml.XMLExperiment: builds the PropertyNode from the given DOM node.
readSaver(Element) - Method in class weka.gui.beans.xml.XMLBeans: builds the Saver from the given DOM node.
readShortFromXML(Element) - Method in class weka.core.xml.XMLSerialization: builds the primitive from the given DOM node.
realCount - Variable in class weka.core.AttributeStats: The number of real-like values (i.e.
recall(int) - Method in class weka.classifiers.Evaluation: Calculate the recall with respect to a particular class.
RECALL - Static variable in class weka.classifiers.meta.ThresholdSelector: recall
RECALL_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: Recall
RECTANGLE - Static variable in class weka.gui.visualize.VisualizePanelEvent
redo() - Method in class weka.classifiers.bayes.net.EditableBayesNet: redo the last edit action performed on the network.
reduce_table() - Method in class weka.core.mathematicalexpression.Parser: Access to reduce_goto table.
reduce_table() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Access to reduce_goto table.
reducedErrorPruningTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
reducedErrorPruningTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
reduceDimensionality(Instances) - Method in class weka.attributeSelection.AttributeSelection: reduce the dimensionality of a set of instances to include only those attributes chosen by the last run of attribute selection.
reduceDimensionality(Instance) - Method in class weka.attributeSelection.AttributeSelection: reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection.
reduceDL(double, boolean) - Method in class weka.classifiers.rules.RuleStats: Try to reduce the DL of the ruleset by testing removing the rules one by one in reverse order and update all the stats
reduceMatrix(double[][]) - Static method in class weka.core.ContingencyTables: Reduces a matrix by deleting all zero rows and columns.
ReferenceInstances - Class in weka.classifiers.trees.adtree: Simple class that extends the Instances class making it possible to create subsets of instances that reference their source set.
ReferenceInstances(Instances, int) - Constructor for class weka.classifiers.trees.adtree.ReferenceInstances: Creates an empty set of instances.
refine(ArrayList) - Method in class weka.associations.tertius.Rule: Refine a rule by adding literal from a set of predictes.
refresh() - Method in class weka.gui.arffviewer.ArffViewer: validates and repaints the frame
refresh() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: validates and repaints the frame
refreshFreqTipText() - Method in class weka.gui.beans.StripChart: GUI Tip text
register(Object, Class, String) - Method in class weka.core.xml.XMLSerializationMethodHandler: adds read and write methods for the given class, i.e., read&;lt;name> and write<name> ("name" is prefixed by read and write)
registerEditors() - Static method in class weka.gui.GenericObjectEditor: registers all the editors in Weka.
RegOptimizer - Class in weka.classifiers.functions.supportVector: Base class implementation for learning algorithm of SMOreg Valid options are:
RegOptimizer() - Constructor for class weka.classifiers.functions.supportVector.RegOptimizer: the default constructor
regOptimizerTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
Regression - Class in weka.classifiers.pmml.consumer: Class implementing import of PMML Regression model.
Regression(Element, Instances, MiningSchema) - Constructor for class weka.classifiers.pmml.consumer.Regression: Constructs a new PMML Regression.
regression(Matrix, double) - Method in class weka.core.matrix.Matrix: Performs a (ridged) linear regression.
regression(Matrix, double[], double) - Method in class weka.core.matrix.Matrix: Performs a weighted (ridged) linear regression.
regression(Matrix, double) - Method in class weka.core.Matrix: Deprecated.

Performs a (ridged) linear regression.
regression(Matrix, double[], double) - Method in class weka.core.Matrix: Deprecated.

Performs a weighted (ridged) linear regression.
RegressionByDiscretization - Class in weka.classifiers.meta: A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
RegressionByDiscretization() - Constructor for class weka.classifiers.meta.RegressionByDiscretization: Default constructor.
RegressionGenerator - Class in weka.datagenerators: Abstract class for data generators for regression classifiers.
RegressionGenerator() - Constructor for class weka.datagenerators.RegressionGenerator: initializes the generator with default values
RegressionSplitEvaluator - Class in weka.experiment: A SplitEvaluator that produces results for a classification scheme on a numeric class attribute.
RegressionSplitEvaluator() - Constructor for class weka.experiment.RegressionSplitEvaluator: No args constructor.
RegSMO - Class in weka.classifiers.functions.supportVector: Implementation of SMO for support vector regression as described in :

A.J.
RegSMO() - Constructor for class weka.classifiers.functions.supportVector.RegSMO: default constructor
RegSMOImproved - Class in weka.classifiers.functions.supportVector: Learn SVM for regression using SMO with Shevade, Keerthi, et al.
RegSMOImproved() - Constructor for class weka.classifiers.functions.supportVector.RegSMOImproved
relabelTipText() - Method in class weka.classifiers.trees.J48graft: Returns the tip text for this property
RELAGGS - Class in weka.filters.unsupervised.attribute: A propositionalization filter inspired by the RELAGGS algorithm.
It processes all relational attributes that fall into the user defined range (all others are skipped, i.e., not added to the output).
RELAGGS() - Constructor for class weka.filters.unsupervised.attribute.RELAGGS
relation() - Method in class weka.core.Attribute: Returns the header info for a relation-valued attribute, null if the attribute is not relation-valued.
relation(int) - Method in class weka.core.Attribute: Returns a value of a relation-valued attribute.
RELATION_NAME - Static variable in class weka.classifiers.evaluation.CostCurve: The name of the relation used in cost curve datasets
RELATION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: The name of the relation used in threshold curve datasets
RELATIONAL - Static variable in class weka.core.Attribute: Constant set for relation-valued attributes.
RelationalLocator - Class in weka.core: This class locates and records the indices of relational attributes,
RelationalLocator(Instances) - Constructor for class weka.core.RelationalLocator: Initializes the RelationalLocator with the given data.
RelationalLocator(Instances, int, int) - Constructor for class weka.core.RelationalLocator: Initializes the RelationalLocator with the given data.
RelationalLocator(Instances, int[]) - Constructor for class weka.core.RelationalLocator: Initializes the RelationalLocator with the given data.
relationalValue(int) - Method in class weka.core.Instance: Returns the relational value of a relational attribute.
relationalValue(Attribute) - Method in class weka.core.Instance: Returns the relational value of a relational attribute.
relationForTableNameTipText() - Method in class weka.core.converters.DatabaseSaver: Returns the tip text fo this property.
relationName() - Method in class weka.core.Instances: Returns the relation's name.
relationNameTipText() - Method in class weka.datagenerators.DataGenerator: Returns the tip text for this property
relativeAbsoluteError() - Method in class weka.classifiers.Evaluation: Returns the relative absolute error.
relativeDL(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats: The description length (DL) of the ruleset relative to if the rule in the given position is deleted, which is obtained by:
MDL if the rule exists - MDL if the rule does not exist
Note the minimal possible DL of the ruleset is calculated(i.e.
ReliefFAttributeEval - Class in weka.attributeSelection: ReliefFAttributeEval :

Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class.
ReliefFAttributeEval() - Constructor for class weka.attributeSelection.ReliefFAttributeEval: Constructor
RemoteBoundaryVisualizerSubTask - Class in weka.gui.boundaryvisualizer: Class that encapsulates a sub task for distributed boundary visualization.
RemoteBoundaryVisualizerSubTask() - Constructor for class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
RemoteEngine - Class in weka.experiment: A general purpose server for executing Task objects sent via RMI.
RemoteEngine(String) - Constructor for class weka.experiment.RemoteEngine: Constructor
RemoteExperiment - Class in weka.experiment: Holds all the necessary configuration information for a distributed experiment.
RemoteExperiment() - Constructor for class weka.experiment.RemoteExperiment: Construct a new RemoteExperiment using an empty Experiment as base Experiment
RemoteExperiment(Experiment) - Constructor for class weka.experiment.RemoteExperiment: Construct a new RemoteExperiment using a base Experiment
RemoteExperimentEvent - Class in weka.experiment: Class encapsulating information on progress of a remote experiment
RemoteExperimentEvent(boolean, boolean, boolean, String) - Constructor for class weka.experiment.RemoteExperimentEvent: Constructor
RemoteExperimentListener - Interface in weka.experiment: Interface for classes that want to listen for updates on RemoteExperiment progress
remoteExperimentStatus(RemoteExperimentEvent) - Method in interface weka.experiment.RemoteExperimentListener: Called when progress has been made in a remote experiment
RemoteExperimentSubTask - Class in weka.experiment: Class to encapsulate an experiment as a task that can be executed on a remote host.
RemoteExperimentSubTask() - Constructor for class weka.experiment.RemoteExperimentSubTask
RemoteResult - Class in weka.gui.boundaryvisualizer: Class that encapsulates a result (and progress info) for part of a distributed boundary visualization.
RemoteResult(int, int) - Constructor for class weka.gui.boundaryvisualizer.RemoteResult: Creates a new RemoteResult instance.
remove() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
remove() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
remove(Object) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
remove(String) - Method in class weka.core.Stopwords: removes the word from the stopword list
remove(PrintStream) - Method in class weka.core.Tee: removes the given PrintStream from the list.
remove(int) - Method in class weka.core.Tee: removes the given PrintStream from the list.
remove(Object) - Method in class weka.core.Trie: Removes a single instance of the specified element from this collection, if it is present.
remove() - Method in class weka.core.Trie.TrieIterator: ignored
remove(String) - Method in class weka.core.Trie.TrieNode: Removes a suffix from the trie.
remove(String) - Method in class weka.core.xml.MethodHandler: removes the method for the property specified by the display name from its internal list.
remove(Class) - Method in class weka.core.xml.MethodHandler: removes the method for the specified class from its internal list.
Remove - Class in weka.filters.unsupervised.attribute: A filter that removes a range of attributes from the dataset.
Remove() - Constructor for class weka.filters.unsupervised.attribute.Remove: Constructor so that we can initialize the Range variable properly.
remove() - Method in class weka.gui.beans.BeanConnection: Remove this connection
remove(int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Removes the element at the specified position in this list.
REMOVE_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
REMOVE_POINT_RADIUS - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanel: The distance we can click away from a point in the GUI and still remove it.
removeActionListener(ActionListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Remove a listener
removeAll(Collection<?>) - Method in class weka.core.Trie: Removes all this collection's elements that are also contained in the specified collection
removeAllBeansFromContainer(JComponent) - Static method in class weka.gui.beans.BeanInstance: Removes all beans from containing component
removeAllElements() - Method in class weka.core.FastVector: Removes all components from this vector and sets its size to zero.
removeAllElements() - Method in class weka.core.Queue: Removes all objects from the queue m_Tail.m_Next.
removeAllInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: This function will remove all the inputs to this unit.
removeAllInputs() - Method in class weka.classifiers.functions.neural.NeuralNode: This function will remove all the inputs to this unit.
removeAllInstances() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Deletes all training instances from our dataset.
removeAllMissingColsTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
removeAllOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: This function will remove all outputs to this unit.
removeAllowed(Class, String) - Method in class weka.core.xml.PropertyHandler: removes the given property (display name) for the specified class from the list of allowed properties.
removeAllPlots() - Method in class weka.gui.visualize.Plot2D: Clears all plots
removeAllPlots() - Method in class weka.gui.visualize.VisualizePanel: Removes all the plots.
removeBatchClassifierListener(BatchClassifierListener) - Method in class weka.gui.beans.Classifier: Remove a batch classifier listener
removeBatchClustererListener(BatchClustererListener) - Method in class weka.gui.beans.Clusterer: Remove a batch clusterer listener
removeBean(JComponent) - Method in class weka.gui.beans.BeanInstance: Remove this bean from the list of beans and from the containing component
removeCancelListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to remove an action listener from the cancel button.
removeCapabilitiesFilter() - Method in class weka.gui.GenericObjectEditor: Removes the current Capabilities filter.
removeCapabilitiesFilterListener(Explorer.CapabilitiesFilterChangeListener) - Method in class weka.gui.explorer.Explorer: Removes the specified listener from the set of listeners if it is present.
removeChangeListener(ChangeListener) - Method in class weka.gui.arffviewer.ArffPanel: Removes a ChangeListener from the panel
removeChangeListener(ChangeListener) - Method in class weka.gui.arffviewer.ArffTable: Removes a ChangeListener from the panel
removeChartListener(ChartListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Remove a chart listener
removeChildFrame(Container) - Method in class weka.gui.GUIChooser: tries to remove the child frame, it returns true if it could do such.
removeChildFrame(Container) - Method in class weka.gui.Main: tries to remove the child frame, it returns true if it could do such.
removeClassColumnTipText() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Returns the tip text for this property
removeConnectionListener(ConnectionListener) - Method in class weka.gui.sql.ConnectionPanel: removes the given listener from the list of listeners.
removeConnectionListener(ConnectionListener) - Method in class weka.gui.sql.SqlViewer: removes the given listener from the list of listeners.
removeConnections(BeanInstance) - Static method in class weka.gui.beans.BeanConnection: Remove all connections for a bean.
removeDataFormatListener(DataFormatListener) - Method in class weka.gui.beans.ClassAssigner
removeDataFormatListener(DataFormatListener) - Method in class weka.gui.beans.ClassValuePicker
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.AbstractDataSource: Remove a listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassAssigner
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassValuePicker
removeDataSourceListener(DataSourceListener) - Method in interface weka.gui.beans.DataSource: Remove a data source listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.DataVisualizer: Remove a listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Filter: Remove a data source listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Loader: Remove a listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.PredictionAppender: Remove a datasource listener
removedPercentageTipText() - Method in class weka.classifiers.meta.RotationForest: Returns the tip text for this property
removeElement(Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Removes the first (lowest-indexed) occurrence of the argument from this list.
removeElementAt(int) - Method in class weka.core.FastVector: Deletes an element from this vector.
removeFirst() - Method in class weka.associations.tertius.SimpleLinkedList
RemoveFolds - Class in weka.filters.unsupervised.instance: This filter takes a dataset and outputs a specified fold for cross validation.
RemoveFolds() - Constructor for class weka.filters.unsupervised.instance.RemoveFolds
RemoveFrequentValues - Class in weka.filters.unsupervised.instance: Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
RemoveFrequentValues() - Constructor for class weka.filters.unsupervised.instance.RemoveFrequentValues
removeGraphListener(GraphListener) - Method in class weka.gui.beans.Associator: Remove a graph listener
removeGraphListener(GraphListener) - Method in class weka.gui.beans.Classifier: Remove a graph listener
removeGraphListener(GraphListener) - Method in class weka.gui.beans.Clusterer: Remove a graph listener
removeHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.ConnectionPanel: removes the given listener from the list of listeners.
removeHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.QueryPanel: removes the given listener from the list of listeners.
removeHistoryChangedListener(HistoryChangedListener) - Method in class weka.gui.sql.SqlViewer: removes the given listener from the list of listeners.
removeIgnored(String) - Method in class weka.core.xml.PropertyHandler: removes the given display name from the ignore list.
removeIgnored(Class, String) - Method in class weka.core.xml.PropertyHandler: removes the given display name from the ignore list of the class.
removeIncrementalClassifierListener(IncrementalClassifierListener) - Method in class weka.gui.beans.Classifier: Remove an incremental classifier listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.AbstractDataSource: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.ClassAssigner
removeInstanceListener(InstanceListener) - Method in interface weka.gui.beans.DataSource: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.Filter: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.InstanceStreamToBatchMaker
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.Loader: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.PredictionAppender: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceJoiner
removeInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceLoader
removeInstanceListener(InstanceListener) - Method in interface weka.gui.streams.InstanceProducer
removeLast() - Method in class weka.classifiers.rules.RuleStats: Remove the last rule in the ruleset as well as it's stats.
removeLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Method to remove a LayoutCompleteEventListener.
removeLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method removes a LayoutCompleteEventListener from the LayoutEngine.
removeLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2: removes an element (Link) at a specific index from the list.
removeLinkAt(int) - Method in class weka.attributeSelection.LFSMethods.LinkedList2: removes an element (Link) at a specific index from the list.
RemoveMisclassified - Class in weka.filters.unsupervised.instance: A filter that removes instances which are incorrectly classified.
RemoveMisclassified() - Constructor for class weka.filters.unsupervised.instance.RemoveMisclassified
removeNotify() - Method in class weka.gui.PropertyPanel: Cleans up when the panel is destroyed.
removeOkListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to remove an action listener from the ok button.
removeOldClassTipText() - Method in class weka.filters.supervised.attribute.AddClassification: Returns the tip text for this property.
RemovePercentage - Class in weka.filters.unsupervised.instance: A filter that removes a given percentage of a dataset.
RemovePercentage() - Constructor for class weka.filters.unsupervised.instance.RemovePercentage
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.AbstractDataSource: Remove a property change listener from this bean
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.AssociatorCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.BeanVisual: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassAssignerCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassifierCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassValuePickerCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClustererCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer: Remove a property change listener
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.DataVisualizer: Remove a property change listener from this bean
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.FilterCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.LoaderCustomizer: Remove a property change listener
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.ModelPerformanceChart: Remove a property change listener from this bean
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.PredictionAppenderCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.SaverCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.SerializedModelSaverCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.StripChartCustomizer: Remove a property change listener
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.TextViewer: Remove a property change listener from this bean
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.CostMatrixEditor: Removes an object from the list of those that wish to be informed when the cost matrix changes.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SetupPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SimpleSetupPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericArrayEditor: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericObjectEditor: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.PropertySheetPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SetInstancesPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SimpleDateFormatEditor: Removes an object from the list of those that wish to be informed when the date format changes.
removePropertyChangeListenersSubFlow(PropertyChangeListener) - Method in class weka.gui.beans.MetaBean
removeQueryExecuteListener(QueryExecuteListener) - Method in class weka.gui.sql.QueryPanel: removes the given listener from the list of listeners.
removeQueryExecuteListener(QueryExecuteListener) - Method in class weka.gui.sql.SqlViewer: removes the given listener from the list of listeners.
RemoveRange - Class in weka.filters.unsupervised.instance: A filter that removes a given range of instances of a dataset.
RemoveRange() - Constructor for class weka.filters.unsupervised.instance.RemoveRange
removeRedundant(RuleItem) - Method in class weka.associations.RuleGeneration: Method that removes redundant rules out of the list of the best rules.
removeResult(String) - Method in class weka.gui.ResultHistoryPanel: Removes one of the result buffers from the history.
removeResultChangedListener(ResultChangedListener) - Method in class weka.gui.sql.ResultPanel: removes the given listener from the list of listeners
removeResultChangedListener(ResultChangedListener) - Method in class weka.gui.sql.SqlViewer: removes the given listener from the list of listeners.
removeSubstring(String, String) - Static method in class weka.core.Utils: Removes all occurrences of a string from another string.
removeTableModelListener(TableModelListener) - Method in class weka.gui.arffviewer.ArffSortedTableModel: removes a listener from the list that is notified each time a change to the data model occurs
removeTableModelListener(TableModelListener) - Method in class weka.gui.arffviewer.ArffTableModel: removes a listener from the list that is notified each time a change to the data model occurs
removeTableModelListener(TableModelListener) - Method in class weka.gui.sql.ResultSetTableModel: removes a listener from the list that is notified each time a change to the data model occurs.
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTestSetProducer: Remove a listener for test sets
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Remove a test set listener
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.ClassAssigner
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.Filter: Remove a test set listener
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.PredictionAppender: Remove a test set listener
removeTestSetListener(TestSetListener) - Method in interface weka.gui.beans.TestSetProducer: Remove a listener for test set events
removeTextListener(TextListener) - Method in class weka.gui.beans.Associator: Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.Classifier: Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.Clusterer: Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.TextViewer: Remove a text listener
removeThresholdDataListener(ThresholdDataListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Remove a Threshold data listener
removeTrainingInstanceFromMouseLocation(int, int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Removes a single training instance from our dataset, if there is one that is close enough to the specified mouse location.
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.ClassAssigner
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.Filter: Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.PredictionAppender: Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in interface weka.gui.beans.TrainingSetProducer: Remove a training set listener
RemoveType - Class in weka.filters.unsupervised.attribute: Removes attributes of a given type.
RemoveType() - Constructor for class weka.filters.unsupervised.attribute.RemoveType
removeUnusedTipText() - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Returns the tip text for this property.
RemoveUseless - Class in weka.filters.unsupervised.attribute: This filter removes attributes that do not vary at all or that vary too much.
RemoveUseless() - Constructor for class weka.filters.unsupervised.attribute.RemoveUseless
removeVariable(String) - Method in class weka.core.Environment: Remove a named variable from the map.
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.AbstractDataSource: Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.CostBenefitAnalysis
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.DataVisualizer: Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.GraphViewer: Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.ModelPerformanceChart: Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.TextViewer: Remove a vetoable change listener from this bean
removeVisualizableErrorListener(VisualizableErrorListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Remove a visualizable error listener
RemoveWithValues - Class in weka.filters.unsupervised.instance: Filters instances according to the value of an attribute.
RemoveWithValues() - Constructor for class weka.filters.unsupervised.instance.RemoveWithValues: Default constructor
renameAttribute(int, String) - Method in class weka.core.Instances: Renames an attribute.
renameAttribute(Attribute, String) - Method in class weka.core.Instances: Renames an attribute.
renameAttribute() - Method in class weka.gui.arffviewer.ArffPanel: renames the current attribute
renameAttribute() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: renames the current selected Attribute
renameAttributeAt(int, String) - Method in class weka.gui.arffviewer.ArffSortedTableModel: renames the attribute at the given col index
renameAttributeAt(int, String) - Method in class weka.gui.arffviewer.ArffTableModel: renames the attribute at the given col index
renameAttributeValue(int, int, String) - Method in class weka.core.Instances: Renames the value of a nominal (or string) attribute value.
renameAttributeValue(Attribute, String, String) - Method in class weka.core.Instances: Renames the value of a nominal (or string) attribute value.
renameNodeValue(int, String, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: change the name of a value of a node
Reorder - Class in weka.filters.unsupervised.attribute: A filter that generates output with a new order of the attributes.
Reorder() - Constructor for class weka.filters.unsupervised.attribute.Reorder
RepeatedHillClimber - Class in weka.classifiers.bayes.net.search.global: This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
RepeatedHillClimber() - Constructor for class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
RepeatedHillClimber - Class in weka.classifiers.bayes.net.search.local: This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
RepeatedHillClimber() - Constructor for class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
repeatLiteralsTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
replaceAllBy(Stack<T>) - Method in class weka.core.neighboursearch.covertrees.Stack: Replace all elements in the stack with the elements of another given stack.
replaceMissingTipText() - Method in class weka.filters.supervised.attribute.PLSFilter: Returns the tip text for this property
replaceMissingValues(double[]) - Method in class weka.core.BinarySparseInstance: Does nothing, since we don't support missing values.
replaceMissingValues(double[]) - Method in class weka.core.Instance: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class weka.core.SparseInstance: Replaces all missing values in the instance with the values contained in the given array.
ReplaceMissingValues - Class in weka.filters.unsupervised.attribute: Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
ReplaceMissingValues() - Constructor for class weka.filters.unsupervised.attribute.ReplaceMissingValues
replaceMissingValuesTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
replaceMissingWithMAX_VALUE(double[]) - Static method in class weka.core.Utils: Replaces all "missing values" in the given array of double values with MAX_VALUE.
replaceSubstring(String, String, String) - Static method in class weka.core.Utils: Replaces with a new string, all occurrences of a string from another string.
replot() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Quickly replot the display using cached probability estimates
reportFrequencyTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
REPTree - Class in weka.classifiers.trees: Fast decision tree learner.
REPTree() - Constructor for class weka.classifiers.trees.REPTree
repulsionTipText() - Method in class weka.clusterers.CLOPE: Returns the tip text for this property
resample(Random) - Method in class weka.core.Instances: Creates a new dataset of the same size using random sampling with replacement.
Resample - Class in weka.filters.supervised.instance: Produces a random subsample of a dataset using either sampling with replacement or without replacement.
The original dataset must fit entirely in memory.
Resample() - Constructor for class weka.filters.supervised.instance.Resample
Resample - Class in weka.filters.unsupervised.instance: Produces a random subsample of a dataset using either sampling with replacement or without replacement.
Resample() - Constructor for class weka.filters.unsupervised.instance.Resample
resampleWithWeights(Random) - Method in class weka.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Random, boolean[]) - Method in class weka.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Random, double[]) - Method in class weka.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
resampleWithWeights(Random, double[], boolean[]) - Method in class weka.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
ReservoirSample - Class in weka.filters.unsupervised.instance: Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
ReservoirSample() - Constructor for class weka.filters.unsupervised.instance.ReservoirSample
reset() - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to reset the unit for another run.
reset() - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to reset the value and error for this unit, ready for the next run.
reset() - Method in class weka.classifiers.functions.SPegasos: Reset the classifier.
reset() - Method in class weka.core.converters.AbstractFileLoader: Resets the loader ready to read a new data set
reset() - Method in class weka.core.converters.AbstractLoader: Default implementation sets retrieval mode to NONE
reset() - Method in class weka.core.converters.ArffLoader: Resets the Loader ready to read a new data set or the same data set again.
reset() - Method in class weka.core.converters.C45Loader: Resets the Loader ready to read a new data set or the same data set again.
reset() - Method in class weka.core.converters.ConverterUtils.DataSource: resets the loader.
reset() - Method in class weka.core.converters.CSVLoader: Resets the Loader ready to read a new data set or the same data set again.
reset() - Method in class weka.core.converters.DatabaseLoader: Resets the Loader ready to read a new data set
reset() - Method in class weka.core.converters.LibSVMLoader: Resets the Loader ready to read a new data set.
reset() - Method in interface weka.core.converters.Loader: Resets the Loader object ready to begin loading.
reset() - Method in class weka.core.converters.SerializedInstancesLoader: Resets the Loader ready to read a new data set
reset() - Method in class weka.core.converters.SVMLightLoader: Resets the Loader ready to read a new data set.
reset() - Method in class weka.core.converters.TextDirectoryLoader: Resets the loader ready to read a new data set
reset() - Method in class weka.core.converters.XRFFLoader: Resets the Loader ready to read a new data set
reset() - Method in class weka.core.neighboursearch.PerformanceStats: Resets all internal fields/counters.
reset() - Method in class weka.core.neighboursearch.TreePerformanceStats: Resets all internal fields/counters.
reset() - Static method in class weka.gui.beans.BeanConnection: Reset the list of connections
reset(JComponent) - Static method in class weka.gui.beans.BeanInstance: Reset the list of beans
resetAttIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Resets the boolean value in AttIndexes array
resetAttIndexTo(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR.Indexes: Resets the boolean value in AttIndexes array based on another set of Indexes
resetDatasetBasedOn(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR.Indexes: Resets the boolean values in Attribute and Instance array to reflect an empty dataset withthe same attributes set as in the incoming Indexes Object
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Sets distribution associated with model.
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.C45Split: Sets distribution associated with model.
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Sets distribution associated with model.
resetInstanceIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Resets the boolean value in the Instance Indexes array to a specified value
resetOptions() - Method in class weka.associations.Apriori: Resets the options to the default values.
resetOptions() - Method in class weka.associations.FPGrowth: Reset all options to their default values.
resetOptions() - Method in class weka.associations.PredictiveApriori: Resets the options to the default values.
resetOptions() - Method in class weka.associations.Tertius: Resets the options to the default values.
resetOptions() - Method in class weka.core.converters.AbstractFileSaver: resets the options
resetOptions() - Method in class weka.core.converters.AbstractSaver: resets the options
resetOptions() - Method in class weka.core.converters.ArffSaver: Resets the Saver
resetOptions() - Method in class weka.core.converters.C45Saver: Resets the Saver
resetOptions() - Method in class weka.core.converters.CSVSaver: Resets the Saver
resetOptions() - Method in class weka.core.converters.DatabaseSaver: Resets the Saver ready to save a new data set.
resetOptions() - Method in class weka.core.converters.LibSVMSaver: Resets the Saver
resetOptions() - Method in class weka.core.converters.SerializedInstancesSaver: Resets the Saver.
resetOptions() - Method in class weka.core.converters.SVMLightSaver: Resets the Saver.
resetOptions() - Method in class weka.core.converters.XRFFSaver: Resets the Saver
resetStructure() - Method in class weka.core.converters.AbstractSaver: Resets the structure (header information of the instances)
resetStructure() - Method in class weka.core.converters.DatabaseLoader: Resets the structure of instances
resetTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
resetWriter() - Method in class weka.core.converters.AbstractFileSaver: Sets the writer to null.
resetWriter() - Method in class weka.core.converters.SerializedInstancesSaver: Resets the writer, setting writer and objectstream to null.
ResidualModelSelection - Class in weka.classifiers.trees.lmt: Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals.
ResidualModelSelection(int) - Constructor for class weka.classifiers.trees.lmt.ResidualModelSelection: Constructor to create ResidualModelSelection object.
ResidualSplit - Class in weka.classifiers.trees.lmt: Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals of the LogitBoost algorithm.
ResidualSplit(int) - Constructor for class weka.classifiers.trees.lmt.ResidualSplit: Creates a split object
restoreBeans() - Method in class weka.gui.beans.MetaBean
restoreWeights() - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to have the connection restore from the saved weights.
restoreWeights() - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to have the connection restore from the saved weights.
restoreWindows() - Method in class weka.gui.Main: restores all windows.
resultChanged(ResultChangedEvent) - Method in interface weka.gui.sql.event.ResultChangedListener: This method gets called when a query has been executed.
resultChanged(ResultChangedEvent) - Method in class weka.gui.sql.SqlViewer: This method gets called when a query has been executed.
resultChanged(ResultChangedEvent) - Method in class weka.gui.sql.SqlViewerDialog: This method gets called when a query has been executed.
ResultChangedEvent - Class in weka.gui.sql.event: An event that is generated when a different Result is activated in the ResultPanel.
ResultChangedEvent(Object, String, String, String, String) - Constructor for class weka.gui.sql.event.ResultChangedEvent: constructs the event
ResultChangedListener - Interface in weka.gui.sql.event: A listener that is notified if another Result is activated in the ResultPanel.
ResultHistoryPanel - Class in weka.gui: A component that accepts named stringbuffers and displays the name in a list box.
ResultHistoryPanel(JTextComponent) - Constructor for class weka.gui.ResultHistoryPanel: Create the result history object
ResultHistoryPanel.RKeyAdapter - Class in weka.gui: Extension of KeyAdapter that implements Serializable.
ResultHistoryPanel.RMouseAdapter - Class in weka.gui: Extension of MouseAdapter that implements Serializable.
ResultListener - Interface in weka.experiment: Interface for objects able to listen for results obtained by a ResultProducer
ResultMatrix - Class in weka.experiment: This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrix() - Constructor for class weka.experiment.ResultMatrix: initializes the matrix as 1x1 matrix
ResultMatrix(int, int) - Constructor for class weka.experiment.ResultMatrix: initializes the matrix with the given dimensions
ResultMatrix(ResultMatrix) - Constructor for class weka.experiment.ResultMatrix: initializes the matrix with the values from the given matrix
ResultMatrixCSV - Class in weka.experiment: This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixCSV() - Constructor for class weka.experiment.ResultMatrixCSV: initializes the matrix as 1x1 matrix
ResultMatrixCSV(int, int) - Constructor for class weka.experiment.ResultMatrixCSV: initializes the matrix with the given dimensions
ResultMatrixCSV(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixCSV: initializes the matrix with the values from the given matrix
ResultMatrixGnuPlot - Class in weka.experiment: This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixGnuPlot() - Constructor for class weka.experiment.ResultMatrixGnuPlot: initializes the matrix as 1x1 matrix
ResultMatrixGnuPlot(int, int) - Constructor for class weka.experiment.ResultMatrixGnuPlot: initializes the matrix with the given dimensions
ResultMatrixGnuPlot(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixGnuPlot: initializes the matrix with the values from the given matrix
ResultMatrixHTML - Class in weka.experiment: This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixHTML() - Constructor for class weka.experiment.ResultMatrixHTML: initializes the matrix as 1x1 matrix
ResultMatrixHTML(int, int) - Constructor for class weka.experiment.ResultMatrixHTML: initializes the matrix with the given dimensions
ResultMatrixHTML(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixHTML: initializes the matrix with the values from the given matrix
ResultMatrixLatex - Class in weka.experiment: This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixLatex() - Constructor for class weka.experiment.ResultMatrixLatex: initializes the matrix as 1x1 matrix
ResultMatrixLatex(int, int) - Constructor for class weka.experiment.ResultMatrixLatex: initializes the matrix with the given dimensions
ResultMatrixLatex(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixLatex: initializes the matrix with the values from the given matrix
ResultMatrixPlainText - Class in weka.experiment: This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixPlainText() - Constructor for class weka.experiment.ResultMatrixPlainText: initializes the matrix as 1x1 matrix
ResultMatrixPlainText(int, int) - Constructor for class weka.experiment.ResultMatrixPlainText: initializes the matrix with the given dimensions
ResultMatrixPlainText(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixPlainText: initializes the matrix with the values from the given matrix
ResultMatrixSignificance - Class in weka.experiment: This matrix is a container for the datasets and classifier setups and their statistics.
ResultMatrixSignificance() - Constructor for class weka.experiment.ResultMatrixSignificance: initializes the matrix as 1x1 matrix
ResultMatrixSignificance(int, int) - Constructor for class weka.experiment.ResultMatrixSignificance: initializes the matrix with the given dimensions
ResultMatrixSignificance(ResultMatrix) - Constructor for class weka.experiment.ResultMatrixSignificance: initializes the matrix with the values from the given matrix
ResultPanel - Class in weka.gui.sql: Represents a panel for displaying the results of a query in table format.
ResultPanel(JFrame) - Constructor for class weka.gui.sql.ResultPanel: initializes the panel
ResultProducer - Interface in weka.experiment: This interface defines the methods required for an object that produces results for different randomizations of a dataset.
resultProducerTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
resultProducerTipText() - Method in class weka.experiment.DatabaseResultProducer: Returns the tip text for this property
resultProducerTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
ResultSetHelper - Class in weka.gui.sql: Represents an extended JTable, containing a table model based on a ResultSet and the corresponding query.
ResultSetHelper(ResultSet) - Constructor for class weka.gui.sql.ResultSetHelper: initializes the helper, with unlimited number of rows.
ResultSetHelper(ResultSet, int) - Constructor for class weka.gui.sql.ResultSetHelper: initializes the helper, with the given maximum number of rows (less than 1 means unlimited).
resultsetKey() - Method in class weka.experiment.PairedTTester: Creates a key that maps resultset numbers to their descriptions.
resultsetKey() - Method in interface weka.experiment.Tester: Creates a key that maps resultset numbers to their descriptions.
ResultSetTable - Class in weka.gui.sql: Represents an extended JTable, containing a table model based on a ResultSet and the corresponding query.
ResultSetTable(String, String, String, String, ResultSetTableModel) - Constructor for class weka.gui.sql.ResultSetTable: initializes the table
ResultSetTableCellRenderer - Class in weka.gui.sql: Handles the background colors for missing values differently than the DefaultTableCellRenderer.
ResultSetTableCellRenderer() - Constructor for class weka.gui.sql.ResultSetTableCellRenderer: initializes the Renderer with a standard color
ResultSetTableCellRenderer(Color, Color) - Constructor for class weka.gui.sql.ResultSetTableCellRenderer: initializes the Renderer with the given colors
ResultSetTableModel - Class in weka.gui.sql: The model for an SQL ResultSet.
ResultSetTableModel(ResultSet) - Constructor for class weka.gui.sql.ResultSetTableModel: initializes the model, retrieves all rows.
ResultSetTableModel(ResultSet, int) - Constructor for class weka.gui.sql.ResultSetTableModel: initializes the model, retrieves only the given amount of rows (0 means all).
ResultsPanel - Class in weka.gui.experiment: This panel controls simple analysis of experimental results.
ResultsPanel() - Constructor for class weka.gui.experiment.ResultsPanel: Creates the results panel with no initial experiment.
ResultVectorTableModel - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI: ResultVectorTableModel.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 12, 2004
Time: 9:23:31 PM
$ Revision 1.4 $
ResultVectorTableModel(FastVector) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.ResultVectorTableModel: Constructs a default DefaultTableModel which is a table of zero columns and zero rows.
retainAll(Collection<?>) - Method in class weka.core.Trie: Retains only the elements in this collection that are contained in the specified collection
retrieveDir() - Method in class weka.core.converters.AbstractFileSaver: Gets the directory
retrieveDir() - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
retrieveDir() - Method in interface weka.core.converters.Saver: Gets the driectory of the output file This method is used in the KnowledgeFlow GUI.
retrieveFile() - Method in class weka.core.converters.AbstractFileLoader: get the File specified as the source
retrieveFile() - Method in class weka.core.converters.AbstractFileSaver: Gets the destination file.
retrieveFile() - Method in class weka.core.converters.ArffLoader: get the File specified as the source
retrieveFile() - Method in interface weka.core.converters.FileSourcedConverter: Return the current source file/ destination file
retrieveInstances() - Method in class weka.experiment.InstanceQuery: Makes a database query using the query set through the -Q option to convert a table into a set of instances
retrieveInstances(String) - Method in class weka.experiment.InstanceQuery: Makes a database query to convert a table into a set of instances
retrieveURL() - Method in class weka.core.converters.ArffLoader: Return the current url
retrieveURL() - Method in class weka.core.converters.LibSVMLoader: Return the current url.
retrieveURL() - Method in class weka.core.converters.SVMLightLoader: Return the current url.
retrieveURL() - Method in interface weka.core.converters.URLSourcedLoader: Return the current url
retrieveURL() - Method in class weka.core.converters.XRFFLoader: Return the current url
returnLeaves(FastVector[]) - Method in class weka.classifiers.trees.m5.RuleNode: Return a list containing all the leaves in the tree
rev() - Method in class weka.core.matrix.DoubleVector: Returns the reverse vector
REVERSED - Static variable in interface weka.gui.graphvisualizer.GraphConstants: Types of Edges
reversedArcs(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader: Count nr of reversed arcs from other network compared to current network
revertNewLines(String) - Static method in class weka.core.Utils: Reverts \r and \n in a string into carriage returns and new lines.
REVISION - Static variable in class weka.core.Version: the revision
RevisionHandler - Interface in weka.core: For classes that should return their source control revision.
RevisionUtils - Class in weka.core: Contains utility functions for handling revisions.
RevisionUtils() - Constructor for class weka.core.RevisionUtils
RevisionUtils.Type - Enum in weka.core: Enumeration of source control types.
ridgeTipText() - Method in class weka.classifiers.functions.LinearRegression: Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.functions.Logistic: Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.mi.MILR: Returns the tip text for this property
Ridor - Class in weka.classifiers.rules: An implementation of a RIpple-DOwn Rule learner.

It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate.
Ridor() - Constructor for class weka.classifiers.rules.Ridor
RIGHT_PARENTHESES - Variable in class weka.experiment.ResultMatrix: the right parentheses for enumerating cols/rows
rightNode() - Method in class weka.classifiers.trees.m5.RuleNode: Get the right child of this node
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split: Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Prints left side of condition satisfied by instances in subset index.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.GraftSplit: Prints condition satisfied by instances in subset index.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Does nothing because no condition has to be satisfied.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit: Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit: Does nothing because no condition has to be satisfied.
rightSide(int, Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Prints the condition satisfied by instances in a subset.
RINT - Static variable in interface weka.core.mathematicalexpression.sym
RINT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
RipperRule() - Constructor for class weka.classifiers.rules.JRip.RipperRule: Constructor
RKeyAdapter() - Constructor for class weka.gui.ResultHistoryPanel.RKeyAdapter
rmCoveredBySuccessives(Instances, FastVector, int) - Static method in class weka.classifiers.rules.RuleStats: Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them.
RMouseAdapter() - Constructor for class weka.gui.ResultHistoryPanel.RMouseAdapter
rnorm(int, double, double, Random) - Static method in class weka.core.matrix.Maths: Generates a sample of a normal distribution.
rocAnalysisTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
rocToString() - Method in class weka.associations.tertius.Rule: Return a String giving the TP-rate and FP-rate of this rule.
ROOT_FINDER_ACCURACY - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
ROOT_FINDER_MAX_ITER - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants: How close the root finder for numeric and nominal have to get
ROOT_NODE - Static variable in class weka.core.xml.XMLOptions: the root node.
ROOT_NODE - Static variable in class weka.core.xml.XMLSerialization: the root node of the XML document
rootMeanPriorSquaredError() - Method in class weka.classifiers.Evaluation: Returns the root mean prior squared error.
rootMeanSquaredError() - Method in class weka.classifiers.Evaluation: Returns the root mean squared error.
rootRelativeSquaredError() - Method in class weka.classifiers.Evaluation: Returns the root relative squared error if the class is numeric.
rotate(double, double, double) - Method in class weka.gui.visualize.PostscriptGraphics
rotate(double) - Method in class weka.gui.visualize.PostscriptGraphics
RotationForest - Class in weka.classifiers.meta: Class for construction a Rotation Forest.
RotationForest() - Constructor for class weka.classifiers.meta.RotationForest: Constructor.
round(double) - Static method in class weka.core.Utils: Rounds a double to the next nearest integer value.
roundDouble(double, int) - Static method in class weka.core.Utils: Rounds a double to the given number of decimal places.
RPAREN - Static variable in interface weka.core.mathematicalexpression.sym
RPAREN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
rsolve(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Solves upper-triangular equation
R x = b
On output, the solution is stored in b
Rule - Class in weka.associations.tertius: Class representing a rule with a body and a head.
Rule(boolean, int, boolean, boolean, boolean, boolean) - Constructor for class weka.associations.tertius.Rule: Constructor for a rule when the counter-instances are not stored, giving all the constraints applied to this rule.
Rule(Instances, boolean, int, boolean, boolean, boolean, boolean) - Constructor for class weka.associations.tertius.Rule: Constructor for a rule when the counter-instances are stored, giving all the constraints applied to this rule.
Rule - Class in weka.classifiers.rules: Abstract class of generic rule
Rule() - Constructor for class weka.classifiers.rules.Rule
Rule - Class in weka.classifiers.trees.m5: Generates a single m5 tree or rule
Rule() - Constructor for class weka.classifiers.trees.m5.Rule: Constructor declaration
RuleGeneration - Class in weka.associations: Class implementing the rule generation procedure of the predictive apriori algorithm.
RuleGeneration(ItemSet) - Constructor for class weka.associations.RuleGeneration: Constructor
RuleItem - Class in weka.associations: Class for storing an (class) association rule.
RuleItem() - Constructor for class weka.associations.RuleItem: Constructor for an empty RuleItem
RuleItem(RuleItem) - Constructor for class weka.associations.RuleItem: Constructor that generates a RuleItem out of a given one
RuleItem(ItemSet, ItemSet, int, int, double[], Hashtable) - Constructor for class weka.associations.RuleItem: Constructor
RuleNode - Class in weka.classifiers.trees.m5: Constructs a node for use in an m5 tree or rule
RuleNode(double, double, RuleNode) - Constructor for class weka.classifiers.trees.m5.RuleNode: Creates a new RuleNode instance.
rulesMustContainTipText() - Method in class weka.associations.FPGrowth: Returns the tip text for this property
RuleStats - Class in weka.classifiers.rules: This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc.
RuleStats() - Constructor for class weka.classifiers.rules.RuleStats: Default constructor
RuleStats(Instances, FastVector) - Constructor for class weka.classifiers.rules.RuleStats: Constructor that provides ruleset and data
run() - Method in class weka.associations.Tertius: Run the search.
run() - Method in class weka.gui.beans.FlowRunner: Launch all loaded KnowledgeFlow
RUN_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer: The name of the key field containing the run number
RUN_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer: The name of the key field containing the run number
runCommand(String) - Method in class weka.gui.SimpleCLIPanel: Executes a simple cli command.
runExperiment() - Method in class weka.experiment.Experiment: Runs all iterations of the experiment, continuing past errors.
runExperiment() - Method in class weka.experiment.RemoteExperiment: Overides runExperiment in Experiment
runFileLoader(AbstractFileLoader, String[]) - Static method in class weka.core.converters.AbstractFileLoader: runs the given loader with the provided options
runFileSaver(AbstractFileSaver, String[]) - Static method in class weka.core.converters.AbstractFileSaver: runs the given saver with the specified options
RunNumberPanel - Class in weka.gui.experiment: This panel controls configuration of lower and upper run numbers in an experiment.
RunNumberPanel() - Constructor for class weka.gui.experiment.RunNumberPanel: Creates the panel with no initial experiment.
RunNumberPanel(Experiment) - Constructor for class weka.gui.experiment.RunNumberPanel: Creates the panel with the supplied initial experiment.
RunPanel - Class in weka.gui.experiment: This panel controls the running of an experiment.
RunPanel() - Constructor for class weka.gui.experiment.RunPanel: Creates the run panel with no initial experiment.
RunPanel(Experiment) - Constructor for class weka.gui.experiment.RunPanel: Creates the panel with the supplied initial experiment.
runsTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
runsTipText() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
runsTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
runsTipText() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
runsTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
runsTipText() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
runsTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
runsTipText() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
runTokenizer(Tokenizer, String[]) - Static method in class weka.core.tokenizers.Tokenizer: initializes the given tokenizer with the given options and runs the tokenizer over all the remaining strings in the options array.

S

s_fileFormatsAvailable - Static variable in class weka.gui.beans.SerializedModelSaver: Available file formats.
s_startupListeners - Static variable in class weka.gui.beans.KnowledgeFlowApp
sameClauseAs(Rule) - Method in class weka.associations.tertius.Rule: Test if this rule and another rule correspond to the same clause.
sameClauseTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
SAMPLE_SIZE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: Sample Size
sampleSizePercentTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
sampleSizePercentTipText() - Method in class weka.filters.supervised.instance.Resample: Returns the tip text for this property.
sampleSizePercentTipText() - Method in class weka.filters.unsupervised.instance.Resample: Returns the tip text for this property
sampleSizeTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
sampleSizeTipText() - Method in class weka.classifiers.functions.LeastMedSq: Returns the tip text for this property
sampleSizeTipText() - Method in class weka.filters.unsupervised.instance.ReservoirSample: Returns the tip text for this property
satisfies(Instance) - Method in class weka.associations.tertius.AttributeValueLiteral
satisfies(Instance) - Method in class weka.associations.tertius.Literal
save(StringBuffer) - Method in class weka.gui.SaveBuffer: Save a buffer
saveBatch() - Method in class weka.gui.beans.Saver: Saves instances in batch mode
saveBinary(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver: Save a model in binary form.
SaveBuffer - Class in weka.gui: This class handles the saving of StringBuffers to files.
SaveBuffer(Logger, Component) - Constructor for class weka.gui.SaveBuffer: Constructor
saveComponent() - Method in class weka.gui.visualize.PrintableComponent: displays a save dialog for saving the panel to a file.
saveComponent() - Method in interface weka.gui.visualize.PrintableHandler: displays a save dialog for saving the component to a file.
saveComponent() - Method in class weka.gui.visualize.PrintablePanel: displays a save dialog for saving the panel to a file.
saveFile() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: saves the current data into a file
saveFileAs() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: saves the current data into a new file
saveInstanceDataTipText() - Method in class weka.classifiers.trees.ADTree
saveInstanceDataTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
saveInstanceDataTipText() - Method in class weka.classifiers.trees.J48graft: Returns the tip text for this property
saveInstanceDataTipText() - Method in class weka.clusterers.Cobweb: Returns the tip text for this property
saveInstancesTipText() - Method in class weka.classifiers.trees.M5P: Returns the tip text for this property
saveInstancesToFile(AbstractFileSaver, Instances) - Method in class weka.gui.explorer.PreprocessPanel: saves the data with the specified saver
saveKOML(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver: Save a model in KOML deep object serialized XML form.
saveLayout(OutputStream) - Method in class weka.gui.beans.KnowledgeFlowApp: Save the knowledge flow into the OutputStream passed at input.
saveModel() - Method in class weka.gui.beans.Classifier
saveModel() - Method in class weka.gui.beans.Clusterer
Saver - Interface in weka.core.converters: Interface to something that can save Instances to an output destination in some format.
Saver - Class in weka.gui.beans: Saves data sets using weka.core.converter classes
Saver() - Constructor for class weka.gui.beans.Saver: Contsructor
SAVER_DIALOG - Static variable in class weka.gui.ConverterFileChooser: the saver dialog
SaverBeanInfo - Class in weka.gui.beans: Bean info class for the saver bean
SaverBeanInfo() - Constructor for class weka.gui.beans.SaverBeanInfo
SaverCustomizer - Class in weka.gui.beans: GUI Customizer for the saver bean
SaverCustomizer() - Constructor for class weka.gui.beans.SaverCustomizer: Constructor
saveSize() - Method in class weka.gui.sql.SqlViewer: obtains the size of the panel and saves it in the history.
saveToFile(String, Object) - Static method in class weka.core.Debug: writes the serialized object to the speicified file
saveWeights() - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to have the connection save the current weights.
saveWeights() - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to have the connection save the current weights.
saveWorkingInstancesToFileQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a file to save instances as, then saves the instances in a background process.
saveXStream(File, Object, Instances) - Static method in class weka.gui.beans.SerializedModelSaver: Save a model in XStream deep object serialized XML form.
scalarMultiply(double) - Method in class weka.core.AlgVector: Computes the scalar product of this vector with a scalar
scale(int) - Method in class weka.gui.beans.BeanVisual: Reduce this BeanVisual's icon size by the given factor
scale(double, double) - Method in class weka.gui.visualize.PostscriptGraphics
scaleTipText() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns the tip text for this property.
Scanner - Class in weka.core.mathematicalexpression: A scanner for mathematical expressions.
Scanner(InputStream, SymbolFactory) - Constructor for class weka.core.mathematicalexpression.Scanner
Scanner(Reader) - Constructor for class weka.core.mathematicalexpression.Scanner: Creates a new scanner There is also a java.io.InputStream version of this constructor.
Scanner(InputStream) - Constructor for class weka.core.mathematicalexpression.Scanner: Creates a new scanner.
Scanner - Class in weka.filters.unsupervised.instance.subsetbyexpression: A scanner for evaluating whether an Instance is to be included in a subset or not.
Scanner(InputStream, SymbolFactory) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
Scanner(Reader) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Creates a new scanner There is also a java.io.InputStream version of this constructor.
Scanner(InputStream) - Constructor for class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Creates a new scanner.
ScatterPlotMatrix - Class in weka.gui.beans: Bean that encapsulates weka.gui.visualize.MatrixPanel for displaying a scatter plot matrix.
ScatterPlotMatrix() - Constructor for class weka.gui.beans.ScatterPlotMatrix
ScatterPlotMatrixBeanInfo - Class in weka.gui.beans: Bean info class for the scatter plot matrix bean
ScatterPlotMatrixBeanInfo() - Constructor for class weka.gui.beans.ScatterPlotMatrixBeanInfo
ScatterSearchV1 - Class in weka.attributeSelection: Class for performing the Sequential Scatter Search.
ScatterSearchV1() - Constructor for class weka.attributeSelection.ScatterSearchV1
ScatterSearchV1.Subset - Class in weka.attributeSelection
Scoreable - Interface in weka.classifiers.bayes.net.search.local: Interface for allowing to score a classifier
scoreTypeTipText() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
scrollToVisible(int, int) - Method in class weka.gui.JTableHelper: Assumes table is contained in a JScrollPane.
scrollToVisible(JTable, int, int) - Static method in class weka.gui.JTableHelper: Assumes table is contained in a JScrollPane.
search() - Method in class weka.associations.Tertius: Search in the space of hypotheses the rules that have the highest confirmation.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ASSearch: Searches the attribute subset/ranking space.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.BestFirst: Searches the attribute subset space by best first search
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ExhaustiveSearch: Searches the attribute subset space using an exhaustive search.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.GeneticSearch: Searches the attribute subset space using a genetic algorithm.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.GreedyStepwise: Searches the attribute subset space by forward selection.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.LinearForwardSelection: Searches the attribute subset space by linear forward selection
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RaceSearch: Searches the attribute subset space by racing cross validation errors of competing subsets
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RandomSearch: Searches the attribute subset space randomly.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.Ranker: Kind of a dummy search algorithm.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RankSearch: Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ScatterSearchV1: Searches the attribute subset space using Scatter Search.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Searches the attribute subset space by subset size forward selection
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.K2: search determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.K2: search determines the network structure/graph of the network with the K2 algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
search(BayesNet, Instances) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
search() - Method in class weka.gui.arffviewer.ArffPanel: searches for a string in the cells
search() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: searches for a string in the cells
search(Vector, String) - Method in class weka.gui.HierarchyPropertyParser: Helper function to search for the given target string in a given vector in which the elements' value may hopefully is equal to the target.
SearchAlgorithm - Class in weka.classifiers.bayes.net.search: This is the base class for all search algorithms for learning Bayes networks.
SearchAlgorithm() - Constructor for class weka.classifiers.bayes.net.search.SearchAlgorithm: c'tor
searchAlgorithmTipText() - Method in class weka.classifiers.bayes.BayesNet
searchBackwardsTipText() - Method in class weka.attributeSelection.GreedyStepwise: Returns the tip text for this property
searchFinish() - Method in class weka.core.neighboursearch.PerformanceStats: Signals end of the nearest neighbour search.
searchFinish() - Method in class weka.core.neighboursearch.TreePerformanceStats: Signals end of the nearest neighbour search.
SEARCHPATH_ALL - Static variable in class weka.classifiers.trees.ADTree: search mode: Expand all paths
SEARCHPATH_HEAVIEST - Static variable in class weka.classifiers.trees.ADTree: search mode: Expand the heaviest path
SEARCHPATH_RANDOM - Static variable in class weka.classifiers.trees.ADTree: search mode: Expand a random path
SEARCHPATH_ZPURE - Static variable in class weka.classifiers.trees.ADTree: search mode: Expand the best z-pure path
searchPathTipText() - Method in class weka.classifiers.trees.ADTree
searchPercentTipText() - Method in class weka.attributeSelection.RandomSearch: Returns the tip text for this property
searchPoints(int, int, boolean) - Method in class weka.gui.visualize.Plot2D: Pops up a window displaying attribute information on any instances at a point+-plotting_point_size (in panel coordinates)
searchStart() - Method in class weka.core.neighboursearch.PerformanceStats: Signals start of the nearest neighbour search.
searchStart() - Method in class weka.core.neighboursearch.TreePerformanceStats: Signals start of the nearest neighbour search.
searchTerminationTipText() - Method in class weka.attributeSelection.BestFirst: Returns the tip text for this property
searchTerminationTipText() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the tip text for this property
searchTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the tip text for this property
searchTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
searchTipText() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns the tip text for this property
secondInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
secondInstanceProduced(InstanceEvent) - Method in interface weka.gui.streams.SerialInstanceListener
secondValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
secondValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
seedTipText() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string for this option suitable for display in the gui as a tip text
seedTipText() - Method in class weka.attributeSelection.RandomSearch: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.ScatterSearchV1: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
seedTipText() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber
seedTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
seedTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
seedTipText() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber
seedTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
seedTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
seedTipText() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableClassifier: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.RandomForest: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.Cobweb: Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.RandomizableClusterer: Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.RandomizableDensityBasedClusterer: Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.RandomizableSingleClustererEnhancer: Returns the tip text for this property
seedTipText() - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Returns the tip text for this property.
seedTipText() - Method in class weka.datagenerators.DataGenerator: Returns the tip text for this property
seedTipText() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns the tip text for this property
seedTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
seedTipText() - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Returns the tip text for this property
seedTipText() - Method in class weka.filters.unsupervised.attribute.RandomSubset: Returns the tip text for this property.
seedTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
seedTipText() - Method in class weka.gui.beans.CrossValidationFoldMaker: Tip text for this property
seedTipText() - Method in class weka.gui.beans.TrainTestSplitMaker: Tip text for this property
select(int, int[], int, int, int) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Implements computation of the kth-smallest element according to Manber's "Introduction to Algorithms".
select(int, int[], int, int, int) - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension: Implements computation of the kth-smallest element according to Manber's "Introduction to Algorithms".
select(String) - Method in class weka.experiment.DatabaseUtils: Executes a SQL SELECT query that returns a ResultSet.
SelectAttributes(ASEvaluation, String[]) - Static method in class weka.attributeSelection.AttributeSelection: Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.
SelectAttributes(Instances) - Method in class weka.attributeSelection.AttributeSelection: Perform attribute selection on the supplied training instances.
SelectAttributes(ASEvaluation, String[], Instances) - Static method in class weka.attributeSelection.AttributeSelection: Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
selectAttributesCVSplit(Instances) - Method in class weka.attributeSelection.AttributeSelection: Select attributes for a split of the data.
selectedAttributes() - Method in class weka.attributeSelection.AttributeSelection: get the final selected set of attributes.
SelectedTag - Class in weka.core: Represents a selected value from a finite set of values, where each value is a Tag (i.e.
SelectedTag(int, Tag[]) - Constructor for class weka.core.SelectedTag: Creates a new SelectedTag instance.
SelectedTag(String, Tag[]) - Constructor for class weka.core.SelectedTag: Creates a new SelectedTag instance.
SelectedTagEditor - Class in weka.gui: A PropertyEditor that uses tags, where the tags are obtained from a weka.core.SelectedTag object.
SelectedTagEditor() - Constructor for class weka.gui.SelectedTagEditor
SELECTION_GREEDY - Static variable in class weka.classifiers.functions.LinearRegression: Attribute selection method: Greedy method
SELECTION_M5 - Static variable in class weka.classifiers.functions.LinearRegression: Attribute selection method: M5 method
SELECTION_NONE - Static variable in class weka.classifiers.functions.LinearRegression: Attribute selection method: No attribute selection
selectionThresholdTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
selectModel(Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.ModelSelection: Selects a model for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.ModelSelection: Selects a model for the given train data using the given test data
selectModel(Instances) - Method in class weka.classifiers.trees.j48.NBTreeModelSelection: Selects NBTree-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.NBTreeModelSelection: Selects NBTree-type split for the given dataset.
selectModel(Instances, double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Selects split based on residuals for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Method not in use
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Method not in use
SEND_INSTANCES - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent: Command to remove instances from this node and send them to the VisualizePanel.
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Return true if a value can be considered for mixture estimation separately from the data indexed between i0 and i1
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.NormalMixture: Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
seq(int, int) - Static method in class weka.core.matrix.IntVector: Generates an IntVector that stores all integers inclusively between two integers.
Sequence - Class in weka.associations.gsp: Class representing a sequence of elements/itemsets.
Sequence() - Constructor for class weka.associations.gsp.Sequence: Constructor.
Sequence(FastVector) - Constructor for class weka.associations.gsp.Sequence: Constructor accepting a set of elements as parameter.
Sequence(int) - Constructor for class weka.associations.gsp.Sequence: Constructor accepting an int value as parameter to set the support count.
SequentialDatabase - Class in weka.clusterers.forOPTICSAndDBScan.Databases: SequentialDatabase.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 20, 2004
Time: 1:23:38 PM
$ Revision 1.4 $
SequentialDatabase(Instances) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Constructs a new sequential database and holds the original instances
SERFileFilter - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI: SERFileFilter.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 15, 2004
Time: 6:54:56 PM
$ Revision 1.4 $
SERFileFilter(String, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERFileFilter
SERIAL_VERSION_UID - Static variable in class weka.core.SerializationHelper: the field name of serialVersionUID.
SerialInstanceListener - Interface in weka.gui.streams: Defines an interface for objects able to produce two output streams of instances.
SerializationHelper - Class in weka.core: A helper class for determining serialVersionUIDs and checking whether classes contain one and/or need one.
SerializationHelper() - Constructor for class weka.core.SerializationHelper
serialize(Object) - Static method in class weka.core.xml.XStream: Serializes the supplied object xml
SERIALIZED_OBJ_FILE_EXTENSION - Static variable in class weka.core.Instances: The filename extension that should be used for bin.
SerializedClassifier - Class in weka.classifiers.misc: A wrapper around a serialized classifier model.
SerializedClassifier() - Constructor for class weka.classifiers.misc.SerializedClassifier
serializedClassifierFileTipText() - Method in class weka.filters.supervised.attribute.AddClassification: Returns the tip text for this property.
SerializedInstancesLoader - Class in weka.core.converters: Reads a source that contains serialized Instances.
SerializedInstancesLoader() - Constructor for class weka.core.converters.SerializedInstancesLoader
SerializedInstancesSaver - Class in weka.core.converters: Serializes the instances to a file with extension bsi.
SerializedInstancesSaver() - Constructor for class weka.core.converters.SerializedInstancesSaver: Constructor.
SerializedModelSaver - Class in weka.gui.beans: A bean that saves serialized models
SerializedModelSaver() - Constructor for class weka.gui.beans.SerializedModelSaver: Constructor.
SerializedModelSaverBeanInfo - Class in weka.gui.beans: Bean info class for the serialized model saver bean
SerializedModelSaverBeanInfo() - Constructor for class weka.gui.beans.SerializedModelSaverBeanInfo
SerializedModelSaverCustomizer - Class in weka.gui.beans: GUI Customizer for the SerializedModelSaver bean
SerializedModelSaverCustomizer() - Constructor for class weka.gui.beans.SerializedModelSaverCustomizer: Constructor
SerializedObject - Class in weka.core: Class for storing an object in serialized form in memory.
SerializedObject(Object) - Constructor for class weka.core.SerializedObject: Creates a new serialized object (without compression).
SerializedObject(Object, boolean) - Constructor for class weka.core.SerializedObject: Creates a new serialized object.
serializePMMLModel(PMMLModel, String) - Static method in class weka.core.pmml.PMMLFactory: Serialize a PMMLModel object that encapsulates a PMML model
serializePMMLModel(PMMLModel, File) - Static method in class weka.core.pmml.PMMLFactory: Serialize a PMMLModel object that encapsulates a PMML model
serializePMMLModel(PMMLModel, OutputStream) - Static method in class weka.core.pmml.PMMLFactory: Serialize a PMMLModel object that encapsulates a PMML model
SerialUIDChanger - Class in weka.core.xml: This class enables one to change the UID of a serialized object and therefore not losing the data stored in the binary format.
SerialUIDChanger() - Constructor for class weka.core.xml.SerialUIDChanger
SERObject - Class in weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI: SERObject.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht
Date: Sep 15, 2004
Time: 9:43:00 PM
$ Revision 1.4 $
SERObject(FastVector, int, int, double, int, boolean, String, String, int, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.SERObject
set(int, double) - Method in class weka.core.matrix.DoubleVector: Set a single element.
set(double) - Method in class weka.core.matrix.DoubleVector: Set all elements to a value
set(int, int, double) - Method in class weka.core.matrix.DoubleVector: Set some elements to a value
set(int, int, double[], int) - Method in class weka.core.matrix.DoubleVector: Set some elements using a 2-D array
set(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Set the elements using a DoubleVector
set(int, int, DoubleVector, int) - Method in class weka.core.matrix.DoubleVector: Set some elements using a DoubleVector.
set(int) - Method in class weka.core.matrix.IntVector: Sets the value of an element.
set(int, int, int[], int) - Method in class weka.core.matrix.IntVector: Sets the values of elements from an int array.
set(int, int, IntVector, int) - Method in class weka.core.matrix.IntVector: Sets the values of elements from another IntVector.
set(IntVector) - Method in class weka.core.matrix.IntVector: Sets the values of elements from another IntVector.
set(int, int) - Method in class weka.core.matrix.IntVector: Sets a single element.
set(int, int, double) - Method in class weka.core.matrix.Matrix: Set a single element.
set(int, T) - Method in class weka.core.neighboursearch.covertrees.Stack: Sets the ith element in the stack.
set(int, Object) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Replaces the element at the specified position in this list with the specified element.
setAcuity(double) - Method in class weka.clusterers.Cobweb: set the acuity.
setAdditionalMeasures(String[]) - Method in class weka.experiment.AveragingResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator: Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in class weka.experiment.CrossValidationResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DatabaseResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in class weka.experiment.LearningRateResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RandomSplitResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RegressionSplitEvaluator: Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.ResultProducer: Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.SplitEvaluator: Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdjustWeights(boolean) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets whether the instance weights will be adjusted to maintain total weight per class.
setAdvanceDataSetFirst(boolean) - Method in class weka.experiment.Experiment: Set the value of m_AdvanceDataSetFirst.
setAlgorithm(SelectedTag) - Method in class weka.filters.supervised.attribute.PLSFilter: Sets the type of algorithm to use
setAlgorithm(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Wavelet: Sets the type of algorithm to use
setAlgorithmType(SelectedTag) - Method in class weka.classifiers.mi.MILR: Sets the algorithm type.
setAllowUnclassifiedInstances(boolean) - Method in class weka.classifiers.trees.RandomTree: Set the value of AllowUnclassifiedInstances.
setAlpha(double) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Set prior used in probability table estimation
setAlpha(double) - Method in class weka.classifiers.functions.Winnow: Set the value of Alpha.
setAmplitude(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Sets the amplitude multiplier.
setAnimated() - Method in class weka.gui.beans.BeanVisual: Set the animated version of the icon
setAppendPredictedProbabilities(boolean) - Method in class weka.gui.beans.PredictionAppender: Set whether to append predicted probabilities rather than class value (for discrete class data sets)
setAppropriateSize() - Method in class weka.classifiers.bayes.net.GUI: Sets the preferred size for m_GraphPanel GraphPanel to the minimum size that is neccessary to display the graph.
setArffFile(String) - Method in class weka.gui.streams.InstanceLoader
setArffFile(String) - Method in class weka.gui.streams.InstanceSavePanel
setArtificialSize(double) - Method in class weka.classifiers.meta.Decorate: Sets factor that determines number of artificial examples to generate.
setAssociatedConnections(Vector) - Method in class weka.gui.beans.MetaBean
setAssociator(Associator) - Method in class weka.associations.CheckAssociator: Set the associator to test.
setAssociator(Associator) - Method in class weka.associations.SingleAssociatorEnhancer: Set the base associator.
setAssociator(Associator) - Method in class weka.gui.beans.Associator: Set the associator for this wrapper
setAsText(String) - Method in class weka.gui.CostMatrixEditor: Some objects can be represented as text, but a cost matrix cannot.
setAsText(String) - Method in class weka.gui.GenericArrayEditor: Returns null as we don't support getting/setting values as text.
setAsText(String) - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting/setting values as text.
setAsText(String) - Method in class weka.gui.SelectedTagEditor: Sets the current property value as text.
setAsText(String) - Method in class weka.gui.SimpleDateFormatEditor: Sets the date format string.
setAttIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Changes the boolean value at the specified index in the AttIndexes array
setAttList_Irr(boolean[]) - Method in class weka.datagenerators.classifiers.classification.RDG1: Sets the array that defines which of the attributes are seen to be irrelevant.
setAttribute(int) - Method in class weka.gui.AttributeSummaryPanel: Sets the attribute that statistics will be displayed for.
setAttribute(int) - Method in class weka.gui.AttributeVisualizationPanel: Tells the panel which attribute to visualize.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.FilteredAttributeEval: Set the attribute evaluator to use
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RaceSearch: Set the attribute evaluator to use for generating the ranking.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RankSearch: Set the attribute evaluator to use for generating the ranking.
setAttributeID(int) - Method in class weka.experiment.ClassifierSplitEvaluator: Set the index of Attibute Identifying the instances
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.Add: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.AddValues: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Sets the index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Sets index of the attribute used.
setAttributeIndexes(String) - Method in class weka.filters.unsupervised.attribute.NominalToString: Sets index of the attribute used.
setAttributeIndices(String) - Method in interface weka.core.DistanceFunction: Sets the range of attributes to use in the calculation of the distance.
setAttributeIndices(String) - Method in class weka.core.NormalizableDistance: Sets the range of attributes to use in the calculation of the distance.
setAttributeIndices(String) - Method in class weka.filters.supervised.attribute.Discretize: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Copy: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Sets which attributes are to be used for interquartile calculations and outlier/extreme value detection (only numeric attributes among the selection will be used).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Sets which attributes are to be acted on.
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Sets the columns to use, e.g., first-last or first-3,5-last
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Sets which attributes are to be "nominalized" (only numeric attributes among the selection will be transformed).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Set which attributes are to be transformed (or kept if invert is true).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Remove: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Reorder: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets which attributes are to be worked on.
setAttributeIndicesArray(int[]) - Method in class weka.filters.supervised.attribute.Discretize: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Copy: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Sets which attributes are to be used for interquartile calculations and outlier/extreme value detection (only numeric attributes among the selection will be used).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Sets which attributes are to be transoformed to nominal.
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Set which attributes are to be transformed (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Remove: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Reorder: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets which attributes are to be processed.
setAttributeName(String) - Method in class weka.filters.unsupervised.attribute.Add: Set the new attribute's name.
setAttributeName(String) - Method in class weka.filters.unsupervised.attribute.AddID: Set the new attribute's name
setAttributeNamePrefix(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Set the attribute name prefix.
setAttributeRange(String) - Method in class weka.filters.unsupervised.attribute.StringToNominal: Sets range of indices of the attributes used.
setAttributeSelectionMethod(SelectedTag) - Method in class weka.classifiers.functions.LinearRegression: Sets the method used to select attributes for use in the linear regression.
setAttributeType(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Add: Sets the type of attribute to generate.
setAttributeType(SelectedTag) - Method in class weka.filters.unsupervised.attribute.RemoveType: Sets the attribute type to be deleted by the filter.
setAttributeTypes(Hashtable) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Sets the attribute - attribute-type relation to use.
setAttrIndexRange(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets which attributes are used in the cluster attributes among the selection will be discretized.
setAtts(int[], boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Changes the boolean value at the specified index in the InstIndexes array
setAttsToEliminatePerIteration(int) - Method in class weka.attributeSelection.SVMAttributeEval: Set the constant rate of attribute elimination per iteration
setAutoBuild(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron: This will set whether the network is automatically built or if it is left up to the user.
setAutoKeyGeneration(boolean) - Method in class weka.core.converters.DatabaseSaver: En/Dis-ables the automatic generation of a primary key.
setBackground(Color) - Method in class weka.gui.visualize.BMPWriter: sets the background color to use in creating the JPEG
setBackground(Color) - Method in class weka.gui.visualize.JPEGWriter: sets the background color to use in creating the JPEG.
setBackground(Color) - Method in class weka.gui.visualize.PNGWriter: sets the background color to use in creating the JPEG
setBackground(Color) - Method in class weka.gui.visualize.PostscriptGraphics
setBagSizePercent(int) - Method in class weka.classifiers.meta.Bagging: Sets the size of each bag, as a percentage of the training set size.
setBagSizePercent(int) - Method in class weka.classifiers.meta.MetaCost: Sets the size of each bag, as a percentage of the training set size.
setBalanceClass(boolean) - Method in class weka.datagenerators.classifiers.classification.Agrawal: Sets whether the class is balanced.
setBalanced(boolean) - Method in class weka.classifiers.functions.Winnow: Set the value of Balanced.
setBallSplitter(BallSplitter) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Sets the ball splitting algorithm to be used by the TopDown constructor.
setBallTreeConstructor(BallTreeConstructor) - Method in class weka.core.neighboursearch.BallTree: Sets the BallTreeConstructor for building the BallTree (default TopDownConstructor).
setBase(double) - Method in class weka.core.neighboursearch.CoverTree: Sets the base to use for expansion constant.
setBaseExperiment(Experiment) - Method in class weka.experiment.RemoteExperiment: Set the base experiment.
setBeanContext(BeanContext) - Method in class weka.gui.beans.AbstractDataSource: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.CostBenefitAnalysis
setBeanContext(BeanContext) - Method in class weka.gui.beans.DataVisualizer: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.GraphViewer: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.Loader: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.ModelPerformanceChart: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.TextViewer: Set a bean context for this bean
setBeanInstances(Vector, JComponent) - Static method in class weka.gui.beans.BeanInstance: Describe setBeanInstances method here.
setBeta(double) - Method in class weka.classifiers.functions.Winnow: Set the value of Beta.
setBias(double) - Method in class weka.classifiers.functions.LibLINEAR: Sets bias term value (default 1) No bias term is added if value < 0
setBias(double) - Method in class weka.classifiers.misc.VFI: Set the value of the exponential bias towards more confident intervals
setBiasToUniformClass(double) - Method in class weka.filters.supervised.instance.Resample: Sets the bias towards a uniform class.
setBIFFile(String) - Method in class weka.classifiers.bayes.BayesNet: Set name of network in BIF file to compare with
setBIFFile(String) - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Set name of network in BIF file to read structure from
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Binarize numeric attributes.
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval: Binarize numeric attributes.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.supervised.attribute.NominalToBinary: Sets if binary attributes are to be treates as nominal ones.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Sets if binary attributes are to be treates as nominal ones.
setBinarySplits(boolean) - Method in class weka.classifiers.rules.PART: Set the value of binarySplits.
setBinarySplits(boolean) - Method in class weka.classifiers.trees.J48: Set the value of binarySplits.
setBinarySplits(boolean) - Method in class weka.classifiers.trees.J48graft: Set the value of binarySplits.
setBins(int) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets the number of bins to divide each selected numeric attribute into
setBins(int) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Ignored
setBinSplit(boolean) - Method in class weka.classifiers.trees.FT: Set the value of binarySplits.
setBinValue(double) - Method in class weka.clusterers.XMeans: Sets the distance value between true and false of binary attributes.
setBlendFactor(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Set the blending factor
setBlendMethod(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Set the blending method
setBooleanCols(Range) - Method in class weka.datagenerators.ClusterGenerator: Sets which attributes are boolean.
setBooleanIndices(String) - Method in class weka.datagenerators.ClusterGenerator: Sets which attributes are boolean
setBuildLogisticModels(boolean) - Method in class weka.classifiers.functions.SMO: Set the value of buildLogisticModels.
setBuildLogisticModels(boolean) - Method in class weka.classifiers.mi.MISMO: Set the value of buildLogisticModels.
setBuildRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.M5Base: Set the value of regressionTree.
setC(double) - Method in class weka.classifiers.functions.SMO: Set the value of C.
setC(double) - Method in class weka.classifiers.functions.SMOreg: Set the value of C.
setC(double) - Method in class weka.classifiers.mi.MISMO: Set the value of C.
setC(double) - Method in class weka.classifiers.mi.MISVM: Set the value of C.
setCacheKeyName(String) - Method in class weka.experiment.DatabaseResultListener: Set the value of CacheKeyName.
setCacheSize(double) - Method in class weka.classifiers.functions.LibSVM: Sets cache memory size in MB (default 40)
setCacheSize(int) - Method in class weka.classifiers.functions.supportVector.CachedKernel: Sets the size of the cache to use (a prime number)
setCacheSize(int) - Method in class weka.classifiers.functions.supportVector.StringKernel: Sets the size of the cache to use (a prime number)
setCalcOutOfBag(boolean) - Method in class weka.classifiers.meta.Bagging: Set whether the out of bag error is calculated.
setCalculateStdDevs(boolean) - Method in class weka.experiment.AveragingResultProducer: Set the value of CalculateStdDevs.
setCanChangeClassInDialog(boolean) - Method in class weka.gui.GenericObjectEditor: Sets whether the user can change the class in the dialog.
setCapabilities(Capabilities) - Method in class weka.core.FindWithCapabilities: Uses the given Capabilities for the search.
setCapabilities(Capabilities) - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog: sets the initial capabilities.
setCapabilitiesFilter(Capabilities) - Method in class weka.gui.ConverterFileChooser: sets the capabilities that the savers must have.
setCapabilitiesFilter(Capabilities) - Method in class weka.gui.GenericObjectEditor: Sets the capabilities to use for filtering.
setCapacity(int) - Method in class weka.core.FastVector: Sets the vector's capacity to the given value.
setCapacity(int) - Method in class weka.core.matrix.DoubleVector: Sets the capacity of the vector
setCapacity(int) - Method in class weka.core.matrix.IntVector: Sets the capacity of the vector
setCar(boolean) - Method in class weka.associations.Apriori: Sets class association rule mining
setCar(boolean) - Method in class weka.associations.PredictiveApriori: Sets class association rule mining
setCardinality(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet: Sets the cardinality of the attributes (incl class attribute)
setCell(int, int, Object) - Method in class weka.classifiers.CostMatrix: Set the value of a particular cell in the matrix
setCenter(double) - Method in class weka.gui.treevisualizer.Node: Set the value of center.
setCenterData(boolean) - Method in class weka.attributeSelection.PrincipalComponents: Set whether to center (rather than standardize) the data.
setCenterData(boolean) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Set whether to center (rather than standardize) the data.
setCenteredLocation() - Method in class weka.gui.arffviewer.ArffViewer: positions the window at the center of the screen
setChanged(boolean) - Method in class weka.gui.arffviewer.ArffPanel: can only reset the changed state to FALSE
setChar(Character) - Method in class weka.core.Trie.TrieNode: sets the character this node represents
setCharSet(String) - Method in class weka.core.converters.TextDirectoryLoader: Set the character set to use when reading text files (an empty string indicates that the default character set will be used).
setChecked(int, boolean) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: sets the checked state of the element at the given index
setChecked(int, boolean) - Method in class weka.gui.CheckBoxList: sets the checked state of the element at the given index
setCheckErrorRate(boolean) - Method in class weka.classifiers.rules.JRip: Sets whether to check for error rate is in stopping criterion
setChecksTurnedOff(boolean) - Method in class weka.classifiers.functions.SMO: Disables or enables the checks (which could be time-consuming).
setChecksTurnedOff(boolean) - Method in class weka.classifiers.functions.supportVector.Kernel: Disables or enables the checks (which could be time-consuming).
setChecksTurnedOff(boolean) - Method in class weka.classifiers.mi.MISMO: Disables or enables the checks (which could be time-consuming).
setChecksTurnedOff(boolean) - Method in class weka.filters.unsupervised.attribute.KernelFilter: Disables or enables the checks (which could be time-consuming).
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.Splitter: Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Sets the child for a branch of the split.
setCindex(int, double, double) - Method in class weka.gui.visualize.AttributePanel: Set the index of the attribute by which to colour the data.
setCindex(int) - Method in class weka.gui.visualize.AttributePanel: Set the index of the attribute by which to colour the data.
setCindex(int) - Method in class weka.gui.visualize.ClassPanel: Set the index of the attribute to display coloured labels for
setCindex(int) - Method in class weka.gui.visualize.Plot2D: Set the index of the attribute to use for colouring
setCindex(int) - Method in class weka.gui.visualize.PlotData2D: Set the colouring index of the data
setClass(Attribute) - Method in class weka.core.Instances: Sets the class attribute.
setClassColumn(String) - Method in class weka.gui.beans.ClassAssigner
setClassFlag(boolean) - Method in class weka.datagenerators.ClusterGenerator: Sets the class flag, if class flag is set, the cluster is listed as class atrribute in an extra attribute.
setClassForIRStatistics(int) - Method in class weka.experiment.ClassifierSplitEvaluator: Set the value of ClassForIRStatistics.
setClassification(boolean) - Method in class weka.associations.Tertius: Set the value of classification.
setClassifier(Classifier) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.classifiers.BVDecompose: Set the classifiers being analysed
setClassifier(Classifier) - Method in class weka.classifiers.BVDecomposeSegCVSub: Set the classifiers being analysed
setClassifier(Classifier) - Method in class weka.classifiers.CheckClassifier: Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.CheckSource: Sets the classifier to use for the comparison.
setClassifier(Classifier) - Method in class weka.classifiers.meta.GridSearch: Set the base learner.
setClassifier(Classifier) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the base learner.
setClassifier(Classifier) - Method in class weka.classifiers.SingleClassifierEnhancer: Set the base learner.
setClassifier(Classifier) - Method in class weka.experiment.ClassifierSplitEvaluator: Sets the classifier.
setClassifier(Classifier) - Method in class weka.experiment.RegressionSplitEvaluator: Sets the classifier.
setClassifier(Classifier) - Method in class weka.filters.supervised.attribute.AddClassification: Sets the classifier to classify instances with.
setClassifier(Classifier) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the classifier to classify instances with.
setClassifier(Classifier) - Method in class weka.gui.beans.BatchClassifierEvent: Set the classifier
setClassifier(Classifier) - Method in class weka.gui.beans.IncrementalClassifierEvent
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the classifier to use.
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Set a classifier to use
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the classifier to use
setClassifierName(String) - Method in class weka.experiment.ClassifierSplitEvaluator: Set the Classifier to use, given it's class name.
setClassifierName(String) - Method in class weka.experiment.RegressionSplitEvaluator: Set the Classifier to use, given it's class name.
setClassifiers(Classifier[]) - Method in class weka.classifiers.meta.MultiScheme: Sets the list of possible classifers to choose from.
setClassifiers(Classifier[]) - Method in class weka.classifiers.MultipleClassifiersCombiner: Sets the list of possible classifers to choose from.
setClassifierTemplate(Classifier) - Method in class weka.gui.beans.Classifier: Set the classifier for this wrapper
setClassifyIterations(int) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets the number of times an instance is classified
setClassIndex(int) - Method in class weka.associations.Apriori: Sets the class index
setClassIndex(int) - Method in interface weka.associations.CARuleMiner: Sets the class index for the class association rule miner
setClassIndex(int) - Method in class weka.associations.FilteredAssociator: Sets the class index
setClassIndex(int) - Method in class weka.associations.PredictiveApriori: Sets the class index
setClassIndex(int) - Method in class weka.associations.Tertius: Set the value of classIndex.
setClassIndex(int) - Method in class weka.classifiers.BVDecompose: Sets index of attribute to discretize on
setClassIndex(int) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets index of attribute to discretize on
setClassIndex(int) - Method in class weka.classifiers.CheckSource: Sets the class index of the dataset.
setClassIndex(String) - Method in class weka.core.converters.LibSVMSaver: Sets index of the class attribute.
setClassIndex(String) - Method in class weka.core.converters.SVMLightSaver: Sets index of the class attribute.
setClassIndex(String) - Method in class weka.core.converters.XRFFSaver: Sets index of the class attribute.
setClassIndex(String) - Method in class weka.core.FindWithCapabilities: sets the class index, -1 for none, first and last are also valid.
setClassIndex(int) - Method in class weka.core.Instances: Sets the class index of the set.
setClassIndex(int) - Method in class weka.core.TestInstances: sets the class index (0-based)
setClassIndex(int) - Method in class weka.filters.CheckSource: Sets the class index of the dataset.
setClassIndex(String) - Method in class weka.filters.unsupervised.attribute.ClassAssigner: sets the class index.
setClassIndex(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the attribute on which misclassifications are based.
setClassMissing() - Method in class weka.core.Instance: Sets the class value of an instance to be "missing".
setClassname(String) - Method in class weka.core.AllJavadoc: sets the classname of the class to generate the Javadoc for
setClassname(String) - Method in class weka.core.Javadoc: sets the classname of the class to generate the Javadoc for
setClassname(String) - Method in class weka.core.ListOptions: sets the classname of the class to generate the Javadoc for
setClassName(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Sets the class containing the transformation method.
setClassOrder(int) - Method in class weka.filters.supervised.attribute.ClassOrder: Set the wanted class order
setClassType(int) - Method in class weka.core.TestInstances: sets the class attribute type
setClassType(Class) - Method in class weka.gui.GenericObjectEditor: Sets the class of values that can be edited.
setClassValue(double) - Method in class weka.core.Instance: Sets the class value of an instance to the given value (internal floating-point format).
setClassValue(String) - Method in class weka.core.Instance: Sets the class value of an instance to the given value.
setClassValue(String) - Method in class weka.filters.supervised.instance.SMOTE: Sets the index of the class value to which SMOTE should be applied.
setClassValue(String) - Method in class weka.gui.beans.ClassValuePicker: Set the class value index considered to be the "positive" class value.
setClearEachDataset(boolean) - Method in class weka.gui.streams.InstanceViewer
setClip(int, int, int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
setClip(Shape) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
setCloseTo(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Set the "close to" number.
setCloseToDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Set the "close to" default.
setCloseToTolerance(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Set the "close to" Tolerance.
setClusterDefinitions(ClusterDefinition[]) - Method in class weka.datagenerators.clusterers.SubspaceCluster: sets the clusters to use
setClusterer(Clusterer) - Method in class weka.attributeSelection.UnsupervisedSubsetEvaluator: Set the clusterer to use
setClusterer(Clusterer) - Method in class weka.classifiers.meta.ClassificationViaClustering: Set the base clusterer.
setClusterer(Clusterer) - Method in class weka.clusterers.CheckClusterer: Set the clusterer for testing.
setClusterer(Clusterer) - Method in class weka.clusterers.ClusterEvaluation: set the clusterer
setClusterer(Clusterer) - Method in class weka.clusterers.MakeDensityBasedClusterer: Sets the clusterer to wrap.
setClusterer(Clusterer) - Method in class weka.clusterers.SingleClustererEnhancer: Set the base clusterer.
setClusterer(DensityBasedClusterer) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Sets the clusterer.
setClusterer(Clusterer) - Method in class weka.filters.unsupervised.attribute.AddCluster: Sets the clusterer to assign clusters with.
setClusterer(Clusterer) - Method in class weka.gui.beans.Clusterer: Set the clusterer for this wrapper
setClustererName(String) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Set the Clusterer to use, given it's class name.
setClusteringSeed(int) - Method in class weka.classifiers.functions.RBFNetwork: Set the random seed to be passed on to K-means.
setClusterLabel(int) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Sets the clusterID (cluster), to which this DataObject belongs to
setClusterLabel(int) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Sets the clusterID (cluster), to which this DataObject belongs to
setClusterLabel(int) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Sets the clusterID (cluster), to which this DataObject belongs to
setClusterSubType(SelectedTag) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets the cluster sub type.
setClusterType(SelectedTag) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets the cluster type.
setCoef0(double) - Method in class weka.classifiers.functions.LibSVM: Sets coef (default 0)
setColHidden(int, boolean) - Method in class weka.experiment.ResultMatrix: sets the hidden status of the column (if the index is valid)
setColName(int, String) - Method in class weka.experiment.ResultMatrix: sets the name of the column (if the index is valid)
setColNameWidth(int) - Method in class weka.experiment.ResultMatrix: sets the width for the column names (0 = optimal)
setColor(Color) - Method in class weka.gui.treevisualizer.Node: Set the value of color.
setColor(Color) - Method in class weka.gui.visualize.PostscriptGraphics: Set current pen color.
setColOrder(int[]) - Method in class weka.experiment.ResultMatrix: sets the ordering of the columns, null means default
setColoringIndex(int) - Method in class weka.gui.AttributeVisualizationPanel: Set the coloring (class) index for the plot
setColoringIndex(int) - Method in class weka.gui.beans.AttributeSummarizer: Set the coloring index for the attribute summary plots
setColors(FastVector) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set a vector of Color objects for the classes
setColourIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Sets the index used for colouring.
setColours(FastVector) - Method in class weka.gui.visualize.AttributePanel: Sets a list of colours to use for colouring data points
setColours(FastVector) - Method in class weka.gui.visualize.ClassPanel: Set a list of colours to use for colouring labels
setColours(FastVector) - Method in class weka.gui.visualize.Plot2D: Set a list of colours to use when colouring points according to class values or cluster numbers
setColumn(int, double[]) - Method in class weka.core.Matrix: Deprecated.

Sets a column of the matrix to the given column.
setColumnDimension(int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the column dimenion of the matrix
setCombination(SelectedTag) - Method in class weka.attributeSelection.ScatterSearchV1: Set the kind of combination
setCombinationRule(SelectedTag) - Method in class weka.classifiers.meta.Vote: Sets the combination rule to use.
setComplexityParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval: Set the value of C for SMO
setComponent(JComponent) - Method in class weka.gui.visualize.JComponentWriter: sets the component to print to an output format
setComposite(Composite) - Method in class weka.gui.visualize.PostscriptGraphics
setCompressOutput(boolean) - Method in class weka.core.converters.ArffSaver: Sets whether to compress the output.
setCompressOutput(boolean) - Method in class weka.core.converters.XRFFSaver: Sets whether to compress the output.
setConfidenceFactor(float) - Method in class weka.classifiers.rules.PART: Set the value of CF.
setConfidenceFactor(float) - Method in class weka.classifiers.trees.J48: Set the value of CF.
setConfidenceFactor(float) - Method in class weka.classifiers.trees.J48graft: Set the value of CF.
setConfirmationThreshold(double) - Method in class weka.associations.Tertius: Set the value of confirmationThreshold.
setConfirmationValues(int) - Method in class weka.associations.Tertius: Set the value of confirmationValues.
setConfirmExit(boolean) - Method in class weka.gui.arffviewer.ArffViewer: whether to present a MessageBox on Exit or not
setConfirmExit(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: whether to present a MessageBox on Exit or not
setConnections(Vector) - Static method in class weka.gui.beans.BeanConnection: Describe setConnections method here.
setConnectPoints(boolean[]) - Method in class weka.gui.visualize.PlotData2D: Set whether consecutive points should be connected by lines
setConnectPoints(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set whether consecutive points should be connected by lines
setConsequent(double) - Method in class weka.classifiers.rules.JRip.RipperRule: Sets the internal representation of the class label to be predicted
setConservativeForwardSelection(boolean) - Method in class weka.attributeSelection.GreedyStepwise: Set whether attributes should continue to be added during a forward search as long as merit does not decrease
setContainChildBalls(boolean) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Sets whether if a parent ball should completely enclose its two child balls.
setConvertNominal(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of convertNominal.
setConvertNominalToBinary(boolean) - Method in class weka.classifiers.functions.LibLINEAR: Whether to turn on conversion of nominal attributes to binary.
setCoreConvertersOnly(boolean) - Method in class weka.gui.ConverterFileChooser: Whether to display only the hardocded core converters.
setCoreDistance(double) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Sets a new coreDistance for this dataObject
setCoreDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Sets a new coreDistance for this dataObject
setCoreDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Sets a new coreDistance for this dataObject
setCoreDistanceColor(Color) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Sets a new color for the coreDistance
setCost(double) - Method in class weka.classifiers.functions.LibLINEAR: Sets the cost parameter C (default 1)
setCost(double) - Method in class weka.classifiers.functions.LibSVM: Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
setCostMatrix(CostMatrix) - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Sets the misclassification cost matrix.
setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Sets the misclassification cost matrix.
setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.MetaCost: Sets the misclassification cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.MetaCost: Sets the source location of the cost matrix.
setCount(int, double) - Method in class weka.experiment.ResultMatrix: sets the count for the row (if the index is valid)
setCounter(int) - Method in class weka.associations.ItemSet: Sets the counter
setCountWidth(int) - Method in class weka.experiment.ResultMatrix: sets the width for the counts (0 = optimal)
setCreatorApplication(Document) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Set the name of the application (if specified) that created this model
setCreatorApplication(Document) - Method in interface weka.core.pmml.PMMLModel: Set the name of the application (if specified) that created this.
setCriticalValue(int) - Method in class weka.classifiers.bayes.AODEsr: Sets the critical value
setCrossoverProb(double) - Method in class weka.attributeSelection.GeneticSearch: set the probability of crossover
setCrossVal(int) - Method in class weka.classifiers.rules.DecisionTable: Sets the number of folds for cross validation (1 = leave one out)
setCrossValidate(boolean) - Method in class weka.classifiers.lazy.IBk: Sets whether hold-one-out cross-validation will be used to select the best k value.
setCurrentFilename(String) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: sets the filename of the current tab
setCurrentInstance(Instance) - Method in class weka.gui.beans.IncrementalClassifierEvent: Set the current instance for this event
setCurveData(PlotData2D, Attribute) - Method in class weka.gui.beans.CostBenefitAnalysis: Set the threshold curve data to use.
setCustomColour(Color) - Method in class weka.gui.visualize.PlotData2D: Set a custom colour to use for this plot.
setCustomHeight(int) - Method in class weka.gui.visualize.JComponentWriter: sets the custom height to use
setCustomName(String) - Method in class weka.gui.beans.Associator: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in interface weka.gui.beans.BeanCommon: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.ClassAssigner: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Classifier: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.ClassValuePicker: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Clusterer: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.CostBenefitAnalysis: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.CrossValidationFoldMaker: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Filter: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Loader: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.MetaBean: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.PredictionAppender: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.Saver: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.SerializedModelSaver: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.StripChart: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.TestSetMaker: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.TextViewer: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.TrainingSetMaker: Set a custom (descriptive) name for this bean
setCustomName(String) - Method in class weka.gui.beans.TrainTestSplitMaker: Set a custom (descriptive) name for this bean
setCustomWidth(int) - Method in class weka.gui.visualize.JComponentWriter: sets the custom width to use
setCutoff(double) - Method in class weka.clusterers.Cobweb: set the cutoff
setCutOffFactor(double) - Method in class weka.clusterers.XMeans: Sets a new cutoff factor.
setCVisible(boolean) - Method in class weka.gui.treevisualizer.Node: Sets all the children of this node either to visible or invisible
setCVParameters(Object[]) - Method in class weka.classifiers.meta.CVParameterSelection: Set method for CVParameters.
setCVType(SelectedTag) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: set cross validation strategy to be used in searching for networks.
setData(Instances) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Assuming a network structure is defined and we want to learn from data, the data set must be put if correct order first and possibly discretized/missing values filled in before proceeding to CPT learning.
setData(Instances) - Method in class weka.classifiers.rules.RuleStats: Set the data of the stats, overwriting the old one if any
setDatabase_distanceType(String) - Method in class weka.clusterers.DBSCAN: Sets a new distance-type
setDatabase_distanceType(String) - Method in class weka.clusterers.OPTICS: Sets a new distance-type
setDatabase_Type(String) - Method in class weka.clusterers.DBSCAN: Sets a new database-type
setDatabase_Type(String) - Method in class weka.clusterers.OPTICS: Sets a new database-type
setDatabaseOutput(File) - Method in class weka.clusterers.OPTICS: Sets the the file to save the generated database to.
setDatabaseURL(String) - Method in class weka.experiment.DatabaseUtils: Set the value of DatabaseURL.
setDataFileName(String) - Method in class weka.classifiers.BVDecompose: Sets the name of the data file used for the decomposition
setDataFileName(String) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets the name of the dataset file.
setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the data generator to use for generating new instances
setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the density estimator to use
setDataPoint(double[]) - Method in class weka.gui.beans.ChartEvent: Set the data point
setDataSeqID(int) - Method in class weka.associations.GeneralizedSequentialPatterns: Sets the attribute representing the data sequence ID.
setDataset(File) - Method in class weka.classifiers.CheckSource: Sets the dataset to use for testing.
setDataset(Instances) - Method in class weka.core.Instance: Sets the reference to the dataset.
setDataset(File) - Method in class weka.filters.CheckSource: Sets the dataset to use for testing.
setDatasetFormat(Instances) - Method in class weka.datagenerators.DataGenerator: Sets the format of the dataset that is to be generated.
setDatasetKeyColumns(Range) - Method in class weka.experiment.PairedTTester: Set the value of DatasetKeyColumns.
setDatasetKeyColumns(Range) - Method in interface weka.experiment.Tester: Set the value of DatasetKeyColumns.
setDatasetKeyFromDialog() - Method in class weka.gui.experiment.ResultsPanel
setDatasets(DefaultListModel) - Method in class weka.experiment.Experiment: Set the datasets to use in the experiment
setDatasets(DefaultListModel) - Method in class weka.experiment.RemoteExperiment: Set the datasets to use in the experiment
setDataType(int) - Method in class weka.gui.beans.xml.XMLBeans: sets what kind of data is to be read/written
setDateAttributes(String) - Method in class weka.core.converters.CSVLoader: Set the attribute range to be forced to type date.
setDateFormat(String) - Method in class weka.core.converters.CSVLoader: Set the format to use for parsing date values.
setDateFormat(String) - Method in class weka.filters.unsupervised.attribute.Add: Set the date format, complying to ISO-8601.
setDateFormat(String) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Sets the output date format.
setDateFormat(SimpleDateFormat) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Sets the output date format.
setDB(boolean) - Method in class weka.gui.beans.Loader
setDebug(boolean) - Method in class weka.associations.GeneralizedSequentialPatterns: Set debugging mode.
setDebug(boolean) - Method in class weka.attributeSelection.RaceSearch: Set whether verbose output should be generated.
setDebug(boolean) - Method in class weka.attributeSelection.ScatterSearchV1: Set whether verbose output should be generated.
setDebug(boolean) - Method in class weka.classifiers.bayes.BayesianLogisticRegression
setDebug(boolean) - Method in class weka.classifiers.BVDecompose: Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.Classifier: Set debugging mode.
setDebug(boolean) - Method in class weka.classifiers.functions.LeastMedSq: sets whether or not debugging output shouild be printed
setDebug(boolean) - Method in class weka.classifiers.functions.LinearRegression: Controls whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.functions.Logistic: Sets whether debugging output will be printed.
setDebug(boolean) - Method in class weka.classifiers.functions.PaceRegression: Controls whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.functions.supportVector.Kernel: Enables or disables the output of debug information (if the derived kernel supports that)
setDebug(boolean) - Method in class weka.classifiers.meta.MultiScheme: Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.rules.JRip: Sets whether debug information is output to the console
setDebug(boolean) - Method in class weka.clusterers.EM: Set debug mode - verbose output
setDebug(boolean) - Method in class weka.clusterers.HierarchicalClusterer: Set debugging mode.
setDebug(boolean) - Method in class weka.clusterers.sIB: Set debug mode - verbose output
setDebug(boolean) - Method in class weka.core.Check: Set debugging mode
setDebug(boolean) - Method in class weka.core.converters.TextDirectoryLoader: Sets whether to print some debug information.
setDebug(boolean) - Method in class weka.core.Debug.Random: sets whether to print the generated random values or not
setDebug(boolean) - Method in class weka.core.Optimization: Set whether in debug mode
setDebug(boolean) - Method in class weka.datagenerators.DataGenerator: Sets the debug flag.
setDebug(boolean) - Method in class weka.estimators.CheckEstimator: Set debugging mode
setDebug(boolean) - Method in class weka.estimators.Estimator: Set debugging mode.
setDebug(boolean) - Method in class weka.experiment.DatabaseUtils: Sets whether there should be printed some debugging output to stderr or not.
setDebug(boolean) - Method in class weka.filters.SimpleFilter: Sets the debugging mode
setDebug(boolean) - Method in class weka.filters.unsupervised.attribute.AddExpression: Set debug mode.
setDebug(boolean) - Method in class weka.gui.SimpleCLIPanel.CommandlineCompletion: sets debug mode on/off.
setDebug(boolean) - Method in class weka.gui.streams.InstanceCounter
setDebug(boolean) - Method in class weka.gui.streams.InstanceJoiner
setDebug(boolean) - Method in class weka.gui.streams.InstanceLoader
setDebug(boolean) - Method in class weka.gui.streams.InstanceSavePanel
setDebug(boolean) - Method in class weka.gui.streams.InstanceTable
setDebug(boolean) - Method in class weka.gui.streams.InstanceViewer
setDebugLevel(int) - Method in class weka.clusterers.XMeans: Sets the debug level.
setDebugVectorsFile(File) - Method in class weka.clusterers.XMeans: Sets the file that has the random vectors stored.
setDecay(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
setDecimals(int) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Set the number of decimals to round to.
setDefaultValue() - Method in class weka.gui.GenericObjectEditor: Sets the current object to be the default, taken as the first item in the chooser.
setDefaultWeight(double) - Method in class weka.classifiers.functions.Winnow: Set the value of defaultWeight.
setDegree(int) - Method in class weka.classifiers.functions.LibSVM: Sets the degree of the kernel
setDegreesOfFreedom(int) - Method in class weka.experiment.PairedStats: Sets the degrees of freedom (if calibration is required).
setDeleteEmptyBins(boolean) - Method in class weka.classifiers.meta.RegressionByDiscretization: Sets the number of bins to divide each selected numeric attribute into
setDelimiters(String) - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer: Set the value of delimiters.
setDelta(double) - Method in class weka.associations.Apriori: Set the value of delta.
setDelta(double) - Method in class weka.associations.FPGrowth: Set the value of delta.
setDelta(double) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Sets the m_fDelta.
setDelta(double) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Sets the m_fDelta.
setDensityBasedClusterer(DensityBasedClusterer) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Set the clusterer for use in filtering
setDescendantPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
setDescendantPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
setDescendents(ArrayList, C45PruneableClassifierTreeG) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: add the grafted nodes at originalLeaf's position in tree.
setDesign(boolean) - Method in class weka.gui.beans.AttributeSummarizer: Set whether the appearance of this bean should be design or application
setDesignatedClass(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector: Sets the method to determine which class value to optimize.
setDesiredSize(int) - Method in class weka.classifiers.meta.Decorate: Sets the desired size of the committee.
setDesiredWeightOfInstancesPerInterval(double) - Method in class weka.filters.unsupervised.attribute.Discretize: Set the DesiredWeightOfInstancesPerInterval value.
setDestination(File) - Method in class weka.core.converters.AbstractFileSaver: Sets the destination file (and directories if necessary).
setDestination(OutputStream) - Method in class weka.core.converters.AbstractFileSaver: Sets the destination output stream.
setDestination(File) - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
setDestination(OutputStream) - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
setDestination(OutputStream) - Method in class weka.core.converters.ArffSaver: Sets the destination output stream.
setDestination(String, String, String) - Method in class weka.core.converters.DatabaseSaver: Sets the database url.
setDestination(String) - Method in class weka.core.converters.DatabaseSaver: Sets the database url.
setDestination() - Method in class weka.core.converters.DatabaseSaver: Sets the database url using the DatabaseUtils file.
setDestination(File) - Method in interface weka.core.converters.Saver: Resets the Saver object and sets the destination to be the supplied File object.
setDestination(OutputStream) - Method in interface weka.core.converters.Saver: Resets the Saver object and sets the destination to be the supplied InputStream.
setDestination(OutputStream) - Method in class weka.core.converters.SerializedInstancesSaver: Sets the destination output stream.
setDestination(OutputStream) - Method in class weka.core.converters.XRFFSaver: Sets the destination output stream.
setDetectionPerAttribute(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Set whether an Outlier/ExtremeValue attribute pair is generated for each numeric attribute ("true") or just one pair for all numeric attributes together ("false").
setDir(String) - Method in class weka.core.converters.AbstractFileSaver: Sets the directory where the instances should be stored
setDir(String) - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
setDir(String) - Method in interface weka.core.converters.Saver: Sets the directory of the output file.
setDir(String) - Method in class weka.core.Javadoc: sets the dir containing the file that is to be updated.
setDirAndPrefix(String, String) - Method in class weka.core.converters.AbstractFileSaver: Sets the directory and the file prefix.
setDirAndPrefix(String, String) - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
setDirAndPrefix(String, String) - Method in interface weka.core.converters.Saver: Sets the file prefix and the directory.
setDirection(SelectedTag) - Method in class weka.attributeSelection.BestFirst: Set the search direction
setDirectory(File) - Method in class weka.core.converters.TextDirectoryLoader: sets the source directory
setDirectory(File) - Method in class weka.gui.beans.SerializedModelSaver: Set the directory that the model(s) will be saved into.
setDiscretizeBin(int) - Method in class weka.classifiers.mi.MIBoost: Set the number of bins in discretization
setDisplayConnectors(boolean) - Method in class weka.gui.beans.BeanVisual: Turn on/off the connector points
setDisplayConnectors(boolean, Color) - Method in class weka.gui.beans.BeanVisual: Turn on/off the connector points
setDisplayedFromDialog() - Method in class weka.gui.experiment.ResultsPanel
setDisplayedResultsets(int[]) - Method in class weka.experiment.PairedTTester: Sets the indicies of the datasets to display (null means all).
setDisplayedResultsets(int[]) - Method in interface weka.experiment.Tester: Sets the indicies of the datasets to display (null means all).
setDisplayModelInOldFormat(boolean) - Method in class weka.classifiers.bayes.NaiveBayes: Set whether to display model output in the old, original format.
setDisplayModelInOldFormat(boolean) - Method in class weka.clusterers.EM: Set whether to display model output in the old, original format.
setDisplayRules(boolean) - Method in class weka.classifiers.rules.DecisionTable: Sets whether rules are to be printed
setDisplayStdDevs(boolean) - Method in class weka.clusterers.SimpleKMeans: Sets whether standard deviations and nominal count Should be displayed in the clustering output
setDistanceF(DistanceFunction) - Method in class weka.clusterers.XMeans: gets the "binary" distance value.
setDistanceFunction(DistanceFunction) - Method in class weka.clusterers.HierarchicalClusterer
setDistanceFunction(DistanceFunction) - Method in class weka.clusterers.SimpleKMeans: sets the distance function to use for instance comparison.
setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.CoverTree: Sets the distance function to use for nearest neighbour search.
setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.KDTree: sets the distance function to use for nearest neighbour search.
setDistanceFunction(DistanceFunction) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: sets the distance function to use for nearest neighbour search.
setDistanceIsBranchLength(boolean) - Method in class weka.clusterers.HierarchicalClusterer
setDistanceWeighting(SelectedTag) - Method in class weka.classifiers.lazy.IBk: Sets the distance weighting method used.
setDistMult(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the distance multiplier.
setDistribution(String, double[][]) - Method in class weka.classifiers.bayes.net.EditableBayesNet: specify distribution of a node
setDistribution(int, double[][]) - Method in class weka.classifiers.bayes.net.EditableBayesNet: specify distribution of a node
setDistribution(SelectedTag) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the distribution to use for calculating the random matrix
setDistributionSpread(double) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets the value for the distribution spread
setDocType(String) - Method in class weka.core.xml.XMLDocument: sets the DOCTYPE-String to use in the XML output.
setDocument(Document) - Method in class weka.core.xml.XMLDocument: sets the DOM document to use.
setDoNotOperateOnPerClassBasis(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Set the DoNotOperateOnPerClassBasis value.
setDoNotReplaceMissingValues(boolean) - Method in class weka.classifiers.functions.LibLINEAR: Whether to turn off automatic replacement of missing values.
setDoNotReplaceMissingValues(boolean) - Method in class weka.classifiers.functions.LibSVM: Whether to turn off automatic replacement of missing values.
setDoNotWeightBags(boolean) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Sets whether bags are weighted
setDontFilterAfterFirstBatch(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Set whether to apply the filter to instances that arrive once the first (training) batch has been seen.
setDontNormalize(boolean) - Method in class weka.classifiers.functions.SPegasos: Turn normalization off/on.
setDontNormalize(boolean) - Method in class weka.core.NormalizableDistance: Sets whether if the attribute values are to be normalized in distance calculation.
setDontReplaceMissing(boolean) - Method in class weka.classifiers.functions.SPegasos: Turn global replacement of missing values off/on.
setDontReplaceMissingValues(boolean) - Method in class weka.clusterers.SimpleKMeans: Sets whether missing values are to be replaced
setElement(int, int, double) - Method in class weka.classifiers.CostMatrix: Set the value of a cell as a double
setElement(int, double) - Method in class weka.core.AlgVector: Sets an element of the matrix to the given value.
setElement(int, int, double) - Method in class weka.core.Matrix: Deprecated.

Sets an element of the matrix to the given value.
setElementAt(Object, int) - Method in class weka.core.FastVector: Sets the element at the given index.
setElementAt(Object, int) - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Sets the component at the specified index of this list to be the specified object.
setElements(double[]) - Method in class weka.core.AlgVector: Sets the elements of the vector to values of the given array.
setEliminateColinearAttributes(boolean) - Method in class weka.classifiers.functions.LinearRegression: Set the value of EliminateColinearAttributes.
setEnabled(boolean) - Method in class weka.core.Debug: sets whether the logging is enabled or not
setEnabled(boolean) - Method in class weka.core.Memory: sets whether the memory management is enabled
setEnabled(boolean) - Method in class weka.gui.GenericObjectEditor: Sets whether the editor is "enabled", meaning that the current values will be painted.
setEnabled(boolean) - Method in class weka.gui.PropertyPanel: Passes on enabled/disabled status to the custom panel (if one is set).
setEnclosureCharacters(String) - Method in class weka.core.converters.CSVLoader: Set the character(s) to use/recognize as string enclosures
setEntropicAutoBlend(boolean) - Method in class weka.classifiers.lazy.KStar: Set whether entropic blending is to be used.
setEnumerateColNames(boolean) - Method in class weka.experiment.ResultMatrix: sets whether the column names are prefixed with "(x)" where "x" is the index
setEnumerateRowNames(boolean) - Method in class weka.experiment.ResultMatrix: sets whether to the row names or numbers instead are enumerateed
setEnvironment(Environment) - Method in class weka.core.converters.AbstractFileLoader: Set the environment variables to use.
setEnvironment(Environment) - Method in class weka.core.converters.AbstractFileSaver: Set the environment variables to use.
setEnvironment(Environment) - Method in interface weka.core.EnvironmentHandler: Set environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.FlowRunner: Set the environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.KnowledgeFlowApp: Set the environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.Loader: Set environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.Saver: Set environment variables to use.
setEnvironment(Environment) - Method in class weka.gui.beans.SerializedModelSaver: Set environment variables to use.
setEpochs(int) - Method in class weka.classifiers.functions.SPegasos: Set the number of epochs to use
setEps(double) - Method in class weka.classifiers.functions.LibLINEAR: Sets tolerance of termination criterion (default 0.001)
setEps(double) - Method in class weka.classifiers.functions.LibSVM: Sets tolerance of termination criterion (default 0.001)
setEpsilon(double) - Method in class weka.classifiers.functions.SMO: Set the value of epsilon.
setEpsilon(double) - Method in class weka.classifiers.functions.supportVector.RegSMO: Set the value of epsilon.
setEpsilon(double) - Method in class weka.classifiers.mi.MISMO: Set the value of epsilon.
setEpsilon(double) - Method in class weka.clusterers.DBSCAN: Sets a new value for epsilon
setEpsilon(double) - Method in class weka.clusterers.OPTICS: Sets a new value for epsilon
setEpsilonParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval: Set the value of P for SMO
setEpsilonParameter(double) - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Set the value of epsilon parameter of the epsilon insensitive loss function.
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of errorOnProbabilities.
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.trees.FT: Set the value of errorOnProbabilities.
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of errorOnProbabilities.
setEstimator(BayesNetEstimator) - Method in class weka.classifiers.bayes.BayesNet: Set the Estimator Algorithm used in calculating the CPTs
setEstimator(SelectedTag) - Method in class weka.classifiers.functions.PaceRegression: Sets the estimator.
setEstimator(Estimator) - Method in class weka.estimators.CheckEstimator: Set the estimator for boosting.
setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.balltrees.BallSplitter: Sets the distance function used to (or to be used to) build the tree.
setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Sets the distance function to use to build the tree.
setEuclideanDistanceFunction(EuclideanDistance) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Sets the EuclideanDistance object to use for splitting nodes.
setEvaluation(SelectedTag) - Method in class weka.classifiers.meta.GridSearch: Sets the criterion to use for evaluating the classifier performance.
setEvaluationMeasure(SelectedTag) - Method in class weka.classifiers.rules.DecisionTable: Sets the performance evaluation measure to use for selecting attributes for the decision table
setEvaluationMode(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector: Sets the evaluation mode used.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.AttributeSelection: set the attribute/subset evaluator
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CheckAttributeSelection: Set the evaluator to test.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Set the base evaluator.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveAttributeEval: Set the base evaluator.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.CostSensitiveSubsetEval: Set the base evaluator.
setEvaluator(ASEvaluation) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Sets the attribute evaluator
setEvaluator(ASEvaluation) - Method in class weka.filters.supervised.attribute.AttributeSelection: set attribute/subset evaluator
setEvalUsingTrainingData(boolean) - Method in class weka.attributeSelection.OneRAttributeEval: Use the training data to evaluate attributes rather than cross validation
setEvidence(int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: set evidence state of a node.
setEvidence(int, int) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
setEvidence(int, int) - Method in class weka.classifiers.bayes.net.MarginCalculator
setExclusive(boolean) - Method in class weka.classifiers.rules.ConjunctiveRule: Sets whether exclusive expressions for nominal attributes splits are considered
setExecutionSlots(int) - Method in class weka.gui.beans.Classifier: Set the number of execution slots (threads) to use to train models with.
setExecutionStatus(int) - Method in class weka.experiment.TaskStatusInfo: Set the execution status of this Task.
setExitIfNoWindowsOpen(boolean) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Sets whether System.exit gets called when no more windows are open.
setExitOnClose(boolean) - Method in class weka.gui.arffviewer.ArffViewer: whether to do a System.exit(0) on close
setExitOnClose(boolean) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: whether to do a System.exit(0) on close
setExpectedResultsPerAverage(int) - Method in class weka.experiment.AveragingResultProducer: Set the value of ExpectedResultsPerAverage.
setExperiment(Experiment) - Method in class weka.experiment.RemoteExperimentSubTask: Set the experiment for this sub task
setExperiment(Experiment) - Method in class weka.gui.experiment.AlgorithmListPanel: Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.DatasetListPanel: Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.DistributeExperimentPanel: Sets the experiment to be configured.
setExperiment(Experiment) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Sets the experiment which will have the custom properties edited.
setExperiment(RemoteExperiment) - Method in class weka.gui.experiment.HostListPanel: Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.ResultsPanel: Tells the panel to use a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunNumberPanel: Sets the experiment to be configured.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunPanel: Sets the experiment the panel operates on.
setExperiment(Experiment) - Method in class weka.gui.experiment.SetupPanel: Sets the experiment to configure.
setExperiment(Experiment) - Method in class weka.gui.experiment.SimpleSetupPanel: Sets the experiment to configure.
setExplicitPropsFile(boolean) - Method in class weka.gui.GenericPropertiesCreator: if FALSE, the locating of a props-file of the Utils-class is used, otherwise it's tried to load the specified file
setExplorer(Explorer) - Method in class weka.gui.explorer.AssociationsPanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.AttributeSelectionPanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.ClassifierPanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.ClustererPanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in interface weka.gui.explorer.Explorer.ExplorerPanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.PreprocessPanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExplorer(Explorer) - Method in class weka.gui.explorer.VisualizePanel: Sets the Explorer to use as parent frame (used for sending notifications about changes in the data)
setExponent(double) - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel: Sets the exponent value (must be different from 1.0).
setExponent(double) - Method in class weka.classifiers.functions.supportVector.PolyKernel: Sets the exponent value.
setExponent(double) - Method in class weka.classifiers.functions.VotedPerceptron: Set the value of exponent.
setExpression(String) - Method in class weka.datagenerators.classifiers.regression.Expression: Sets the mathematical expression to generate y out of x.
setExpression(String) - Method in class weka.filters.unsupervised.attribute.AddExpression: Set the expression to apply
setExpression(String) - Method in class weka.filters.unsupervised.attribute.MathExpression: Set the expression to apply
setExpression(String) - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Sets the expression used for filtering.
setExtremeValuesAsOutliers(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Set whether extreme values are also tagged as outliers.
setExtremeValuesFactor(double) - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Sets the factor for determining the thresholds for extreme values.
setFalseNegative(double) - Method in class weka.classifiers.evaluation.TwoClassStats: Sets the number of positive instances predicted as negative
setFalsePositive(double) - Method in class weka.classifiers.evaluation.TwoClassStats: Sets the number of negative instances predicted as positive
setFastRegression(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of fastRegression.
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.DerivedFieldMetaInfo: Upadate the field definitions for this derived field
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Discretize: Set the field definitions for this Expression to use
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Expression: Set the field definitions for this Expression to use
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.FieldRef
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.NormContinuous: Set the field definitions for this Expression to use
setFieldDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.NormDiscrete: Set the field definitions for this Expression to use
setFile(File) - Method in class weka.core.converters.AbstractFileLoader: sets the source File
setFile(File) - Method in class weka.core.converters.AbstractFileSaver: Sets the destination file.
setFile(File) - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
setFile(File) - Method in class weka.core.converters.ArffLoader: sets the source File
setFile(File) - Method in class weka.core.converters.ArffSaver: Sets the destination file.
setFile(File) - Method in interface weka.core.converters.FileSourcedConverter: Set the file to load from/ to save in
setFile(File) - Method in interface weka.core.converters.Saver: Sets the output file
setFile(File) - Method in class weka.core.converters.XRFFSaver: Sets the destination file.
setFile(File) - Method in class weka.gui.visualize.JComponentWriter: sets the file to store the output in
setFileFormat(Tag) - Method in class weka.gui.beans.SerializedModelSaver: Set the file format to use for saving.
setFileMustExist(boolean) - Method in class weka.gui.ConverterFileChooser: Whether the selected file must exst (only open dialog).
setFilename(String) - Method in class weka.core.FindWithCapabilities: Sets the dataset filename to base the capabilities on.
setFilename(String) - Method in class weka.gui.arffviewer.ArffPanel: sets the filename
setFilename(int, String) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: sets the filename of the specified panel
setFilePrefix(String) - Method in class weka.core.converters.AbstractFileSaver: Sets the file name prefix
setFilePrefix(String) - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
setFilePrefix(String) - Method in interface weka.core.converters.Saver: Sets the file prefix.
setFillWithMissing(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Sets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
setFilter(Filter) - Method in class weka.associations.FilteredAssociator: Sets the filter
setFilter(Filter) - Method in class weka.attributeSelection.FilteredAttributeEval: Set the filter to use
setFilter(Filter) - Method in class weka.attributeSelection.FilteredSubsetEval: Set the filter to use
setFilter(Filter) - Method in class weka.classifiers.functions.PLSClassifier: Set the PLS filter (only used for setup).
setFilter(Filter) - Method in class weka.classifiers.meta.FilteredClassifier: Sets the filter
setFilter(Filter) - Method in class weka.classifiers.meta.GridSearch: Set the kernel filter (only used for setup).
setFilter(Filter) - Method in class weka.clusterers.FilteredClusterer: Sets the filter.
setFilter(Filter) - Method in class weka.filters.CheckSource: Sets the filter to use for the comparison.
setFilter(Filter) - Method in class weka.filters.unsupervised.attribute.Wavelet: Set the preprocessing filter (only used for setup).
setFilter(Filter) - Method in class weka.gui.beans.Filter: Set the filter to be wrapped by this bean
setFilterAfterFirstBatch(boolean) - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Set whether to apply the filter to instances that arrive once the first (training) batch has been seen.
setFilterAttributes(String) - Method in class weka.associations.GeneralizedSequentialPatterns: Sets the String containing the attributes which are used for output filtering.
setFilters(Filter[]) - Method in class weka.filters.MultiFilter: Sets the list of possible filters to choose from.
setFilters(Filter[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Sets the list of possible filters to choose from.
setFilterType(SelectedTag) - Method in class weka.attributeSelection.SVMAttributeEval: The filtering mode to pass to SMO
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.GaussianProcesses: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.SMO: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.SMOreg: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MDD: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIDD: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIEMDD: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MIOptimalBall: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MISMO: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.mi.MISVM: Sets how the training data will be transformed.
setFindAllRulesForSupportLevel(boolean) - Method in class weka.associations.FPGrowth: If true then turn off the iterative support reduction method of finding x rules that meet the minimum support and metric thresholds and just return all the rules that meet the lower bound on minimum support and the minimum metric.
setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize: Set the value of FindNumBins.
setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Set the value of FindNumBins.
setFirstValueIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Sets index of the first value used.
setFirstValueIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues: Sets index of the first value used.
setFlow(Vector) - Method in class weka.gui.beans.KnowledgeFlowApp: Set the flow for the KnowledgeFlow to edit.
setFlows(Vector) - Method in class weka.gui.beans.FlowRunner: Set the vector holding the flows(s) to run
setFocus() - Method in class weka.gui.sql.ConnectionPanel: sets the focus in a designated control.
setFocus() - Method in class weka.gui.sql.InfoPanel: sets the focus in a designated control
setFocus() - Method in class weka.gui.sql.QueryPanel: sets the focus in a designated control.
setFocus() - Method in class weka.gui.sql.ResultPanel: sets the focus in a designated control
setFold(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Selects a fold.
setFold(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Selects a fold.
setFoldColumn(int) - Method in class weka.experiment.PairedTTester: Set the value of FoldColumn.
setFoldColumn(int) - Method in interface weka.experiment.Tester: Set the value of FoldColumn.
setFolds(int) - Method in class weka.attributeSelection.AttributeSelection: set the number of folds for cross validation
setFolds(int) - Method in class weka.attributeSelection.OneRAttributeEval: Set the number of folds to use for cross validation
setFolds(int) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the number of folds to use for accuracy estimation
setFolds(int) - Method in class weka.classifiers.rules.ConjunctiveRule: the number of folds to use
setFolds(int) - Method in class weka.classifiers.rules.JRip: Sets the number of folds to use
setFolds(int) - Method in class weka.classifiers.rules.Ridor
setFolds(int) - Method in class weka.gui.beans.CrossValidationFoldMaker: Set the number of folds for the cross validation
setFoldsType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch: Set the xfold type
setFont(Font) - Method in class weka.gui.visualize.PostscriptGraphics: Set current font.
setFormat(String) - Method in class weka.core.Debug.Timestamp: sets the format for the timestamp
setForwardSelectionMethod(SelectedTag) - Method in class weka.attributeSelection.LinearForwardSelection: Set the search direction
setFrequencyLimit(int) - Method in class weka.classifiers.bayes.AODE: Sets the frequency limit
setFrequencyLimit(int) - Method in class weka.classifiers.bayes.AODEsr: Sets the frequency limit
setFrequencyThreshold(double) - Method in class weka.associations.Tertius: Set the value of frequencyThreshold.
setFunction(SelectedTag) - Method in class weka.datagenerators.classifiers.classification.Agrawal: Sets the function for generating the data.
setFunctionValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Sets a particular function value
setGamma(double) - Method in class weka.classifiers.functions.LibSVM: Sets gamma (default = 1/no of attributes)
setGamma(double) - Method in class weka.classifiers.functions.supportVector.RBFKernel: Sets the gamma value.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.GreedyStepwise: Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.RaceSearch: Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean) - Method in interface weka.attributeSelection.RankedOutputSearch: Sets whether or not ranking is to be performed.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.Ranker: This is a dummy set method---Ranker is ONLY capable of producing a ranked list of attributes for attribute evaluators.
setGenerator(DataGenerator) - Method in class weka.gui.explorer.DataGeneratorPanel: sets the generator to use initially
setGeneratorSamplesBase(double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the base for computing the number of samples to obtain from each generator.
setGeneratorSamplesBase(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the base for computing the number of samples to obtain from each generator.
setGlobalBlend(int) - Method in class weka.classifiers.lazy.KStar: Set the global blend parameter
setGlobalModel(NBTreeNoSplit) - Method in class weka.classifiers.trees.j48.NBTreeSplit: Set the global naive bayes model for this node
setGridIsExtendable(boolean) - Method in class weka.classifiers.meta.GridSearch: Set whether the grid can be extended dynamically.
setGridWidth(int) - Method in class weka.gui.beans.AttributeSummarizer: Set the width of the grid of plots
setGroupIdentifier(long) - Method in class weka.gui.beans.BatchClassifierEvent
setGUI(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron: This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.
setGUIType(SelectedTag) - Method in class weka.gui.Main: Sets the type of GUI to use.
setHandler(CapabilitiesHandler) - Method in class weka.core.FindWithCapabilities: sets the Capabilities handler to generate the data for.
setHandler(CapabilitiesHandler) - Method in class weka.core.TestInstances: sets the Capabilities handler to generate the data for
setHandleRightClicks(boolean) - Method in class weka.gui.ResultHistoryPanel: Set whether the result history list should handle right clicks or whether the parent object will handle them.
setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Set hashtable from END.
setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Set hashtable from END.
setHashtable(Hashtable) - Method in class weka.classifiers.meta.nestedDichotomies.ND: Set hashtable from END.
setHDRank(int) - Method in class weka.classifiers.mi.CitationKNN: Sets the rank associated to the Hausdorff distance
setHeuristic(boolean) - Method in class weka.classifiers.trees.BFTree: Set if use heuristic search for nominal attributes in multi-class problems.
setHeuristic(boolean) - Method in class weka.classifiers.trees.SimpleCart: Set if use heuristic search for nominal attributes in multi-class problems.
setHeuristicStop(int) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of heuristicStop.
setHeuristicStop(int) - Method in class weka.classifiers.trees.lmt.LogisticBase: Sets the option "heuristicStop".
setHidden(boolean) - Method in class weka.gui.beans.BeanConnection: Make this connection invisible on the display
setHiddenLayers(String) - Method in class weka.classifiers.functions.MultilayerPerceptron: This will set what the hidden layers are made up of when auto build is enabled.
setHighlight(String) - Method in class weka.gui.treevisualizer.TreeVisualizer: Set the highlight for the node with the given id
setHistory(DefaultListModel) - Method in class weka.gui.sql.ConnectionPanel: sets the local history to the given one.
setHistory(DefaultListModel) - Method in class weka.gui.sql.QueryPanel: sets the local history to the given one.
setHoldOutFile(File) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set the file that contains hold out/test instances
setHornClauses(boolean) - Method in class weka.associations.Tertius: Set the value of hornClauses.
setHyperparameterRange(String) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the range of hyperparameter values to consider during CV-based selection
setHyperparameterSelection(SelectedTag) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the method used to select the hyperparameter
setHyperparameterValue(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the hyperparameter value.
setID(int) - Method in class weka.core.Tag: Sets the numeric ID of the Tag.
setIDFTransform(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
setIDIndex(String) - Method in class weka.filters.unsupervised.attribute.AddID: Sets index of the attribute used.
setIDStr(String) - Method in class weka.core.Tag: Sets the string ID of the Tag.
setIgnoreClass(boolean) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Set the IgnoreClass value.
setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AddCluster: Sets the ranges of attributes to be ignored.
setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Sets the ranges of attributes to be ignored.
setIgnoredProperties(String) - Method in class weka.core.CheckGOE: Sets the properties to ignore in checkToolTips().
setIgnoreRange(String) - Method in class weka.filters.unsupervised.attribute.MathExpression: Set which attributes are to be ignored
setIncludeClass(boolean) - Method in class weka.core.InstanceComparator: sets whether the class should be included (= TRUE) in the comparison
setIncludeClass(boolean) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Sets whether the class can be cleaned, too.
setIndex(int) - Method in class weka.core.pmml.MiningFieldMetaInfo: Set the index of this field in the mining schema Instances
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Sets whether to init as naive bayes
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.global.K2: Sets whether to init as naive bayes
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Sets whether to init as naive bayes
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.net.search.local.K2: Sets whether to init as naive bayes
setInitFile(File) - Method in class weka.filters.unsupervised.attribute.KernelFilter: Sets the file to initialize the filter with, can be null.
setInitFileClassIndex(String) - Method in class weka.filters.unsupervised.attribute.KernelFilter: Sets class index of the file to initialize the filter with.
setInitialAnchorRandom(boolean) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Sets whether if the initial anchor is chosen randomly.
setInputCenterFile(File) - Method in class weka.clusterers.XMeans: Sets the file to read the list of centers from.
setInputFilename(String) - Method in class weka.gui.GenericPropertiesCreator: sets the file to get the information about the packages from.
setInputFormat(Instances) - Method in class weka.filters.AllFilter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.Filter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.SimpleFilter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.ClassOrder: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.Discretize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.NominalToBinary: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.Resample: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.SMOTE: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Add: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddExpression: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddID: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Center: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Copy: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MathExpression: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NominalToString: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Normalize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Obfuscate: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Remove: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveType: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Reorder: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Standardize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToNominal: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SwapValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.NonSparseToSparse: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Normalize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Randomize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveRange: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Resample: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.ReservoirSample: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.SparseToNonSparse: Sets the format of the input instances.
setInputOrder(SelectedTag) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the input order.
setInputs(Vector) - Method in class weka.gui.beans.MetaBean
setInstance(Instance) - Method in class weka.gui.beans.InstanceEvent: Set the instance
setInstanceIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Changes the boolean value at the specified index in the InstIndexes array
setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.BallSplitter: Sets the master index array containing indices of the training instances.
setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Sets the master index array that points to instances in m_Instances, so that only this array is manipulated, and m_Instances is left untouched.
setInstanceList(int[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Sets the master index array that points to instances in m_Instances, so that only this array is manipulated, and m_Instances is left untouched.
setInstanceList(int[]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Sets the master index array containing indices of the training instances.
setInstanceRange(int) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Sets the number of instances forward to translate values between.
setInstances(Instances) - Method in class weka.core.converters.AbstractSaver: Sets instances that should be stored.
setInstances(Instances) - Method in class weka.core.converters.LibSVMSaver: Sets instances that should be stored.
setInstances(Instances) - Method in interface weka.core.converters.Saver: Sets the instances to be saved
setInstances(Instances) - Method in class weka.core.converters.SVMLightSaver: Sets instances that should be stored.
setInstances(Instances) - Method in class weka.core.converters.XRFFSaver: Sets instances that should be stored.
setInstances(Instances) - Method in interface weka.core.DistanceFunction: Sets the instances.
setInstances(Instances) - Method in class weka.core.neighboursearch.BallTree: Builds the BallTree based on the given set of instances.
setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.BallSplitter: Sets the training instances on which the tree is (or is to be) built.
setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Sets the instances on which the tree is to be built.
setInstances(Instances) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Sets the instances on which the tree is to be built.
setInstances(Instances) - Method in class weka.core.neighboursearch.CoverTree: Builds the Cover Tree on the given set of instances.
setInstances(Instances) - Method in class weka.core.neighboursearch.KDTree: Builds the KDTree on the given set of instances.
setInstances(Instances) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Sets the training instances on which the tree is (or is to be) built.
setInstances(Instances) - Method in class weka.core.neighboursearch.LinearNNSearch: Sets the instances comprising the current neighbourhood.
setInstances(Instances) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Sets the instances.
setInstances(Instances) - Method in class weka.core.NormalizableDistance: Sets the instances.
setInstances(Instances) - Method in class weka.core.xml.XMLInstances: builds up the XML structure based on the given data
setInstances(Instances) - Method in class weka.experiment.AveragingResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.CrossValidationResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.DatabaseResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.LearningRateResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.PairedTTester: Set the value of Instances.
setInstances(Instances) - Method in class weka.experiment.RandomSplitResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in interface weka.experiment.ResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in interface weka.experiment.Tester: Set the value of Instances.
setInstances(Instances) - Method in class weka.gui.arffviewer.ArffPanel: displays the given instances, i.e.
setInstances(Instances) - Method in class weka.gui.arffviewer.ArffSortedTableModel: sets the data
setInstances(Instances) - Method in class weka.gui.arffviewer.ArffTableModel: sets the data
setInstances(Instances) - Method in class weka.gui.AttributeListPanel: Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.AttributeSelectionPanel: Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.AttributeSummaryPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.AttributeVisualizationPanel: Sets the instances for use
setInstances(Instances) - Method in class weka.gui.beans.AttributeSummarizer: Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.beans.DataVisualizer: Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.beans.ScatterPlotMatrix: Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Set the training instances
setInstances(Instances) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the training data
setInstances(Instances) - Method in class weka.gui.experiment.ResultsPanel: Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.
setInstances(Instances) - Method in class weka.gui.explorer.AssociationsPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.ClassifierPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.ClustererPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in interface weka.gui.explorer.Explorer.ExplorerPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.PreprocessPanel: Tells the panel to use a new base set of instances.
setInstances(Instances) - Method in class weka.gui.InstancesSummaryPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.SetInstancesPanel: Updates the set of instances that is currently held by the panel
setInstances(Instances) - Method in class weka.gui.ViewerDialog: sets the instances to display
setInstances(Instances) - Method in class weka.gui.visualize.AttributePanel: This sets the instances to be drawn into the attribute panel
setInstances(Instances) - Method in class weka.gui.visualize.ClassPanel: Set the instances.
setInstances(Instances) - Method in class weka.gui.visualize.MatrixPanel: This method changes the Instances object of this class to a new one.
setInstances(Instances) - Method in class weka.gui.visualize.Plot2D: Sets the master plot from a set of instances
setInstances(Instances) - Method in class weka.gui.visualize.VisualizePanel: Tells the panel to use a new set of instances.
setInstancesFromDB(InstanceQuery) - Method in class weka.gui.explorer.PreprocessPanel: Loads instances from a database
setInstancesFromDBQ(String, String, String, String) - Method in class weka.gui.explorer.PreprocessPanel: Loads instances from an SQL query the user provided with the SqlViewerDialog, then loads the instances in a background process.
setInstancesFromFile(AbstractFileLoader) - Method in class weka.gui.explorer.PreprocessPanel: Loads results from a set of instances retrieved with the supplied loader.
setInstancesFromFileQ() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromFileQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromFileQ() - Method in class weka.gui.SetInstancesPanel: Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromURL(URL) - Method in class weka.gui.explorer.PreprocessPanel: Loads instances from a URL.
setInstancesFromURLQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesFromURLQ() - Method in class weka.gui.SetInstancesPanel: Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesIndices(String) - Method in class weka.filters.unsupervised.instance.RemoveRange: Sets the ranges of instances to be selected.
SetInstancesPanel - Class in weka.gui: A panel that displays an instance summary for a set of instances and lets the user open a set of instances from either a file or URL.
SetInstancesPanel() - Constructor for class weka.gui.SetInstancesPanel: Default constructor
SetInstancesPanel(boolean, ConverterFileChooser) - Constructor for class weka.gui.SetInstancesPanel: Create the panel.
setInsts(int[], boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Changes the boolean value at the specified index in the InstIndexes array
setInterAnchorDistances(Vector, MiddleOutConstructor.TempNode, Vector) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Sets the distances of a supplied new anchor to all the rest of the previous anchor points.
setInternalCacheSize(int) - Method in class weka.classifiers.functions.supportVector.StringKernel: sets the size of the internal cache for intermediate results.
setInternals(boolean) - Method in class weka.classifiers.bayes.WAODE: Sets whether internals about classifier are printed via toString().
setInvert(boolean) - Method in class weka.core.Range: Sets whether the range sense is inverted, i.e.
setInvert(boolean) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Set whether selection is inverted.
setInvertSelection(boolean) - Method in interface weka.core.DistanceFunction: Sets whether the matching sense of attribute indices is inverted or not.
setInvertSelection(boolean) - Method in class weka.core.NormalizableDistance: Sets whether the matching sense of attribute indices is inverted or not.
setInvertSelection(boolean) - Method in class weka.filters.supervised.attribute.Discretize: Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.supervised.instance.Resample: Sets whether the selection is inverted (only if instances are drawn WIHTOUT replacement).
setInvertSelection(boolean) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Copy: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.MathExpression: Set whether selected columns should be select or unselect.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Sets whether the selection of the indices is inverted or not
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Sets whether selected columns should be worked on or all the others apart from these.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Set whether selected columns should be transformed or not.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.RELAGGS: Sets whether selected columns should be processed or skipped.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Remove: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.RemoveType: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether selected columns should be processed or skipped.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Set whether selected values should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveRange: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Set whether selected values should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.Resample: Sets whether the selection is inverted (only if instances are drawn WIHTOUT replacement).
setItem(int[]) - Method in class weka.associations.ItemSet: Sets an item sets
setItemAt(int, int) - Method in class weka.associations.ItemSet: Sets the index of an attribute value
setJitter(int) - Method in class weka.gui.visualize.Plot2D: Set level of jitter and repaint the plot using the new jitter value
setKDTree(KDTree) - Method in class weka.clusterers.XMeans: Sets the KDTree class.
setKernel(Kernel) - Method in class weka.classifiers.functions.GaussianProcesses: Sets the kernel to use.
setKernel(Kernel) - Method in class weka.classifiers.functions.SMO.BinarySMO: sets the kernel to use
setKernel(Kernel) - Method in class weka.classifiers.functions.SMO: sets the kernel to use
setKernel(Kernel) - Method in class weka.classifiers.functions.SMOreg: sets the kernel to use
setKernel(Kernel) - Method in class weka.classifiers.functions.supportVector.CheckKernel: Set the lernel to test.
setKernel(Kernel) - Method in class weka.classifiers.mi.MISMO: Sets the kernel to use.
setKernel(Kernel) - Method in class weka.classifiers.mi.MISVM: Sets the kernel to use.
setKernel(Kernel) - Method in class weka.filters.unsupervised.attribute.KernelFilter: Sets the kernel to use.
setKernelBandwidth(int) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Set the kernel bandwidth (number of nearest neighbours to cover)
setKernelFactorExpression(String) - Method in class weka.filters.unsupervised.attribute.KernelFilter: Sets the expression for the kernel.
setKernelMatrixFile(File) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Sets the file holding the kernel matrix
setKernelType(SelectedTag) - Method in class weka.classifiers.functions.LibSVM: Sets type of kernel function (default KERNELTYPE_RBF)
setKey(String) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Sets the key for this DataObject
setKey(String) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Sets the key for this DataObject
setKey(String) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Sets the key for this DataObject
setKeyFieldName(String) - Method in class weka.experiment.AveragingResultProducer: Set the value of KeyFieldName.
setKeys(String) - Method in class weka.core.converters.DatabaseLoader: Sets the key columns of a database table
setKeywords(String) - Method in class weka.experiment.DatabaseUtils: Sets the keywords (comma-separated list) to use.
setKeywordsMaskChar(String) - Method in class weka.experiment.DatabaseUtils: Sets the mask character to append to table or attribute names that are a reserved keyword.
setKNN(int) - Method in class weka.classifiers.lazy.IBk: Set the number of neighbours the learner is to use.
setKNN(int) - Method in class weka.classifiers.lazy.LWL: Sets the number of neighbours used for kernel bandwidth setting.
setKValue(int) - Method in class weka.classifiers.trees.RandomTree: Set the value of K.
setLabels(String) - Method in class weka.filters.unsupervised.attribute.AddValues: Sets the comma-separated list of labels.
setLambda(double) - Method in class weka.classifiers.functions.SPegasos: Set the value of lambda to use
setLambda(double) - Method in class weka.classifiers.functions.supportVector.StringKernel: Sets the lambda constant used in the string kernel
setLearningRate(double) - Method in class weka.classifiers.functions.MultilayerPerceptron: The learning rate can be set using this command.
setLegendText(Vector) - Method in class weka.gui.beans.ChartEvent: Set the legend text vector
setLikelihoodThreshold(double) - Method in class weka.classifiers.meta.LogitBoost: Set the value of Precision.
setLinkType(SelectedTag) - Method in class weka.clusterers.HierarchicalClusterer
setListData(Object[]) - Method in class weka.gui.CheckBoxList: Constructs a CheckBoxListModel from an array of objects and then applies setModel to it.
setListData(Vector) - Method in class weka.gui.CheckBoxList: Constructs a CheckBoxListModel from a Vector and then applies setModel to it.
setLNorm(double) - Method in class weka.filters.unsupervised.instance.Normalize: Set the L-norm to used
setLoader(Loader) - Method in class weka.gui.beans.Loader: Set the loader to use
setLocallyPredictive(boolean) - Method in class weka.attributeSelection.CfsSubsetEval: Include locally predictive attributes
setLocationProbs(int, double[]) - Method in class weka.gui.boundaryvisualizer.RemoteResult: Store the classifier's distribution for a particular pixel in the visualization
setLog(Logger) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Set a logger to use.
setLog(Debug.Log) - Method in class weka.core.Debug.Random: the log to use, if it is null then stdout is used
setLog(Logger) - Method in interface weka.core.pmml.PMMLModel: Set a logger to use.
setLog(Logger) - Method in class weka.gui.beans.AbstractDataSink: Set a log for this bean
setLog(Logger) - Method in class weka.gui.beans.AbstractEvaluator: Set a logger
setLog(Logger) - Method in class weka.gui.beans.AbstractTestSetProducer: Set a logger
setLog(Logger) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Set a log for this bean
setLog(Logger) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Set a logger
setLog(Logger) - Method in class weka.gui.beans.Associator: Set a logger
setLog(Logger) - Method in interface weka.gui.beans.BeanCommon: Set a logger
setLog(Logger) - Method in class weka.gui.beans.ClassAssigner
setLog(Logger) - Method in class weka.gui.beans.Classifier: Set a logger
setLog(Logger) - Method in class weka.gui.beans.ClassValuePicker
setLog(Logger) - Method in class weka.gui.beans.Clusterer: Set a logger
setLog(Logger) - Method in class weka.gui.beans.CostBenefitAnalysis: Set a logger
setLog(Logger) - Method in class weka.gui.beans.Filter: Set a logger
setLog(Logger) - Method in class weka.gui.beans.FlowRunner
setLog(Logger) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Set a logger
setLog(Logger) - Method in class weka.gui.beans.Loader: Set a logger
setLog(Logger) - Method in interface weka.gui.beans.LogWriter: Set a logger
setLog(Logger) - Method in class weka.gui.beans.MetaBean: Set a logger
setLog(Logger) - Method in class weka.gui.beans.PredictionAppender: Set a logger
setLog(Logger) - Method in class weka.gui.beans.SerializedModelSaver: Set a log for this bean.
setLog(Logger) - Method in class weka.gui.beans.StripChart: Set a logger
setLog(Logger) - Method in class weka.gui.beans.TextViewer: Set a logger
setLog(Logger) - Method in class weka.gui.explorer.AssociationsPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.AttributeSelectionPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.ClassifierPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.ClustererPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.DataGeneratorPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in interface weka.gui.explorer.Explorer.LogHandler: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.PreprocessPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.visualize.VisualizePanel: Sets the Logger to receive informational messages
setLogFile(File) - Method in class weka.classifiers.meta.GridSearch: Sets the log file to use.
setLookAndFeel(String) - Static method in class weka.gui.LookAndFeel: sets the look and feel to the specified class
setLookAndFeel() - Static method in class weka.gui.LookAndFeel: sets the look and feel to the one in the props-file or if not set the default one of the system
setLookupCacheSize(int) - Method in class weka.attributeSelection.BestFirst: Set the maximum size of the evaluated subset cache (hashtable).
setLookupCacheSize(int) - Method in class weka.attributeSelection.LinearForwardSelection: Set the maximum size of the evaluated subset cache (hashtable).
setLookupCacheSize(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Set the maximum size of the evaluated subset cache (hashtable).
setLoss(double) - Method in class weka.classifiers.functions.LibSVM: Sets the epsilon in loss function of epsilon-SVR (default 0.1)
setLossFunction(SelectedTag) - Method in class weka.classifiers.functions.SPegasos: Set the loss function to use.
setLowerBoundMinSupport(double) - Method in class weka.associations.Apriori: Set the value of lowerBoundMinSupport.
setLowerBoundMinSupport(double) - Method in class weka.associations.FPGrowth: Set the value of lowerBoundMinSupport.
setLowerCaseTokens(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the tokens are to be downcased or not.
setLowerSize(int) - Method in class weka.experiment.LearningRateResultProducer: Set the value of LowerSize.
setMajorityClass(boolean) - Method in class weka.classifiers.rules.Ridor
setMakeBinary(boolean) - Method in class weka.filters.supervised.attribute.Discretize: Sets whether binary attributes should be made for discretized ones.
setMakeBinary(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets whether binary attributes should be made for discretized ones.
setManualThresholdValue(double) - Method in class weka.classifiers.meta.ThresholdSelector: Sets the value for a manual threshold.
setMargin(int, double[]) - Method in class weka.classifiers.bayes.net.EditableBayesNet: set marginal distibution for a node
setMarkovBlanketClassifier(boolean) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
setMarkovBlanketClassifier(boolean) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D: Set the master plot.
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel: Set the master plot for the visualize panel
setMatchMissingValues(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Sets whether missing values are counted as a match.
setMatrix(int, int, int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the submatrix A[i0:i1][j0:j1] with a same value
setMatrix(int, int, int, DoubleVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the submatrix A[i0:i1][j] with the values stored in a DoubleVector
setMatrix(double[], boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the whole matrix from a 1-D array
setMatrix(int, int, int, int, Matrix) - Method in class weka.core.matrix.Matrix: Set a submatrix.
setMatrix(int[], int[], Matrix) - Method in class weka.core.matrix.Matrix: Set a submatrix.
setMatrix(int[], int, int, Matrix) - Method in class weka.core.matrix.Matrix: Set a submatrix.
setMatrix(int, int, int[], Matrix) - Method in class weka.core.matrix.Matrix: Set a submatrix.
setMax(double) - Method in class weka.gui.beans.ChartEvent: Set the max y value
setMaxBoostingIterations(int) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of maxBoostingIterations.
setMaxCardinality(int) - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: sets the cardinality
setMaxCardinality(int) - Method in class weka.filters.unsupervised.attribute.RELAGGS: Sets the maximum number of values allowed for nominal attributes, before they're skipped.
setMaxChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the maximum chunk size
setMaxCount(double) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets the value for the max count
setMaxDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Set the naximum default.
setMaxDepth(int) - Method in class weka.classifiers.trees.RandomForest: Set the maximum depth of the tree, 0 for unlimited.
setMaxDepth(int) - Method in class weka.classifiers.trees.RandomTree: Set the maximum depth of the tree, 0 for unlimited.
setMaxDepth(int) - Method in class weka.classifiers.trees.REPTree: Set the value of MaxDepth.
setMaxGenerations(int) - Method in class weka.attributeSelection.GeneticSearch: set the number of generations to evaluate
setMaxGridExtensions(int) - Method in class weka.classifiers.meta.GridSearch: Sets the maximum number of grid extensions, -1 for unlimited.
setMaxGroup(int) - Method in class weka.classifiers.meta.RotationForest: Sets the maximum size of a group.
setMaximumAttributeNames(int) - Method in class weka.attributeSelection.LatentSemanticAnalysis: Sets maximum number of attributes to include in transformed attribute names.
setMaximumAttributeNames(int) - Method in class weka.attributeSelection.PrincipalComponents: Sets maximum number of attributes to include in transformed attribute names.
setMaximumAttributeNames(int) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Sets maximum number of attributes to include in transformed attribute names.
setMaximumAttributes(int) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Sets maximum number of PC attributes to retain.
setMaximumVariancePercentageAllowed(double) - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Sets the maximum variance attributes are allowed to have before they are deleted by the filter.
setMaxInstancesInLeaf(int) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Sets the maximum number of instances allowed in a leaf.
setMaxInstancesInLeaf(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Sets the maximum number of instances allowed in a leaf.
setMaxInstInLeaf(int) - Method in class weka.core.neighboursearch.KDTree: Sets the maximum number of instances in a leaf.
setMaxInstNum(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the upper boundary for instances per cluster.
setMaxInstNum(int) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets the upper boundary for instances per cluster.
setMaxIteration(int) - Method in class weka.core.Optimization: Set the maximal number of iterations in searching (Default 200)
setMaxIterations(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the maximum number of iterations to perform
setMaxIterations(int) - Method in class weka.classifiers.mi.MIBoost: Set the maximum number of boost iterations
setMaxIterations(int) - Method in class weka.classifiers.mi.MISVM: Sets the maximum number of iterations.
setMaxIterations(int) - Method in class weka.classifiers.trees.lmt.LogisticBase: Sets the parameter "maxIterations".
setMaxIterations(int) - Method in class weka.clusterers.EM: Set the maximum number of iterations to perform
setMaxIterations(int) - Method in class weka.clusterers.sIB: Set the max number of iterations
setMaxIterations(int) - Method in class weka.clusterers.SimpleKMeans: set the maximum number of iterations to be executed
setMaxIterations(int) - Method in class weka.clusterers.XMeans: Sets the maximum number of iterations to perform.
setMaxIterations(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the maximum number of cleansing iterations to perform - < 1 means go until fully cleansed
setMaxIts(int) - Method in class weka.classifiers.functions.Logistic: Set the value of MaxIts.
setMaxIts(int) - Method in class weka.classifiers.functions.RBFNetwork: Set the value of MaxIts.
setMaxK(int) - Method in class weka.classifiers.functions.VotedPerceptron: Set the value of maxK.
setMaxKMeans(int) - Method in class weka.clusterers.XMeans: Set the maximum number of iterations to perform in KMeans.
setMaxKMeansForChildren(int) - Method in class weka.clusterers.XMeans: Sets the maximum number of iterations KMeans that is performed on the child centers.
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Sets the max number of parents
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.global.K2: Sets the max number of parents
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Sets the max number of parents
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.K2: Sets the max number of parents
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Sets the max number of parents
setMaxNumberOfItems(int) - Method in class weka.associations.FPGrowth: Set the maximum number of items to include in large items sets.
setMaxNumClusters(int) - Method in class weka.clusterers.XMeans: Sets the maximum number of clusters to generate.
setMaxPlots(int) - Method in class weka.gui.beans.AttributeSummarizer: Set the maximum number of plots to display
setMaxRadius(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the upper boundary for the radiuses of the clusters.
setMaxRange(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Sets the upper boundary for the range of x
setMaxRelativeLeafRadius(double) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Sets the maximum relative radius, allowed for a leaf node.
setMaxRows(int) - Method in class weka.gui.sql.QueryPanel: sets the maximum number of rows to display.
setMaxRuleSize(int) - Method in class weka.datagenerators.classifiers.classification.RDG1: Sets the maximum number of tests in rules.
setMaxSubsequenceLength(int) - Method in class weka.classifiers.functions.supportVector.StringKernel: Sets the maximum length of the subsequence.
setMaxThreshold(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Set the maximum threshold.
setMDLTheoryWeight(double) - Method in class weka.classifiers.rules.RuleStats: Set the weight of theory in MDL calcualtion
setMean(int, int, double) - Method in class weka.experiment.ResultMatrix: sets the mean at the given position (if the position is valid)
setMeanPrec(int) - Method in class weka.experiment.ResultMatrix: sets the precision for the means
setMeanPrec(int) - Method in class weka.gui.experiment.OutputFormatDialog: Sets the precision of the mean output.
setMeanSquared(boolean) - Method in class weka.classifiers.lazy.IBk: Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
setMeanStddev(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets mean and standarddeviation.
setMeanWidth(int) - Method in class weka.experiment.ResultMatrix: sets the width for the mean (0 = optimal)
setMeasure(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector: set measure used for determining threshold
setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.BallTree: Sets whether to calculate the performance statistics or not.
setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.KDTree: Sets whether to calculate the performance statistics or not.
setMeasurePerformance(boolean) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Sets whether to calculate the performance statistics or not.
setMestWeight(double) - Method in class weka.classifiers.bayes.AODEsr: Sets the weight for m-estimate
setMetaClassifier(Classifier) - Method in class weka.classifiers.meta.Stacking: Adds meta classifier
setMethod(NeuralMethod) - Method in class weka.classifiers.functions.neural.NeuralNode: Set how this node should operate (note that the neural method has no internal state, so the same object can be used by any number of nodes.
setMethod(SelectedTag) - Method in class weka.classifiers.meta.MultiClassClassifier: Sets the method used.
setMethod(SelectedTag) - Method in class weka.classifiers.mi.MIWrapper: Set the method used in testing.
setMethodName(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Set the transformation method.
setMetricType(SelectedTag) - Method in class weka.associations.Apriori: Set the metric type for ranking rules
setMetricType(SelectedTag) - Method in class weka.associations.FPGrowth: Set the metric type to use.
setMin(double) - Method in class weka.gui.beans.ChartEvent: Set the min y value
setMinBoxRelWidth(double) - Method in class weka.core.neighboursearch.KDTree: Sets the minimum relative box width.
setMinBucketSize(int) - Method in class weka.classifiers.rules.OneR: Set the value of minBucketSize.
setMinChange(int) - Method in class weka.clusterers.sIB: set the minimum number of changes
setMinChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the minimum chunk size
setMinDefault(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Set the minimum default.
setMinGroup(int) - Method in class weka.classifiers.meta.RotationForest: Sets the minimum size of a group.
setMinimax(boolean) - Method in class weka.classifiers.mi.MISMO: Set if the MIMinimax feature space is to be used.
setMinimizeExpectedCost(boolean) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Set the value of MinimizeExpectedCost.
setMinimumBucketSize(int) - Method in class weka.attributeSelection.OneRAttributeEval: Set the minumum bucket size used by OneR
setMinimumNumberInstances(int) - Method in class weka.core.Capabilities: sets the minimum number of instances that have to be in the dataset
setMinInstNum(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the lower boundary for instances per cluster.
setMinInstNum(int) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets the lower boundary for instances per cluster.
setMinMaxValues() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Sets the minimum and maximum values for each attribute in different arrays by walking through every DataObject of the database
setMinMaxValues() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Sets the minimum and maximum values for each attribute in different arrays by walking through every DataObject of the database
setMinMaxX(double, double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the minimum and maximum values of the x axis fixed dimension
setMinMaxY(double, double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the minimum and maximum values of the y axis fixed dimension
setMinMetric(double) - Method in class weka.associations.Apriori: Set the value of minConfidence.
setMinMetric(double) - Method in class weka.associations.FPGrowth: Set the value of minConfidence.
setMinNo(double) - Method in class weka.classifiers.rules.ConjunctiveRule: Sets the minimum total weight of the instances in a rule
setMinNo(double) - Method in class weka.classifiers.rules.JRip: Sets the minimum total weight of the instances in a rule
setMinNo(double) - Method in class weka.classifiers.rules.Ridor
setMinNum(double) - Method in class weka.classifiers.trees.RandomTree: Set the value of MinNum.
setMinNum(double) - Method in class weka.classifiers.trees.REPTree: Set the value of MinNum.
setMinNumClusters(int) - Method in class weka.clusterers.XMeans: Sets the minimum number of clusters to generate.
setMinNumInstances(int) - Method in class weka.classifiers.trees.FT: Set the value of minNumInstances.
setMinNumInstances(int) - Method in class weka.classifiers.trees.LMT: Set the value of minNumInstances.
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.M5Base: Set the minimum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.Rule: Set the minumum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.RuleNode: Set the minumum number of instances to allow at a leaf node
setMinNumObj(int) - Method in class weka.classifiers.rules.PART: Set the value of minNumObj.
setMinNumObj(int) - Method in class weka.classifiers.trees.BFTree: Set minimal number of instances at the terminal nodes.
setMinNumObj(int) - Method in class weka.classifiers.trees.J48: Set the value of minNumObj.
setMinNumObj(int) - Method in class weka.classifiers.trees.J48graft: Set the value of minNumObj.
setMinNumObj(double) - Method in class weka.classifiers.trees.SimpleCart: Set minimal number of instances at the terminal nodes.
setMinPoints(int) - Method in class weka.clusterers.DBSCAN: Sets a new value for minPoints
setMinPoints(int) - Method in class weka.clusterers.OPTICS: Sets a new value for minPoints
setMinRadius(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the lower boundary for the radiuses of the clusters.
setMinRange(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Sets the lower boundary for the range of x
setMinRuleSize(int) - Method in class weka.datagenerators.classifiers.classification.RDG1: Sets the minimum number of tests in rules.
setMinStdDev(double) - Method in class weka.classifiers.functions.RBFNetwork: Set the MinStdDev value.
setMinStdDev(double) - Method in class weka.clusterers.EM: Set the minimum value for standard deviation when calculating normal density.
setMinStdDev(double) - Method in class weka.clusterers.MakeDensityBasedClusterer: Set the minimum value for standard deviation when calculating normal density.
setMinStdDevPerAtt(double[]) - Method in class weka.clusterers.EM
setMinSupport(double) - Method in class weka.associations.GeneralizedSequentialPatterns: Sets the minimum support threshold.
setMinTermFreq(int) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Set the MinTermFreq value.
setMinThreshold(double) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Set the minimum threshold.
setMinVarianceProp(double) - Method in class weka.classifiers.trees.REPTree: Set the value of MinVarianceProp.
setMissing(int) - Method in class weka.core.Instance: Sets a specific value to be "missing".
setMissing(Attribute) - Method in class weka.core.Instance: Sets a specific value to be "missing".
setMissingMerge(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.GainRatioAttributeEval: distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval: distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: distribute the counts for missing values across observed values
setMissingMode(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Set the missing value mode.
setMissingMode(SelectedTag) - Method in class weka.classifiers.lazy.KStar: Sets the method to use for handling missing values.
setMissingSeparate(boolean) - Method in class weka.attributeSelection.CfsSubsetEval: Treat missing as a separate value
setMissingValue(String) - Method in class weka.core.converters.CSVLoader: Sets the placeholder for missing values.
setMissingValues(SelectedTag) - Method in class weka.associations.Tertius: Set the value of missingValues.
setMixingDistribution(DiscreteFunction) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Sets the mixing distribution
setModel(Classifier) - Method in class weka.classifiers.misc.SerializedClassifier: Sets the fully built model to use, if one doesn't want to load a model from a file or already deserialized a model from somewhere else.
setModel(TableModel) - Method in class weka.gui.arffviewer.ArffTable: sets the new model
setModel(ListModel) - Method in class weka.gui.CheckBoxList: sets the model - must be an instance of CheckBoxListModel
setModel(TableModel) - Method in class weka.gui.SortedTableModel: sets the model to use
setModelFile(File) - Method in class weka.classifiers.misc.SerializedClassifier: Sets the file containing the serialized model.
setModelType(SelectedTag) - Method in class weka.classifiers.trees.FT: Set the Functional Tree type.
setModePanel(SetupModePanel) - Method in class weka.gui.experiment.SimpleSetupPanel: Sets the panel used to switch between simple and advanced modes.
setModifyHeader(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Sets whether the header will be modified when selecting on nominal attributes.
setModifyHeader(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Sets whether the header will be modified when selecting on nominal attributes.
setMomentum(double) - Method in class weka.classifiers.functions.MultilayerPerceptron: The momentum can be set using this command.
setMultiInstance(boolean) - Method in class weka.core.TestInstances: sets whether multi-instance data should be generated (with a fixed data structure)
setMultinomialWord(boolean) - Method in class weka.classifiers.bayes.DMNBtext: Sets whether use binary text representation
setMutationProb(double) - Method in class weka.attributeSelection.GeneticSearch: set the probability of mutation
setName(String) - Method in class weka.filters.unsupervised.attribute.AddExpression: Set the name for the new attribute.
setName(String) - Method in class weka.gui.visualize.VisualizePanel: Set a name for this plot
setNearestNeighbors(int) - Method in class weka.filters.supervised.instance.SMOTE: Sets the number of nearest neighbors to use.
setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch) - Method in class weka.classifiers.lazy.IBk: Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch) - Method in class weka.classifiers.lazy.LWL: Sets the nearestNeighbourSearch algorithm to be used for finding nearest neighbour(s).
setNegation(Literal) - Method in class weka.associations.tertius.Literal
setNegation(SelectedTag) - Method in class weka.associations.Tertius: Set the value of negation.
setNewToolTip(String) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Displays a toolTip for the selected DataObject
setNGramMaxSize(int) - Method in class weka.core.tokenizers.NGramTokenizer: Sets the max size of the Ngram.
setNGramMinSize(int) - Method in class weka.core.tokenizers.NGramTokenizer: Sets the min size of the Ngram.
setNoClass(boolean) - Method in class weka.core.TestInstances: whether to have no class, e.g., for clusterers; otherwise the class attribute index is set to last
setNodeName(int, String) - Method in class weka.classifiers.bayes.net.EditableBayesNet: change the name of a node
setNodesEdges(FastVector, FastVector) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Sets the nodes and edges for this LayoutEngine.
setNodesEdges(FastVector, FastVector) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method sets the nodes and edges vectors of the LayoutEngine
setNodeSize(int, int) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Sets the size of a node.
setNodeSize(int, int) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method sets the allowed size of the node
setNodeSplitter(KDTreeNodeSplitter) - Method in class weka.core.neighboursearch.KDTree: Sets the splitting method to use to split the nodes of the KDTree.
setNodeWidthNormalization(boolean) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Sets whether if a nodes region is normalized or not.
setNoise(double) - Method in class weka.classifiers.functions.GaussianProcesses: Set the level of Gaussian Noise.
setNoisePercent(double) - Method in class weka.datagenerators.classifiers.classification.LED24: Sets the noise percentage.
setNoiseRate(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Sets the gaussian noise rate.
setNoiseRate(double) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the percentage of noise set.
setNoiseRate(double) - Method in class weka.datagenerators.clusterers.SubspaceCluster: Sets the percentage of noise set.
setNoiseThreshold(double) - Method in class weka.associations.Tertius: Set the value of noiseThreshold.
setNoiseVariance(double) - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Sets the noise variance
setNominalAttributes(String) - Method in class weka.core.converters.CSVLoader: Sets the attribute range to be forced to type nominal.
setNominalCols(Range) - Method in class weka.datagenerators.ClusterGenerator: Sets which attributes are nominal.
setNominalIndices(String) - Method in class weka.datagenerators.ClusterGenerator: Sets which attributes are nominal
setNominalIndices(String) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Set which nominal labels are to be included in the selection.
setNominalIndicesArr(int[]) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Set which values of a nominal attribute are to be used for selection.
setNominalLabels(String) - Method in class weka.filters.unsupervised.attribute.Add: Set the labels for nominal attribute creation.
setNominalToBinaryFilter(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
setNoPruning(boolean) - Method in class weka.classifiers.trees.REPTree: Set the value of NoPruning.
setNoReplacement(boolean) - Method in class weka.filters.supervised.instance.Resample: Sets whether instances are drawn with or with out replacement.
setNoReplacement(boolean) - Method in class weka.filters.unsupervised.instance.Resample: Sets whether instances are drawn with or with out replacement.
setNorm(double) - Method in class weka.filters.unsupervised.instance.Normalize: Set the norm of the instances
setNormalize(boolean) - Method in class weka.attributeSelection.LatentSemanticAnalysis: Set whether input data will be normalized.
setNormalize(boolean) - Method in class weka.classifiers.functions.LibLINEAR: whether to normalize input data
setNormalize(boolean) - Method in class weka.classifiers.functions.LibSVM: whether to normalize input data
setNormalizeAttributes(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
setNormalizeData(boolean) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set whether to normalize the data or not
setNormalizeDimWidths(boolean) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Should we normalize the widths(ranges) of the dimensions (attributes) before selecting the widest one.
setNormalizeDocLength(SelectedTag) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the word frequencies for a document (instance) should be normalized or not.
setNormalizeNodeWidth(boolean) - Method in class weka.core.neighboursearch.KDTree: Sets the flag for normalizing the widths of a KDTree Node by the width of the dimension in the universe.
setNormalizeNumericClass(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
setNormalizeWordWeights(boolean) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Sets whether if the word weights for each class should be normalized
setNotCapabilities(Capabilities) - Method in class weka.core.FindWithCapabilities: Uses the given "not to have" Capabilities for the search.
setNotes(String) - Method in class weka.experiment.Experiment: Set the user notes.
setNotes(String) - Method in class weka.experiment.RemoteExperiment: Set the user notes.
setNotificationEnabled(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel: sets whether the notification of changes is enabled
setNotificationEnabled(boolean) - Method in class weka.gui.arffviewer.ArffTableModel: sets whether the notification of changes is enabled
setNotUnifyNorm(boolean) - Method in class weka.clusterers.sIB: Set whether to normalize instances to unify prior probability before building the clusterer
setNrOfGoodOperations(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Sets the number of "good operations"
setNrOfLookAheadSteps(int) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Sets the number of look-ahead steps
setNu(double) - Method in class weka.classifiers.functions.LibSVM: Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
setNumAllConds(double) - Method in class weka.classifiers.rules.RuleStats: Set the number of all conditions that could appear in a rule in this RuleStats object, if the number set is smaller than 0 (typically -1), then it calcualtes based on the data store
setNumAntds(int) - Method in class weka.classifiers.rules.ConjunctiveRule: Sets the number of antecedants
setNumArcs(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet: Sets the number of arcs for the bayesian net
setNumAttemptsOfGeneOption(int) - Method in class weka.classifiers.rules.NNge: Sets the number of attempts for generalisation.
setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet: Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.classifiers.classification.RDG1: Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.clusterers.SubspaceCluster: Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.ClusterGenerator: Sets the number of attributes the dataset should have.
setNumAttributes(double) - Method in class weka.filters.unsupervised.attribute.RandomSubset: Set the number of attributes.
setNumberLiterals(int) - Method in class weka.associations.Tertius: Set the value of numberLiterals.
setNumberOfAttributes(int) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the number of attributes (dimensions) the data should be reduced to
setNumberOfGroups(boolean) - Method in class weka.classifiers.meta.RotationForest: Set whether minGroup and maxGroup refer to the number of groups or their size
setNumBins(int) - Method in class weka.classifiers.meta.RegressionByDiscretization: Sets the number of bins to divide each selected numeric attribute into
setNumBoostingIterations(int) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of numBoostingIterations.
setNumBoostingIterations(int) - Method in class weka.classifiers.trees.FT: Set the value of numBoostingIterations.
setNumBoostingIterations(int) - Method in class weka.classifiers.trees.LMT: Set the value of numBoostingIterations.
setNumCentroids(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Sets the number of centroids to use.
setNumCiters(int) - Method in class weka.classifiers.mi.CitationKNN: Sets the number of citers considered to estimate the class prediction of tests bags
setNumClasses(int) - Method in class weka.core.TestInstances: sets the number of classes
setNumClasses(int) - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Sets the number of classes the dataset should have.
setNumClasses(int) - Method in class weka.datagenerators.classifiers.classification.RDG1: Sets the number of classes the dataset should have.
setNumClusters(int) - Method in class weka.classifiers.functions.RBFNetwork: Set the number of clusters for K-means to generate.
setNumClusters(int) - Method in class weka.clusterers.EM: Set the number of clusters (-1 to select by CV).
setNumClusters(int) - Method in class weka.clusterers.FarthestFirst: set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.HierarchicalClusterer
setNumClusters(int) - Method in class weka.clusterers.MakeDensityBasedClusterer: Set the number of clusters to generate.
setNumClusters(int) - Method in interface weka.clusterers.NumberOfClustersRequestable: Set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.sIB: Set the number of clusters
setNumClusters(int) - Method in class weka.clusterers.SimpleKMeans: set the number of clusters to generate
setNumClusters(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the number of clusters the dataset should have.
setNumComponents(int) - Method in class weka.filters.supervised.attribute.PLSFilter: sets the maximum number of attributes to use.
setNumCycles(int) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the the number of cycles.
setNumDate(int) - Method in class weka.core.CheckScheme: sets the number of data attributes
setNumDate(int) - Method in class weka.core.TestInstances: sets the number of date attributes
setNumeric(boolean) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets if the new Attribute is to be numeric.
setNumExamples(int) - Method in class weka.datagenerators.ClassificationGenerator: Sets the number of examples, given by option.
setNumExamples(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet: Sets the number of examples, given by option.
setNumExamples(int) - Method in class weka.datagenerators.RegressionGenerator: Sets the number of examples, given by option.
setNumFeatures(int) - Method in class weka.classifiers.trees.RandomForest: Set the number of features to use in random selection.
setNumFoldersMIOption(int) - Method in class weka.classifiers.rules.NNge: Sets the number of folder for mutual information.
setNumFolds(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the number of folds to use for CV-based hyperparameter selection
setNumFolds(int) - Method in class weka.classifiers.functions.SMO: Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.meta.CVParameterSelection: Sets the number of folds for the cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.Dagging: Sets the number of folds to use for splitting the training set.
setNumFolds(int) - Method in class weka.classifiers.meta.LogitBoost: Set the value of NumFolds.
setNumFolds(int) - Method in class weka.classifiers.meta.MultiScheme: Sets the number of folds for cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.Stacking: Sets the number of folds for the cross-validation.
setNumFolds(int) - Method in class weka.classifiers.mi.MISMO: Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.rules.PART: Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.trees.J48: Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.trees.RandomTree: Set the value of NumFolds.
setNumFolds(int) - Method in class weka.classifiers.trees.REPTree: Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.CrossValidationResultProducer: Set the value of NumFolds.
setNumFolds(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Sets the number of folds the dataset is split into.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Sets the number of folds the dataset is split into.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the number of cross-validation folds to use - < 2 means no cross-validation.
setNumFoldsPruning(int) - Method in class weka.classifiers.trees.BFTree: Set number of folds in internal cross-validation.
setNumFoldsPruning(int) - Method in class weka.classifiers.trees.SimpleCart: Set number of folds in internal cross-validation.
setNumInstances(int) - Method in class weka.core.CheckScheme: Sets the number of instances to use in the datasets (some classifiers might require more instances).
setNumInstances(int) - Method in class weka.core.TestInstances: sets the number of instances to produce
setNumInstances(Random) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets the real number of instances for this cluster.
setNumInstances(int) - Method in class weka.estimators.CheckEstimator: Sets the number of instances to use in the datasets (some estimators might require more instances).
setNumInstancesRelational(int) - Method in class weka.core.CheckScheme: sets the number of instances in relational/bag attributes to produce
setNumInstancesRelational(int) - Method in class weka.core.TestInstances: sets the number of instances in relational/bag attributes to produce
setNumIrrelevant(int) - Method in class weka.datagenerators.classifiers.classification.RDG1: Sets the number of irrelevant attributes.
setNumIterations(int) - Method in class weka.classifiers.bayes.DMNBtext: Sets the number of iterations to be performed
setNumIterations(int) - Method in class weka.classifiers.functions.VotedPerceptron: Set the value of NumIterations.
setNumIterations(int) - Method in class weka.classifiers.functions.Winnow: Set the value of numIterations.
setNumIterations(int) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.classifiers.meta.MetaCost: Sets the number of bagging iterations
setNumNeighbours(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the number of nearest neighbours
setNumNeighbours(int) - Method in class weka.classifiers.mi.MINND: Sets the number of nearest neighbours to estimate the class prediction of tests bags
setNumNominal(int) - Method in class weka.core.CheckScheme: sets the number of nominal attributes
setNumNominal(int) - Method in class weka.core.TestInstances: sets the number of nominal attributes
setNumNominalValues(int) - Method in class weka.core.TestInstances: sets the number of values for nominal attributes
setNumNumeric(int) - Method in class weka.core.CheckScheme: sets the number of numeric attributes
setNumNumeric(int) - Method in class weka.core.TestInstances: sets the number of numeric attributes
setNumNumeric(int) - Method in class weka.datagenerators.classifiers.classification.RDG1: Sets the number of numerical attributes.
setNumOfBoostingIterations(int) - Method in class weka.classifiers.trees.ADTree: Sets the number of boosting iterations.
setNumOfBoostingIterations(int) - Method in class weka.classifiers.trees.LADTree: Sets the number of boosting iterations.
setNumReferences(int) - Method in class weka.classifiers.mi.CitationKNN: Sets the number of references considered to estimate the class prediction of tests bags
setNumRelational(int) - Method in class weka.core.CheckScheme: sets the number of relational attributes
setNumRelational(int) - Method in class weka.core.TestInstances: sets the number of relational attributes
setNumRelationalDate(int) - Method in class weka.core.TestInstances: sets the number of date attributes in a relational attribute
setNumRelationalNominal(int) - Method in class weka.core.TestInstances: sets the number of nominal attributes in a relational attribute
setNumRelationalNominalValues(int) - Method in class weka.core.TestInstances: sets the number of values for nominal attributes in a relational attribute
setNumRelationalNumeric(int) - Method in class weka.core.TestInstances: sets the number of numeric attributes in a relational attribute
setNumRelationalString(int) - Method in class weka.core.TestInstances: sets the number of string attributes in a relational attribute
setNumRestarts(int) - Method in class weka.clusterers.sIB: Set the number of restarts
setNumRules(int) - Method in class weka.associations.Apriori: Set the value of numRules.
setNumRules(int) - Method in class weka.associations.PredictiveApriori: Set the value of required rules.
setNumRulesToFind(int) - Method in class weka.associations.FPGrowth: Set the desired number of rules to find.
setNumRuns(int) - Method in class weka.classifiers.meta.LogitBoost: Set the value of NumRuns.
setNumSamplesPerRegion(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the number of points to uniformly sample from a region (fixed dimensions).
setNumSamplesPerRegion(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the number of points to uniformly sample from a region (fixed dimensions).
setNumString(int) - Method in class weka.core.CheckScheme: sets the number of string attributes
setNumString(int) - Method in class weka.core.TestInstances: sets the number of string attributes
setNumSubCmtys(int) - Method in class weka.classifiers.meta.MultiBoostAB: Set the number of sub committees to use
setNumSubsetSizeCVFolds(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Set the number of cross validation folds for subset size determination (default = 5).
setNumTestingNoises(int) - Method in class weka.classifiers.mi.MINND: Sets The number of nearest neighbour exemplars in the selection of noises in the test data
setNumToSelect(int) - Method in class weka.attributeSelection.GreedyStepwise: Specify the number of attributes to select from the ranked list (if generating a ranking).
setNumToSelect(int) - Method in class weka.attributeSelection.RaceSearch: Specify the number of attributes to select from the ranked list (if generating a ranking).
setNumToSelect(int) - Method in interface weka.attributeSelection.RankedOutputSearch: Specify the number of attributes to select from the ranked list.
setNumToSelect(int) - Method in class weka.attributeSelection.Ranker: Specify the number of attributes to select from the ranked list.
setNumTrainingNoises(int) - Method in class weka.classifiers.mi.MINND: Sets the number of nearest neighbour instances in the selection of noises in the training data
setNumTrees(int) - Method in class weka.classifiers.trees.RandomForest: Set the value of numTrees.
setNumUsedAttributes(int) - Method in class weka.attributeSelection.LinearForwardSelection: Set the number of top-ranked attributes that taken into account by the search process.
setNumUsedAttributes(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Set the number of top-ranked attributes that taken into account by the search process.
setNumValues(int) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Sets how many values are retained
setNumXValFolds(int) - Method in class weka.classifiers.meta.ThresholdSelector: Set the number of folds used for cross-validation.
setObject(Object) - Method in class weka.core.CheckGOE: Set the object to work on..
setObject(Object) - Method in class weka.gui.beans.AssociatorCustomizer: Set the classifier object to be edited
setObject(Object) - Method in class weka.gui.beans.ClassAssignerCustomizer: Set the bean to be edited
setObject(Object) - Method in class weka.gui.beans.ClassifierCustomizer: Set the classifier object to be edited
setObject(Object) - Method in class weka.gui.beans.ClassValuePickerCustomizer: Set the bean to be edited
setObject(Object) - Method in class weka.gui.beans.ClustererCustomizer: Set the Clusterer object to be edited
setObject(Object) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer: Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.FilterCustomizer: Set the filter bean to be edited
setObject(Object) - Method in class weka.gui.beans.IncrementalClassifierEvaluatorCustomizer: Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.LoaderCustomizer: Set the loader to be customized
setObject(Object) - Method in class weka.gui.beans.PredictionAppenderCustomizer: Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.SaverCustomizer: Set the saver to be customized
setObject(Object) - Method in class weka.gui.beans.SerializedModelSaverCustomizer: Set the model saver to be customized
setObject(Object) - Method in class weka.gui.beans.StripChartCustomizer: Set the StripChart object to be customized
setObject(Object) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer: Set the TrainTestSplitMaker to be customized
setOfSequencesToString(FastVector, Instances, FastVector) - Static method in class weka.associations.gsp.Sequence: Returns a String representation of a set of Sequences where the numeric value of each event/item is represented by its respective nominal value.
setOkButtonText(String) - Method in class weka.gui.GenericObjectEditor.GOEPanel: Allows customization of the action label on the dialog.
setOmega(double) - Method in class weka.classifiers.functions.supportVector.Puk: Sets the omega value.
setOn(boolean) - Method in class weka.gui.visualize.ClassPanel: Enables the panel
setOnDemandDirectory(File) - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.classifiers.meta.MetaCost: Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Sets the directory that will be searched for cost files when loading on demand.
setOptimalColumnWidth(int) - Method in class weka.gui.JTableHelper: sets the optimal column width for the given column
setOptimalColumnWidth(JTable, int) - Static method in class weka.gui.JTableHelper: sets the optimal column width for the given column
setOptimalColumnWidth() - Method in class weka.gui.JTableHelper: sets the optimal column width for all columns
setOptimalColumnWidth(JTable) - Static method in class weka.gui.JTableHelper: sets the optimal column width for alls column if the given table
setOptimalColWidth() - Method in class weka.gui.arffviewer.ArffPanel: calculates the optimal column width for the current column
setOptimalColWidths() - Method in class weka.gui.arffviewer.ArffPanel: calculates the optimal column widths for all columns
setOptimalColWidths() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: sets the optimal column width for all columns
setOptimalHeaderWidth(int) - Method in class weka.gui.JTableHelper: sets the optimal header width for the given column
setOptimalHeaderWidth(JTable, int) - Static method in class weka.gui.JTableHelper: sets the optimal header width for the given column
setOptimalHeaderWidth() - Method in class weka.gui.JTableHelper: sets the optimal header width for all columns
setOptimalHeaderWidth(JTable) - Static method in class weka.gui.JTableHelper: sets the optimal header width for alls column if the given table
setOptimizations(int) - Method in class weka.classifiers.rules.JRip: Sets the number of optimization runs
setOptionHandler(OptionHandler) - Method in class weka.core.CheckOptionHandler: Set the OptionHandler to work on..
setOptions(String[]) - Method in class weka.associations.Apriori: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.CheckAssociator: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.FilteredAssociator: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.FPGrowth: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.GeneralizedSequentialPatterns: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.PredictiveApriori: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.SingleAssociatorEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.Tertius: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.BestFirst: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CfsSubsetEval: Parses and sets a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CheckAttributeSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ClassifierSubsetEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ExhaustiveSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.FilteredAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.FilteredSubsetEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GainRatioAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GeneticSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GreedyStepwise: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.InfoGainAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.LatentSemanticAnalysis: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.LinearForwardSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.OneRAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.PrincipalComponents: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RaceSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RandomSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.Ranker: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RankSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ReliefFAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ScatterSearchV1: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.SVMAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.WrapperSubsetEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.AODE: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.AODEsr: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.BayesNet: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.DMNBtext
setOptions(String[]) - Method in class weka.classifiers.bayes.NaiveBayes: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.BayesNetGenerator: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.K2: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.global.TAN: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.K2: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.LAGDHillClimber: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.local.TAN: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.WAODE: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.BVDecompose: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.CheckClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.CheckSource: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.Classifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.GaussianProcesses: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.LeastMedSq: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.functions.LibLINEAR: Sets the classifier options
Valid options are:
setOptions(String[]) - Method in class weka.classifiers.functions.LibSVM: Sets the classifier options
Valid options are:
setOptions(String[]) - Method in class weka.classifiers.functions.LinearRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.Logistic: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.MultilayerPerceptron: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.PaceRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.PLSClassifier: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.functions.RBFNetwork: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SimpleLogistic: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SMO: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SMOreg: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SPegasos: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.CachedKernel: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.CheckKernel: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.Kernel: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.PolyKernel: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.Puk: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RBFKernel: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegSMO: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.supportVector.StringKernel: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.VotedPerceptron: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.Winnow: Parses a given list of options.
Valid options are:
setOptions(String[]) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.lazy.IBk: Parses a given list of options.
setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute: Sets the options.
setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Set options.
setOptions(String[]) - Method in class weka.classifiers.lazy.KStar: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.lazy.LWL: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AdaBoostM1: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AdditiveRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Bagging: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.ClassificationViaClustering: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.CVParameterSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Dagging: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Decorate: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.FilteredClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.GridSearch: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.meta.LogitBoost: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MetaCost: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiBoostAB: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiClassClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiScheme: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.OrdinalClassClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RandomSubSpace: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RegressionByDiscretization: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RotationForest: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Stacking: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.ThresholdSelector: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Vote: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.CitationKNN: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.mi.MDD: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIBoost: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIDD: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIEMDD: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MILR: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MINND: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIOptimalBall: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MISMO: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MISVM: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.MIWrapper: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.mi.SimpleMI: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.misc.SerializedClassifier: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.misc.VFI: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.MultipleClassifiersCombiner: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.ConjunctiveRule: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.DecisionTable: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.rules.DTNB: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.rules.JRip: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.NNge: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.rules.OneR: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.PART: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.Ridor: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.SingleClassifierEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.ADTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.BFTree: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.trees.FT: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.J48: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.J48graft: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.LADTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.LMT: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.m5.M5Base: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.M5P: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomForest: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.REPTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.SimpleCart: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.CheckClusterer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.CLOPE: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.Cobweb: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.DBSCAN: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.clusterers.EM: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.FarthestFirst: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.FilteredClusterer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.HierarchicalClusterer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.MakeDensityBasedClusterer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.OPTICS: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.clusterers.RandomizableClusterer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.RandomizableDensityBasedClusterer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.RandomizableSingleClustererEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.sIB: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.SimpleKMeans: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.SingleClustererEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.XMeans: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.Check: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.CheckGOE: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.CheckOptionHandler: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.CheckScheme: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.AbstractFileSaver: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.ArffSaver: Parses the options for this object.
setOptions(String[]) - Method in class weka.core.converters.C45Saver: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.CSVLoader: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.DatabaseLoader: Sets the options.
setOptions(String[]) - Method in class weka.core.converters.DatabaseSaver: Sets the options.
setOptions(String[]) - Method in class weka.core.converters.LibSVMSaver: Parses the options for this object.
setOptions(String[]) - Method in class weka.core.converters.SVMLightSaver: Parses the options for this object.
setOptions(String[]) - Method in class weka.core.converters.TextDirectoryLoader: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.converters.XRFFSaver: Parses the options for this object.
setOptions(String[]) - Method in class weka.core.FindWithCapabilities: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.Javadoc: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.ListOptions: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.BallTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.BallSplitter: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.BallTreeConstructor: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.balltrees.TopDownConstructor: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.CoverTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.KDTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.LinearNNSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.NormalizableDistance: Parses a given list of options.
setOptions(String[]) - Method in interface weka.core.OptionHandler: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.core.OptionHandlerJavadoc: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.stemmers.SnowballStemmer: Parses the options.
setOptions(String[]) - Method in class weka.core.TechnicalInformationHandlerJavadoc: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.TestInstances: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.tokenizers.CharacterDelimitedTokenizer: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.core.tokenizers.NGramTokenizer: Parses a given list of options.
setOptions(String[]) - Method in class weka.core.tokenizers.Tokenizer: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.datagenerators.ClassificationGenerator: Sets the options.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.Agrawal: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.BayesNet: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.LED24: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.RandomRBF: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.classification.RDG1: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.regression.Expression: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.classifiers.regression.MexicanHat: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.ClusterDefinition: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.clusterers.SubspaceCluster: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.ClusterGenerator: Sets the options.
setOptions(String[]) - Method in class weka.datagenerators.DataGenerator: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.RegressionGenerator: Sets the options.
setOptions(String[]) - Method in class weka.estimators.CheckEstimator: Parses a given list of options.
setOptions(String[]) - Method in class weka.estimators.Estimator: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.AveragingResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CrossValidationResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CSVResultListener: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DatabaseResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.Experiment: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.InstanceQuery: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.LearningRateResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.PairedTTester: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RandomSplitResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RegressionSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.CheckSource: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.MultiFilter: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.SimpleFilter: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.AddClassification: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.AttributeSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.ClassOrder: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.Discretize: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.NominalToBinary: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.PLSFilter: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.instance.Resample: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.instance.SMOTE: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.instance.SpreadSubsample: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Add: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddCluster: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddExpression: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddID: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddNoise: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddValues: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ClassAssigner: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Copy: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Discretize: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.KernelFilter: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MathExpression: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NominalToString: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Normalize: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericCleaner: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericToNominal: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RandomSubset: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RELAGGS: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Remove: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RemoveType: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Reorder: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.StringToNominal: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.SwapValues: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Wavelet: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Normalize: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Randomize: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveRange: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Resample: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.ReservoirSample: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.SubsetByExpression: Parses a given list of options.
setOptions(String[]) - Method in class weka.gui.Main: Parses the options for this object.
setOriginalCoords(Vector) - Method in class weka.gui.beans.MetaBean: sets the vector containing the original coordinates (instances of class Point) for the inputs
setOutlierFactor(double) - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Sets the factor for determining the thresholds for outliers.
setOutput(PrintWriter) - Method in class weka.datagenerators.DataGenerator: Sets the print writer.
setOutputCenterFile(File) - Method in class weka.clusterers.XMeans: Sets file to write the list of centers to.
setOutputClassification(boolean) - Method in class weka.filters.supervised.attribute.AddClassification: Set whether the classification of the classifier is output.
setOutputDistribution(boolean) - Method in class weka.filters.supervised.attribute.AddClassification: Set whether the Distribution of the classifier is output.
setOutputErrorFlag(boolean) - Method in class weka.filters.supervised.attribute.AddClassification: Set whether the classification of the classifier is output.
setOutputFile(File) - Method in class weka.experiment.CrossValidationResultProducer: Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.CSVResultListener: Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.RandomSplitResultProducer: Set the value of OutputFile.
setOutputFilename(boolean) - Method in class weka.core.converters.TextDirectoryLoader: Sets whether the filename will be stored as an extra attribute.
setOutputFileName(String) - Method in class weka.experiment.CSVResultListener: Set the value of OutputFileName.
setOutputFilename(String) - Method in class weka.gui.GenericPropertiesCreator: sets the file to output the properties for the GEO to
setOutputFormat(int) - Method in class weka.core.Debug.Clock: sets the format of the output
setOutputFormatFromDialog() - Method in class weka.gui.experiment.ResultsPanel: displays the Dialog for the output format and sets the chosen settings, if the user approves.
setOutputItemSets(boolean) - Method in class weka.associations.Apriori: Sets whether itemsets are output as well
setOutputOffsetMultiplier(boolean) - Method in class weka.filters.unsupervised.attribute.InterquartileRange: Set whether an additional attribute "Offset" is generated per Outlier/ExtremeValue attribute pair that lists the multiplier the value is off the median: value = median + 'multiplier' * IQR.
setOutputPerClassInfoRetrievalStats(boolean) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Set whether to output per-class information retrieval statistics (nominal class only).
setOutputs(Vector) - Method in class weka.gui.beans.MetaBean
setOutputTypes(String) - Method in class weka.core.Debug.DBO: Switches the outputs on that are requested from the option O
setOutputWordCounts(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether output instances contain 0 or 1 indicating word presence, or word counts.
setOverwriteWarning(boolean) - Method in class weka.gui.ConverterFileChooser: Whether a warning is popped up if the file that is to be saved already exists (only save dialog).
setOwner(CapabilitiesHandler) - Method in class weka.core.Capabilities: sets the owner of this capabilities object
setP(double) - Method in class weka.classifiers.BVDecomposeSegCVSub: Set the proportion of instances that are common between two training sets used to train a classifier.
setPadding(SelectedTag) - Method in class weka.filters.unsupervised.attribute.Wavelet: Sets the type of Padding to use
setPaint(Paint) - Method in class weka.gui.visualize.PostscriptGraphics
setPaintMode() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
setPanelHeight(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the height of the visualization
setPanelWidth(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the width of the visualization
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInArithmetic: Set the structure of the parameters that are expected as input by this function.
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInMath: Set the structure of the parameters that are expected as input by this function.
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.BuiltInString: Set the structure of the parameters that are expected as input by this function.
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.DefineFunction: Set the structure of the parameters that are expected as input by this function.
setParameterDefs(ArrayList<Attribute>) - Method in class weka.core.pmml.Function: Set the structure of the parameters that are expected as input by this function.
SetParent(int, int) - Method in class weka.classifiers.bayes.net.ParentSet: sets index parent of parent specified by index
setParent(ClusterGenerator) - Method in class weka.datagenerators.ClusterDefinition: sets the parent datagenerator this cluster belongs to
setParent(SubspaceCluster) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: sets the parent datagenerator this cluster belongs to
setParent(Container) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: sets the new parent frame
setParent(Edge) - Method in class weka.gui.treevisualizer.Node: Set the value of parent.
setParentFrame(JFrame) - Method in class weka.gui.beans.AssociatorCustomizer
setParentFrame(JFrame) - Method in class weka.gui.beans.ClassAssignerCustomizer
setParentFrame(JFrame) - Method in class weka.gui.beans.ClassifierCustomizer
setParentFrame(JFrame) - Method in class weka.gui.beans.ClassValuePickerCustomizer
setParentFrame(JFrame) - Method in class weka.gui.beans.ClustererCustomizer
setParentFrame(JFrame) - Method in interface weka.gui.beans.CustomizerCloseRequester: A reference to the parent is passed in
setParentFrame(JFrame) - Method in class weka.gui.beans.FilterCustomizer
setParentFrame(JFrame) - Method in class weka.gui.beans.LoaderCustomizer
setParentFrame(JFrame) - Method in class weka.gui.beans.SaverCustomizer
setParentFrame(JFrame) - Method in class weka.gui.beans.SerializedModelSaverCustomizer
setParentFrame(JFrame) - Method in class weka.gui.SetInstancesPanel: Sets the frame, this panel resides in.
setParentSeparator(MarginCalculator.JunctionTreeSeparator) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
setPassword(String) - Method in interface weka.core.converters.DatabaseConverter
setPassword(String) - Method in class weka.core.converters.DatabaseLoader: Sets user password for the database
setPassword(String) - Method in class weka.core.converters.DatabaseSaver: Sets the database password.
setPassword(String) - Method in class weka.experiment.DatabaseUtils: Set the database password.
setPassword(String) - Method in class weka.gui.sql.ConnectionPanel: sets the Password.
setPattern(SelectedTag) - Method in class weka.datagenerators.clusterers.BIRCHCluster: Sets the pattern type.
setPercent(int) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets the size of noise data, as a percentage of the original set.
setPercent(double) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the percent the attributes (dimensions) of the data should be reduced to
setPercent() - Method in class weka.gui.visualize.MatrixPanel: Calculates the percentage to resample
setPercentage(double) - Method in class weka.filters.supervised.instance.SMOTE: Sets the percentage of SMOTE instances to create.
setPercentage(double) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Sets the percentage of intances to select.
setPercentCompleted(int) - Method in class weka.gui.boundaryvisualizer.RemoteResult: Set the progress for this row so far
setPercentThreshold(int) - Method in class weka.attributeSelection.SVMAttributeEval: Set the threshold below which percentage elimination reverts to constant elimination.
setPercentToEliminatePerIteration(int) - Method in class weka.attributeSelection.SVMAttributeEval: Set the percentage of attributes to eliminate per iteration
setPerformPrediction(boolean) - Method in class weka.filters.supervised.attribute.PLSFilter: Sets whether to update the class attribute with the predicted value.
setPerformRanking(boolean) - Method in class weka.attributeSelection.LinearForwardSelection: Perform initial ranking to select top-ranked attributes.
setPerformRanking(boolean) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Perform initial ranking to select top-ranked attributes.
setPeriodicPruning(double) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets the rate at which the dictionary is periodically pruned, as a percentage of the dataset size.
setPerturbationFraction(double) - Method in class weka.datagenerators.classifiers.classification.Agrawal: Sets the perturbation fraction.
setPivot(Instance) - Method in class weka.core.neighboursearch.balltrees.BallNode: Sets the pivot/centre of this nodes ball.
setPixHeight(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the height of a pixel
setPixWidth(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the width of a pixel
setPlotCompanion(Plot2DCompanion) - Method in class weka.gui.visualize.Plot2D: Set a companion class.
setPlotList(FastVector) - Method in class weka.gui.visualize.LegendPanel: Set the list of plots to generate legend entries for
setPlotName(String) - Method in class weka.gui.visualize.PlotData2D: Set the name of this plot
setPlotNameHTML(String) - Method in class weka.gui.visualize.PlotData2D: Set the plot name for use in a tool tip text.
setPlotTrainingData(boolean) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set whether to superimpose the training data plot
setPlus(int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix: Add a value to an element and reset the element
setPlus(int, double) - Method in class weka.core.matrix.DoubleVector: Adds a value to an element
setPMMLVersion(Document) - Method in class weka.classifiers.pmml.consumer.PMMLClassifier: Set the version of PMML used for this model.
setPMMLVersion(Document) - Method in interface weka.core.pmml.PMMLModel: Set the version of the PMML.
setPoints(MiddleOutConstructor.TempNode, int, int, int[]) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Sets the points of an anchor node.
setPointValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Sets a particular point value
setPopulationSize(int) - Method in class weka.attributeSelection.GeneticSearch: set the population size
setPopulationSize(int) - Method in class weka.attributeSelection.ScatterSearchV1: Set the population size
setPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
setPopulationSize(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
setPopup(JPopupMenu) - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog: sets the JPopupMenu to display again after closing the dialog.
setPosition(int, int, int) - Method in class weka.classifiers.bayes.net.EditableBayesNet: set position of node
setPosition(int, int, int, FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: Set position of node.
setPositiveIndex(int) - Method in class weka.associations.FPGrowth: Set the index of the attribute value to consider as positive for binary attributes in normal dense instances.
setPostProcessor(CheckScheme.PostProcessor) - Method in class weka.core.CheckScheme: sets the PostProcessor to use
setPostProcessor(CheckEstimator.PostProcessor) - Method in class weka.estimators.CheckEstimator: sets the PostProcessor to use
setPredTargetColumn(boolean) - Method in class weka.experiment.ClassifierSplitEvaluator: Set the flag for prediction and target output.
setPreferredScrollableViewportSize(Dimension) - Method in class weka.gui.AttributeSelectionPanel
setPrefix(String) - Method in class weka.gui.beans.SerializedModelSaver: Set the prefix to prepend to the model file names.
setPreprocessing(SelectedTag) - Method in class weka.filters.supervised.attribute.PLSFilter: Sets the type of preprocessing to use
setPreprocessing(Filter) - Method in class weka.filters.unsupervised.attribute.KernelFilter: Sets the filter to use for preprocessing (use the AllFilter for no preprocessing)
setPreserveInstancesOrder(boolean) - Method in class weka.clusterers.SimpleKMeans: Sets whether order of instances must be preserved
setPrintColNames(boolean) - Method in class weka.experiment.ResultMatrix: sets whether the column names or numbers instead are printed.
setPrintNewick(boolean) - Method in class weka.clusterers.HierarchicalClusterer
setPrintRowNames(boolean) - Method in class weka.experiment.ResultMatrix: sets whether the row names or numbers instead are printed deactivating automatically sets m_EnumerateColNames to TRUE.
setPriorClass(SelectedTag) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the type of prior to use.
setPriors(Instances) - Method in class weka.classifiers.Evaluation: Sets the class prior probabilities
setProbabilityEstimates(boolean) - Method in class weka.classifiers.functions.LibLINEAR: Returns whether probability estimates are generated instead of -1/+1 for classification problems.
setProbabilityEstimates(boolean) - Method in class weka.classifiers.functions.LibSVM: Returns whether probability estimates are generated instead of -1/+1 for classification problems.
setProcessed(boolean) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Marks this dataObject as processed
setProcessed(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Marks this dataObject as processed
setProcessed(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Marks this dataObject as processed
setProjectionFilter(Filter) - Method in class weka.classifiers.meta.RotationForest: Sets the filter used to project the data.
setProlog(boolean) - Method in class weka.core.OptionHandlerJavadoc: sets whether to add the "Valid options are..." prolog
setProlog(boolean) - Method in class weka.core.TechnicalInformationHandlerJavadoc: sets whether to add the "Valid options are..." prolog
setProperty(String, String) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
setPropertyArray(Object) - Method in class weka.experiment.Experiment: Sets the array of values to set the custom property to.
setPropertyArray(Object) - Method in class weka.experiment.RemoteExperiment: Sets the array of values to set the custom property to.
setPropertyPath(PropertyNode[]) - Method in class weka.experiment.Experiment: Sets the path of properties taken to get to the custom property to iterate over.
setPropertyPath(PropertyNode[]) - Method in class weka.experiment.RemoteExperiment: Sets the path of properties taken to get to the custom property to iterate over.
setPruningMethod(SelectedTag) - Method in class weka.classifiers.functions.supportVector.StringKernel: Sets the method used to for pruning.
setPruningStrategy(SelectedTag) - Method in class weka.classifiers.trees.BFTree: Sets the pruning strategy.
setPruningType(SelectedTag) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the pruning type
setQuality(float) - Method in class weka.gui.visualize.JPEGWriter: sets the quality the JPEG is saved in.
setQuery(String) - Method in class weka.core.converters.DatabaseLoader: Sets the query to execute against the database
setQuery(String) - Method in class weka.experiment.InstanceQuery: Set the query to execute against the database
setQuery(String) - Method in class weka.gui.sql.QueryPanel: sets the query in the textarea.
setQueryPanel(QueryPanel) - Method in class weka.gui.sql.ResultPanel: sets the QueryPanel to use for displaying the query
setRaceType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch: Set the race type
setRadius(double) - Method in class weka.core.neighboursearch.balltrees.BallNode: Sets the radius of the node's ball.
setRandom(Random) - Method in class weka.datagenerators.DataGenerator: Sets the random generator.
setRandomize(boolean) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Sets whether the order of the generated data is randomized
setRandomizeData(boolean) - Method in class weka.experiment.RandomSplitResultProducer: Set to true if dataset is to be randomized
setRandomOrder(boolean) - Method in class weka.classifiers.bayes.net.search.global.K2: Set random order flag
setRandomOrder(boolean) - Method in class weka.classifiers.bayes.net.search.local.K2: Set random order flag
setRandomSeed(long) - Method in class weka.classifiers.functions.LeastMedSq: Set the seed for the random number generator
setRandomSeed(int) - Method in class weka.classifiers.functions.SMO: Set the value of randomSeed.
setRandomSeed(int) - Method in class weka.classifiers.mi.MISMO: Set the value of randomSeed.
setRandomSeed(int) - Method in class weka.classifiers.trees.ADTree: Sets random seed for a random walk.
setRandomSeed(int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Sets the seed for random number generator.
setRandomSeed(int) - Method in class weka.filters.supervised.instance.Resample: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.supervised.instance.SMOTE: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets the random number seed.
setRandomSeed(long) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the random seed of the random number generator
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Randomize: Set the random number generator seed value.
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Resample: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.ReservoirSample: Sets the random number seed.
setRandomWidthFactor(double) - Method in class weka.classifiers.meta.MultiClassClassifier: Sets the multiplier when generating random codes.
setRangeCorrection(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector: Sets the confidence range correction mode used.
setRanges(String) - Method in class weka.core.Range: Sets the ranges from a string representation.
setRanges(Range[]) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Sets the list of possible Ranges to choose from.
setRank(double) - Method in class weka.attributeSelection.LatentSemanticAnalysis: Sets the desired matrix rank (or coverage proportion) for feature-space reduction
setRanking(boolean) - Method in class weka.attributeSelection.AttributeSelection: produce a ranking (if possible with the set search and evaluator)
setRanking(int[][]) - Method in class weka.experiment.ResultMatrix: sets the ranking data based on the wins
setRawOutput(boolean) - Method in class weka.experiment.CrossValidationResultProducer: Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.RandomSplitResultProducer: Set to true if raw split evaluator output is to be saved
setReachabilityDistance(double) - Method in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject: Sets a new reachability-distance for this dataObject
setReachabilityDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject: Sets a new reachability-distance for this dataObject
setReachabilityDistance(double) - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject: Sets a new reachability-distance for this dataObject
setReachabilityDistanceColor(Color) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Sets a new color for the reachabilityDistance
setReadable(String) - Method in class weka.core.Tag: Sets the string description of the Tag.
setReadIncrementally(boolean) - Method in class weka.gui.SetInstancesPanel: Sets whether or not instances should be read incrementally by the Loader.
setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffPanel: sets whether the model is read-only
setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel: sets whether the model is read-only
setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffTable: sets whether the model is read-only
setReadOnly(boolean) - Method in class weka.gui.arffviewer.ArffTableModel: sets whether the model is read-only
setReducedErrorPruning(boolean) - Method in class weka.classifiers.rules.PART: Set the value of reducedErrorPruning.
setReducedErrorPruning(boolean) - Method in class weka.classifiers.trees.J48: Set the value of reducedErrorPruning.
setRefer(String) - Method in class weka.gui.treevisualizer.Node: Set the value of refer.
setRefreshFreq(int) - Method in class weka.gui.beans.StripChart: Set how often (in x axis points) to refresh the display
setRegOptimizer(RegOptimizer) - Method in class weka.classifiers.functions.SMOreg: sets the learning algorithm
setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.Rule: Set the value of regressionTree.
setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.RuleNode: Set the value of regressionTree.
setRelabel(boolean) - Method in class weka.classifiers.trees.J48graft: Set the value of relabelling.
setRelation(String) - Method in class weka.core.TestInstances: sets the name of the relation
setRelationalClassFormat(Instances) - Method in class weka.core.TestInstances: sets the structure for the relational class attribute
setRelationalFormat(int, Instances) - Method in class weka.core.TestInstances: sets the structure for the bags for the relational attribute
setRelationForTableName(boolean) - Method in class weka.core.converters.DatabaseSaver: En/Dis-ables that the relation name is used for the name of the table (default enabled).
setRelationName(String) - Method in class weka.core.Instances: Sets the relation's name.
setRelationName(String) - Method in class weka.datagenerators.DataGenerator: Sets the relation name the dataset should have.
setRelationNameForFilename(boolean) - Method in class weka.gui.beans.Saver: Set whether to use the relation name as the primary part of the filename.
setRemoteHosts(DefaultListModel) - Method in class weka.experiment.RemoteExperiment: Set the list of remote host names
setRemoteHosts(Vector) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Set a list of host names of machines to distribute processing to
setRemoveAllMissingCols(boolean) - Method in class weka.associations.Apriori: Remove columns containing all missing values.
setRemoveClassColumn(boolean) - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Set whether the class column should be removed from the data.
setRemovedPercentage(int) - Method in class weka.classifiers.meta.RotationForest: Sets the percentage of instance to be removed
setRemoveFilterName(boolean) - Method in class weka.experiment.ResultMatrix: sets whether to remove the filter classname from the dataset name
setRemoveFilterName(boolean) - Method in class weka.gui.experiment.OutputFormatDialog: sets whether to remove the filter classname from the dataset name.
setRemoveOldClass(boolean) - Method in class weka.filters.supervised.attribute.AddClassification: Set whether the old class attribute is removed.
setRemoveUnused(boolean) - Method in class weka.filters.unsupervised.attribute.PartitionedMultiFilter: Sets whether unused attributes (ones that are not covered by any of the ranges) are removed from the output.
setRenderingHint(RenderingHints.Key, Object) - Method in class weka.gui.visualize.PostscriptGraphics
setRenderingHints(Map) - Method in class weka.gui.visualize.PostscriptGraphics
setRepeatLiterals(boolean) - Method in class weka.associations.Tertius: Set the value of repeatLiterals.
setReplaceMissing(boolean) - Method in class weka.filters.supervised.attribute.PLSFilter: Sets whether to replace missing values.
setReplaceMissingValues(boolean) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets either to use replace missing values filter or not
setReportFrequency(int) - Method in class weka.attributeSelection.GeneticSearch: set how often reports are generated
setRepulsion(double) - Method in class weka.clusterers.CLOPE: set the repulsion
setReset(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron: This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.
setReset(boolean) - Method in class weka.gui.beans.ChartEvent: Set the reset flag
setResult(Double) - Method in class weka.core.mathematicalexpression.Parser: Sets the result of the evaluation.
setResult(Boolean) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Sets the result of the evaluation.
setResultKeyFromDialog() - Method in class weka.gui.experiment.ResultsPanel
setResultListener(ResultListener) - Method in class weka.experiment.AveragingResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.CrossValidationResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.DatabaseResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.Experiment: Sets the result listener where results will be sent.
setResultListener(ResultListener) - Method in class weka.experiment.LearningRateResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.RandomSplitResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.RemoteExperiment: Sets the result listener where results will be sent.
setResultListener(ResultListener) - Method in interface weka.experiment.ResultProducer: Sets the object to send results of each run to.
setResultMatrix(ResultMatrix) - Method in class weka.experiment.PairedTTester: Sets the matrix to use to produce the output.
setResultMatrix(ResultMatrix) - Method in interface weka.experiment.Tester: Sets the matrix to use to produce the output.
setResultMatrix(Class) - Method in class weka.gui.experiment.OutputFormatDialog: Sets the matrix to use as initial selected output format.
setResultProducer(ResultProducer) - Method in class weka.experiment.AveragingResultProducer: Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer: Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.Experiment: Set the result producer used for the current experiment.
setResultProducer(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer: Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.RemoteExperiment: Set the result producer used for the current experiment.
setResultsetKeyColumns(Range) - Method in class weka.experiment.PairedTTester: Set the value of ResultsetKeyColumns.
setResultsetKeyColumns(Range) - Method in interface weka.experiment.Tester: Set the value of ResultsetKeyColumns.
setResultsPanel(ResultsPanel) - Method in class weka.gui.experiment.RunPanel: Sets the pointer to the results panel.
setResultVector(FastVector) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Sets a new resultVector
setRetrieval(int) - Method in class weka.core.converters.AbstractLoader: Sets the retrieval mode.
setRetrieval(int) - Method in class weka.core.converters.AbstractSaver: Sets the retrieval mode.
setRetrieval(int) - Method in interface weka.core.converters.Loader: Sets the retrieval mode.
setRetrieval(int) - Method in interface weka.core.converters.Saver: Sets the retrieval mode
setRidge(double) - Method in class weka.classifiers.functions.LinearRegression: Set the value of Ridge.
setRidge(double) - Method in class weka.classifiers.functions.Logistic: Sets the ridge in the log-likelihood.
setRidge(double) - Method in class weka.classifiers.functions.RBFNetwork: Sets the ridge value for logistic or linear regression.
setRidge(double) - Method in class weka.classifiers.mi.MILR: Sets the ridge in the log-likelihood.
setRocAnalysis(boolean) - Method in class weka.associations.Tertius: Set the value of rocAnalysis.
setROCString(String) - Method in class weka.gui.visualize.ThresholdVisualizePanel: Set the string with ROC area
setRoot(boolean) - Method in class weka.gui.treevisualizer.Node: Set the value of root.
setRootNode(String) - Method in class weka.core.xml.XMLDocument: sets the root node to use in the XML output.
setRow(int, double[]) - Method in class weka.core.Matrix: Deprecated.

Sets a row of the matrix to the given row.
setRowDimension(int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the row dimenion of the matrix
setRowHidden(int, boolean) - Method in class weka.experiment.ResultMatrix: sets the hidden status of the row (if the index is valid)
setRowName(int, String) - Method in class weka.experiment.ResultMatrix: sets the name of the row (if the index is valid)
setRowNameWidth(int) - Method in class weka.experiment.ResultMatrix: sets the width for the row names (0 = optimal)
setRowNumber(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the row number for this sub task
setRowOrder(int[]) - Method in class weka.experiment.ResultMatrix: sets the ordering of the rows, null means default
setRsource(String) - Method in class weka.gui.treevisualizer.Edge: Set the value of rsource.
setRtarget(String) - Method in class weka.gui.treevisualizer.Edge: Set the value of rtarget.
setRuleset(FastVector) - Method in class weka.classifiers.rules.RuleStats: Set the ruleset of the stats, overwriting the old one if any
setRulesMustContain(String) - Method in class weka.associations.FPGrowth: Set the comma separated list of items that rules must contain in order to be output.
setRunColumn(int) - Method in class weka.experiment.PairedTTester: Set the value of RunColumn.
setRunColumn(int) - Method in interface weka.experiment.Tester: Set the value of RunColumn.
setRunLower(int) - Method in class weka.experiment.Experiment: Set the lower run number for the experiment.
setRunLower(int) - Method in class weka.experiment.RemoteExperiment: Set the lower run number for the experiment.
setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch: Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Sets the m_nRuns.
setRuns(int) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch: Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber: Sets the number of runs
setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Sets the m_nRuns.
setRuns(int) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: Sets the number of runs
setRunUpper(int) - Method in class weka.experiment.Experiment: Set the upper run number for the experiment.
setRunUpper(int) - Method in class weka.experiment.RemoteExperiment: Set the upper run number for the experiment.
setSampleSize(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the number of instances to sample for attribute estimation
setSampleSize(int) - Method in class weka.classifiers.functions.LeastMedSq: sets number of samples
setSampleSize(int) - Method in class weka.filters.unsupervised.instance.ReservoirSample: Sets the size of the subsample.
setSampleSizePercent(double) - Method in class weka.classifiers.meta.GridSearch: Sets the sample size for the initial grid search.
setSampleSizePercent(double) - Method in class weka.filters.supervised.instance.Resample: Sets the size of the subsample, as a percentage of the original set.
setSampleSizePercent(double) - Method in class weka.filters.unsupervised.instance.Resample: Sets the size of the subsample, as a percentage of the original set.
setSaveDialogTitle(String) - Method in class weka.gui.visualize.PrintableComponent: sets the title for the save dialog.
setSaveDialogTitle(String) - Method in interface weka.gui.visualize.PrintableHandler: sets the title for the save dialog
setSaveDialogTitle(String) - Method in class weka.gui.visualize.PrintablePanel: sets the title for the save dialog
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.ADTree: Sets whether the tree is to save instance data.
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.J48: Set whether instance data is to be saved.
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.J48graft: Set whether instance data is to be saved.
setSaveInstanceData(boolean) - Method in class weka.clusterers.Cobweb: Set the value of saveInstances.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.M5P: Set whether to save instance data at each node in the tree for visualization purposes
setSaverTemplate(Saver) - Method in class weka.gui.beans.Saver: Set the loader to use
setScale(double) - Method in class weka.filters.unsupervised.attribute.Normalize: Sets the scaling factor.
setScale(double, double) - Method in class weka.gui.visualize.JComponentWriter: sets the scale factor - is ignored since we always create a screenshot!
setScale(double, double) - Method in class weka.gui.visualize.PrintableComponent: sets the scale factor.
setScale(double, double) - Method in interface weka.gui.visualize.PrintableHandler: sets the scale factor
setScale(double, double) - Method in class weka.gui.visualize.PrintablePanel: sets the scale factor
setScalingEnabled(boolean) - Method in class weka.gui.visualize.JComponentWriter: sets whether to enable scaling
setScoreType(SelectedTag) - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: set quality measure to be used in searching for networks.
setSearch(ASSearch) - Method in class weka.attributeSelection.AttributeSelection: set the search method
setSearch(ASSearch) - Method in class weka.attributeSelection.CheckAttributeSelection: Set the search method to test.
setSearch(ASSearch) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Sets the search method
setSearch(ASSearch) - Method in class weka.classifiers.rules.DecisionTable: Sets the search method to use
setSearch(ASSearch) - Method in class weka.classifiers.rules.DTNB: Sets the search method to use
setSearch(ASSearch) - Method in class weka.filters.supervised.attribute.AttributeSelection: Set search class
setSearchAlgorithm(SearchAlgorithm) - Method in class weka.classifiers.bayes.BayesNet: Set the SearchAlgorithm used in searching for network structures.
setSearchBackwards(boolean) - Method in class weka.attributeSelection.GreedyStepwise: Set whether to search backwards instead of forwards
setSearchPath(SelectedTag) - Method in class weka.classifiers.trees.ADTree: Sets the method of searching the tree for a new insertion.
setSearchPercent(double) - Method in class weka.attributeSelection.RandomSearch: set the percentage of the search space to consider
setSearchString(String) - Method in class weka.gui.arffviewer.ArffTable: sets the search string to look for in the table, NULL or "" disables the search
setSearchTermination(int) - Method in class weka.attributeSelection.BestFirst: Set the numnber of non-improving nodes to consider before terminating search.
setSearchTermination(int) - Method in class weka.attributeSelection.LinearForwardSelection: Set the numnber of non-improving nodes to consider before terminating search.
setSecondValueIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Sets index of the second value used.
setSecondValueIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues: Sets index of the second value used.
setSeed(int) - Method in class weka.attributeSelection.AttributeSelection: set the seed for use in cross validation
setSeed(int) - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Set the seed for random number generation.
setSeed(int) - Method in class weka.attributeSelection.GeneticSearch: set the seed for random number generation
setSeed(int) - Method in class weka.attributeSelection.OneRAttributeEval: Set the random number seed for cross validation
setSeed(int) - Method in class weka.attributeSelection.RandomSearch: Set the random seed to use
setSeed(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the random number seed for randomly sampling instances.
setSeed(int) - Method in class weka.attributeSelection.ScatterSearchV1: set the seed for random number generation
setSeed(int) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Seed for cross validation subset size determination.
setSeed(int) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the seed to use for cross validation
setSeed(int) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the seed for randomizing the instances for CV-based hyperparameter selection
setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.BVDecompose: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.evaluation.EvaluationUtils: Sets the seed for randomization during cross-validation
setSeed(int) - Method in class weka.classifiers.functions.MultilayerPerceptron: This seeds the random number generator, that is used when a random number is needed for the network.
setSeed(int) - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Sets the seed value for the random number generator
setSeed(int) - Method in class weka.classifiers.functions.VotedPerceptron: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.functions.Winnow: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.meta.MultiScheme: Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableClassifier: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Set the seed for random number generation.
setSeed(long) - Method in class weka.classifiers.rules.ConjunctiveRule: sets the seed for randomizing the data
setSeed(long) - Method in class weka.classifiers.rules.JRip: Sets the seed value to use in randomizing the data
setSeed(int) - Method in class weka.classifiers.rules.PART: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.rules.Ridor
setSeed(int) - Method in class weka.classifiers.trees.J48: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.trees.RandomForest: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.trees.RandomTree: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.trees.REPTree: Set the value of Seed.
setSeed(int) - Method in class weka.clusterers.RandomizableClusterer: Set the seed for random number generation.
setSeed(int) - Method in class weka.clusterers.RandomizableDensityBasedClusterer: Set the seed for random number generation.
setSeed(int) - Method in class weka.clusterers.RandomizableSingleClustererEnhancer: Set the seed for random number generation.
setSeed(long) - Method in class weka.core.Debug.Random: Sets the seed of this random number generator using a single long seed.
setSeed(int) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Sets the seed for random number generator (that is used for selecting the first anchor point randomly).
setSeed(int) - Method in interface weka.core.Randomizable: Set the seed for random number generation.
setSeed(int) - Method in class weka.core.TestInstances: sets the seed value for the random number generator
setSeed(int) - Method in class weka.datagenerators.classifiers.classification.BayesNet: Sets the random number seed.
setSeed(int) - Method in class weka.datagenerators.DataGenerator: Sets the random number seed.
setSeed(long) - Method in class weka.filters.supervised.attribute.ClassOrder: Set randomization seed
setSeed(long) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Sets the random number seed for shuffling the dataset.
setSeed(int) - Method in class weka.filters.unsupervised.attribute.PropositionalToMultiInstance: Sets the new seed for randomizing the order of the generated data
setSeed(int) - Method in class weka.filters.unsupervised.attribute.RandomSubset: Set the seed value for the random number generator.
setSeed(long) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Sets the random number seed for shuffling the dataset.
setSeed(int) - Method in class weka.gui.beans.CrossValidationFoldMaker: Set the seed
setSeed(int) - Method in class weka.gui.beans.TrainTestSplitMaker: Set the random seed
setSeed(int) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Set a seed for random number generation (if needed).
setSeed(int) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Initializes a new random number generator using the supplied seed.
setSelectedAttributes(boolean[]) - Method in class weka.gui.AttributeSelectionPanel: Set the selected attributes in the widget.
setSelectedColumn(int) - Method in class weka.gui.arffviewer.ArffTable: sets the selected column
setSelectedRange(String) - Method in class weka.filters.unsupervised.attribute.RELAGGS: Set the range of attributes to process.
setSelectedRange(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Set the value of m_SelectedRange.
setSelectionThreshold(double) - Method in class weka.attributeSelection.RaceSearch: Set the threshold by which the AttributeSelection module can discard attributes.
setSeparatingThreshold(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Sets the separating threshold value
setSeparatingThreshold(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Sets the separating threshold value
setSeperator(String) - Method in class weka.gui.HierarchyPropertyParser: Set the seperator between levels.
setSequentialAttIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: A Sequential Attribute index is all those Attributes that are set to the specified value placed in a sequential array.
setSequentialDataset(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Sets both the Instance and Attribute indexes to a specified value
setSequentialInstanceIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: A Sequential Instance index is all those Instances that are set to the specified value placed in a sequential array.
setSerializedClassifierFile(File) - Method in class weka.filters.supervised.attribute.AddClassification: Sets the file pointing to a serialized, trained classifier.
setShape(int) - Method in class weka.gui.treevisualizer.Node: Set the value of shape.
setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel: This will set the shapes for the instances.
setShapeSize(int[]) - Method in class weka.gui.visualize.PlotData2D: Set the shape sizes for the plot data
setShapeSize(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set the shape sizes for the plot data
setShapeType(int[]) - Method in class weka.gui.visualize.PlotData2D: Set the shape type for the plot data
setShapeType(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set the shape type for the plot data
setShowAttBars(boolean) - Method in class weka.gui.visualize.VisualizePanel: Set whether the attribute bars should be shown or not.
setShowAverage(boolean) - Method in class weka.experiment.ResultMatrix: sets whether to display the average per column or not
setShowAverage(boolean) - Method in class weka.gui.experiment.OutputFormatDialog: sets whether the average for each column is displayed.
setShowClassPanel(boolean) - Method in class weka.gui.visualize.VisualizePanel: Set whether the class panel should be shown or not.
setShowCoreDistances(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Sets the flag for showCoreDistances
setShowGUI(boolean) - Method in class weka.clusterers.OPTICS: Sets the flag for displaying the GUI.
setShowReachabilityDistances(boolean) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Sets the flag for showReachabilityDistances
setShowStdDev(boolean) - Method in class weka.experiment.ResultMatrix: sets whether to display the std deviations or not
setShowStdDev(boolean) - Method in class weka.experiment.ResultMatrixSignificance: sets whether to display the std deviations or not - always false!
setShowStdDevs(boolean) - Method in class weka.experiment.PairedTTester: Set whether standard deviations are displayed or not.
setShowStdDevs(boolean) - Method in interface weka.experiment.Tester: Set whether standard deviations are displayed or not.
setShowZeroInstancesAsUnknown(boolean) - Method in class weka.gui.InstancesSummaryPanel: Set whether to show zero instances as unknown (i.e.
setShrinkage(double) - Method in class weka.classifiers.meta.AdditiveRegression: Set the shrinkage parameter
setShrinkage(double) - Method in class weka.classifiers.meta.LogitBoost: Set the value of Shrinkage.
setShrinking(boolean) - Method in class weka.classifiers.functions.LibSVM: whether to use the shrinking heuristics
setShuffle(int) - Method in class weka.classifiers.rules.Ridor
setSigma(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Sets the sigma value.
setSigma(double) - Method in class weka.classifiers.functions.supportVector.Puk: Sets the sigma value.
setSignificance(int, int, int) - Method in class weka.experiment.ResultMatrix: sets the significance at the given position (if the position is valid)
setSignificanceLevel(double) - Method in class weka.associations.Apriori: Set the value of significanceLevel.
setSignificanceLevel(double) - Method in class weka.attributeSelection.RaceSearch: Sets the significance level to use
setSignificanceLevel(double) - Method in class weka.experiment.PairedTTester: Set the value of SignificanceLevel.
setSignificanceLevel(double) - Method in interface weka.experiment.Tester: Set the value of SignificanceLevel.
setSignificanceWidth(int) - Method in class weka.experiment.ResultMatrix: sets the width for the significance (0 = optimal)
setSilent(boolean) - Method in class weka.core.AllJavadoc: sets whether to suppress output in the console
setSilent(boolean) - Method in class weka.core.Check: Set slient mode, i.e., no output at all to stdout
setSilent(boolean) - Method in class weka.core.Javadoc: sets whether to suppress output in the console
setSilent(boolean) - Method in class weka.estimators.CheckEstimator: Set slient mode, i.e., no output at all to stdout
setSIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Set the shape for creating splits.
setSingle(String) - Method in class weka.gui.ResultHistoryPanel: Sets the single-click display to view the named result.
setSingleIndex(String) - Method in class weka.core.SingleIndex: Sets the index from a string representation.
setSize(int) - Method in class weka.core.matrix.DoubleVector: Sets the size of the vector
setSize(int) - Method in class weka.core.matrix.IntVector: Sets the size of the vector.
setSize(int, int) - Method in class weka.experiment.ResultMatrix: clears the content of the matrix and sets the new size
setSizePer(double) - Method in class weka.classifiers.trees.BFTree: Set training set size.
setSizePer(double) - Method in class weka.classifiers.trees.SimpleCart: Set training set size.
setSkipIdentical(boolean) - Method in class weka.core.neighboursearch.LinearNNSearch: Sets the property to skip identical instances (with distance zero from the target) from the set of neighbours returned.
setSmoothing(boolean) - Method in class weka.classifiers.trees.m5.Rule: Smooth predictions
setSmoothingParameter(double) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Sets the smoothing value used to avoid zero WordGivenClass probabilities
setSMOReg(SMOreg) - Method in class weka.classifiers.functions.supportVector.RegOptimizer: sets the parent SVM
setSort(boolean) - Method in class weka.filters.unsupervised.attribute.AddValues: Sets whether the labels are sorted.
setSortColumn(int) - Method in class weka.experiment.PairedTTester: Set the column to sort on, -1 means the default sorting.
setSortColumn(int) - Method in interface weka.experiment.Tester: Set the column to sort on, -1 means the default sorting.
setSource(File) - Method in class weka.core.converters.AbstractFileLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.AbstractLoader: Default implementation throws an IOException.
setSource(InputStream) - Method in class weka.core.converters.AbstractLoader: Default implementation throws an IOException.
setSource(URL) - Method in class weka.core.converters.ArffLoader: Resets the Loader object and sets the source of the data set to be the supplied url.
setSource(InputStream) - Method in class weka.core.converters.ArffLoader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(File) - Method in class weka.core.converters.C45Loader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(InputStream) - Method in class weka.core.converters.CSVLoader: Resets the Loader object and sets the source of the data set to be the supplied Stream object.
setSource(File) - Method in class weka.core.converters.CSVLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(String, String, String) - Method in class weka.core.converters.DatabaseLoader: Sets the database url, user and pw
setSource(String) - Method in class weka.core.converters.DatabaseLoader: Sets the database url
setSource() - Method in class weka.core.converters.DatabaseLoader: Sets the database url using the DatabaseUtils file
setSource(URL) - Method in class weka.core.converters.LibSVMLoader: Resets the Loader object and sets the source of the data set to be the supplied url.
setSource(InputStream) - Method in class weka.core.converters.LibSVMLoader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(File) - Method in interface weka.core.converters.Loader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(InputStream) - Method in interface weka.core.converters.Loader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream) - Method in class weka.core.converters.SerializedInstancesLoader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(URL) - Method in class weka.core.converters.SVMLightLoader: Resets the Loader object and sets the source of the data set to be the supplied url.
setSource(InputStream) - Method in class weka.core.converters.SVMLightLoader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(File) - Method in class weka.core.converters.TextDirectoryLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.XRFFLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(URL) - Method in class weka.core.converters.XRFFLoader: Resets the Loader object and sets the source of the data set to be the supplied url.
setSource(InputStream) - Method in class weka.core.converters.XRFFLoader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(Node) - Method in class weka.gui.treevisualizer.Edge: Set the value of source.
setSourceCode(Classifier) - Method in class weka.classifiers.CheckSource: Sets the class to test.
setSourceCode(Filter) - Method in class weka.filters.CheckSource: Sets the class to test.
setSparseData(boolean) - Method in class weka.experiment.InstanceQuery: Sets whether data should be encoded as sparse instances
setSplitByDataSet(boolean) - Method in class weka.experiment.RemoteExperiment: Set whether sub experiments are to be created on the basis of data set.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.CrossValidationResultProducer: Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.RandomSplitResultProducer: Set the SplitEvaluator.
setSplitOnResiduals(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of splitOnResiduals.
setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.C45Split: Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(double) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Split point to be used for selection on numeric attribute.
setStartEndIndices(int, int) - Method in class weka.core.neighboursearch.balltrees.BallNode: Sets the the start and end index of the portion of the master index array that is assigned to this node.
setStartPoint(int) - Method in class weka.attributeSelection.RankSearch: Set the point at which to start evaluating the ranking
setStartSequentially(boolean) - Method in class weka.gui.beans.FlowRunner: Set whether to launch Startable beans one after the other or all in parallel.
setStartSet(String) - Method in class weka.attributeSelection.BestFirst: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.GeneticSearch: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.GreedyStepwise: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.LinearForwardSelection: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.RandomSearch: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.Ranker: Sets a starting set of attributes for the search.
setStartSet(String) - Method in interface weka.attributeSelection.StartSetHandler: Sets a starting set of attributes for the search.
setStatic() - Method in class weka.gui.beans.BeanVisual: Set the static version of the icon
setStatus(int) - Method in class weka.gui.beans.IncrementalClassifierEvent: Set the status
setStatus(int) - Method in class weka.gui.beans.InstanceEvent: Set the status
setStatusFrequency(int) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Set how often progress is reported to the status bar.
setStatusMessage(String) - Method in class weka.experiment.TaskStatusInfo: Set the status message.
setStdDev(int, int, double) - Method in class weka.experiment.ResultMatrix: sets the std deviation at the given position (if the position is valid)
setStdDevPrec(int) - Method in class weka.experiment.ResultMatrix: sets the precision for the standard deviation
setStdDevPrec(int) - Method in class weka.gui.experiment.OutputFormatDialog: Sets the precision of the std.
setStdDevWidth(int) - Method in class weka.experiment.ResultMatrix: sets the width for the std dev (0 = optimal)
setStemmer(String) - Method in class weka.core.stemmers.SnowballStemmer: sets the stemmer with the given name, e.g., "porter".
setStemmer(Stemmer) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: the stemming algorithm to use, null means no stemming at all (i.e., the NullStemmer is used).
setStepSize(int) - Method in class weka.attributeSelection.RankSearch: Set the number of attributes to add from the rankining in each iteration
setStepSize(int) - Method in class weka.experiment.LearningRateResultProducer: Set the value of StepSize.
setStopwords(File) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: sets the file containing the stopwords, null or a directory unset the stopwords.
setStringAttributes(String) - Method in class weka.core.converters.CSVLoader: Sets the attribute range to be forced to type string.
setStroke(Stroke) - Method in class weka.gui.visualize.PostscriptGraphics
setStructure(Instances) - Method in class weka.core.converters.AbstractSaver: Sets the strcuture of the instances for the first step of incremental saving.
setStructure(Instances) - Method in class weka.gui.beans.IncrementalClassifierEvent: Set the instances structure
setStructure(Instances) - Method in class weka.gui.beans.InstanceEvent: Set the instances structure
setSubFlow(Vector) - Method in class weka.gui.beans.MetaBean
setSubFlowPreview(ImageIcon) - Method in class weka.gui.beans.MetaBean
setSubsequenceLength(int) - Method in class weka.classifiers.functions.supportVector.StringKernel: Sets the length of the subsequence.
setSubsetEvaluator(ASEvaluation) - Method in class weka.attributeSelection.FilteredSubsetEval: Set the subset evaluator to use
setSubsetSizeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Set the subset evaluator to use for subset size determination.
setSubSpaceSize(double) - Method in class weka.classifiers.meta.RandomSubSpace: Sets the size of each subSpace, as a percentage of the training set size.
setSubtreeRaising(boolean) - Method in class weka.classifiers.trees.J48: Set the value of subtreeRaising.
setSubtreeRaising(boolean) - Method in class weka.classifiers.trees.J48graft: Set the value of subtreeRaising.
setSummary(int[][], int[][]) - Method in class weka.experiment.ResultMatrix: sets the non-significant and significant wins of the resultsets
setSuppressErrorMessage(boolean) - Method in class weka.classifiers.functions.SimpleLinearRegression: Turn off the error message that is reported when no useful attribute is found.
setSVMType(SelectedTag) - Method in class weka.classifiers.functions.LibLINEAR: Sets type of SVM (default SVMTYPE_L2)
setSVMType(SelectedTag) - Method in class weka.classifiers.functions.LibSVM: Sets type of SVM (default SVMTYPE_C_SVC)
setSymbols(HashMap) - Method in class weka.core.mathematicalexpression.Parser: Sets the variable - value relation to use.
setSymbols(HashMap) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Sets the variable - value relation to use.
setTableName(String) - Method in class weka.core.converters.DatabaseSaver: Sets the table's name.
setTabuList(int) - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: Sets the Tabu List length.
setTabuList(int) - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: Sets the Tabu List length.
setTarget(Object) - Method in class weka.gui.PropertySheetPanel: Sets a new target object for customisation.
setTarget(Node) - Method in class weka.gui.treevisualizer.Edge: Set the value of target.
setTargetClass(int) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary: Sets the Target Class
setTaskResult(Object) - Method in class weka.experiment.TaskStatusInfo: Set the returnable result for this task..
setTestBaseFromDialog() - Method in class weka.gui.experiment.ResultsPanel
setTestEvaluator(boolean) - Method in class weka.attributeSelection.CheckAttributeSelection: Sets whether the evaluator or the search method is being tested.
setTestSet(DataSetEvent) - Method in class weka.gui.beans.BatchClassifierEvent: Set the test set
setText(String) - Method in class weka.gui.beans.BeanVisual: Set the label for the visual.
setTFTransform(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.
setThreshold(double) - Method in class weka.attributeSelection.GreedyStepwise: Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double) - Method in class weka.attributeSelection.RaceSearch: Sets the threshold for comparisons
setThreshold(double) - Method in interface weka.attributeSelection.RankedOutputSearch: Sets a threshold by which attributes can be discarded from the ranking.
setThreshold(double) - Method in class weka.attributeSelection.Ranker: Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double) - Method in class weka.attributeSelection.ScatterSearchV1: Set the treshold
setThreshold(double) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the value of the threshold for repeating cross validation
setThreshold(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the threshold to use.
setThreshold(double) - Method in class weka.classifiers.functions.PaceRegression: Set threshold for the olsc estimator
setThreshold(double) - Method in class weka.classifiers.functions.Winnow: Set the value of Threshold.
setThreshold(double) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the threshold for the max error when predicting a numeric class.
setTimes(int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix: Multiply a value with an element and reset the element
setTimes(int, double) - Method in class weka.core.matrix.DoubleVector: Multiplies a value to an element
setTokenizer(Tokenizer) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: the tokenizer algorithm to use.
setTolerance(double) - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Set the tolerance value
setTolerance(double) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: sets the tolerance
setToleranceParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval: Set the value of T for SMO
setToleranceParameter(double) - Method in class weka.classifiers.functions.SMO: Set the value of tolerance parameter.
setToleranceParameter(double) - Method in class weka.classifiers.mi.MISMO: Set the value of tolerance parameter.
setToolTipText(String) - Method in class weka.gui.GenericObjectEditor.GOETreeNode: Set the tool tip for this node
setTop(double) - Method in class weka.gui.treevisualizer.Node: Set the value of top.
setTrainingData(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the training data to use
setTrainingTime(int) - Method in class weka.classifiers.functions.MultilayerPerceptron: Set the number of training epochs to perform.
setTrainIterations(int) - Method in class weka.classifiers.BVDecompose: Sets the maximum number of boost iterations
setTrainPercent(double) - Method in class weka.experiment.RandomSplitResultProducer: Set the value of TrainPercent.
setTrainPercent(double) - Method in class weka.gui.beans.TrainTestSplitMaker: Set the percentage of data to be in the training portion of the split
setTrainPoolSize(int) - Method in class weka.classifiers.BVDecompose: Set the number of instances in the training pool.
setTrainSet(DataSetEvent) - Method in class weka.gui.beans.BatchClassifierEvent: Set the training set
setTrainSize(int) - Method in class weka.classifiers.BVDecomposeSegCVSub: Set the training size.
setTransactionsMustContain(String) - Method in class weka.associations.FPGrowth: Set the comma separated list of items that transactions must contain in order to be considered for large item sets and rules.
setTransform(AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
setTransformAllValues(boolean) - Method in class weka.filters.supervised.attribute.NominalToBinary: Sets whether all nominal values are transformed into new attributes, not just if there are more than 2.
setTransformAllValues(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Sets whether all nominal values are transformed into new attributes, not just if there are more than 2.
setTransformBackToOriginal(boolean) - Method in class weka.attributeSelection.PrincipalComponents: Sets whether the data should be transformed back to the original space
setTransformMethod(SelectedTag) - Method in class weka.classifiers.mi.SimpleMI: Set the method used in transformation.
setTranslation(double) - Method in class weka.filters.unsupervised.attribute.Normalize: Sets the translation.
setTraversal(SelectedTag) - Method in class weka.classifiers.meta.GridSearch: Sets the type of traversal for the grid.
setTrimingThreshold(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Sets the triming thresholding value.
setTrimingThreshold(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Sets the triming thresholding value.
setTrueNegative(double) - Method in class weka.classifiers.evaluation.TwoClassStats: Sets the number of negative instances predicted as negative
setTruePositive(double) - Method in class weka.classifiers.evaluation.TwoClassStats: Sets the number of positive instances predicted as positive
setTStart(double) - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Sets the m_fTStart.
setTStart(double) - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Sets the m_fTStart.
setType(SelectedTag) - Method in class weka.attributeSelection.LinearForwardSelection: Set the type
setType(SelectedTag) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Set the type
setType(int) - Method in class weka.classifiers.functions.neural.NeuralConnection
setUndoEnabled(boolean) - Method in interface weka.core.Undoable: sets whether undo support is enabled
setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffPanel: sets whether undo support is enabled
setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffSortedTableModel: sets whether undo support is enabled
setUndoEnabled(boolean) - Method in class weka.gui.arffviewer.ArffTableModel: sets whether undo support is enabled
setUnpruned(boolean) - Method in class weka.classifiers.rules.PART: Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.J48: Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.J48graft: Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.M5Base: Use unpruned tree/rules
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.Rule: Use unpruned tree/rules
setupAttribLists() - Method in class weka.gui.visualize.MatrixPanel: Sets up the UI's attributes lists
setUpBoundaryPanel() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Sets up the BoundaryPanel object so that it is ready for plotting.
setUpComboBoxes(Instances) - Method in class weka.gui.visualize.ThresholdVisualizePanel: This overloads VisualizePanel's setUpComboBoxes to add ActionListeners to watch for when the X/Y Axis comboboxes are changed.
setUpComboBoxes(Instances) - Method in class weka.gui.visualize.VisualizePanel: initializes the comboboxes based on the data
setUpdateIncrementalClassifier(boolean) - Method in class weka.gui.beans.Classifier: Set whether an incremental classifier will be updated on the incoming instance stream.
setUpFile() - Method in class weka.gui.beans.LoaderCustomizer
setUpFile() - Method in class weka.gui.beans.SaverCustomizer: Sets up dialog for saving instances in a file
setUpFile() - Method in class weka.gui.beans.SerializedModelSaverCustomizer: Sets up dialog for saving models to a file
SetupModePanel - Class in weka.gui.experiment: This panel switches between simple and advanced experiment setup panels.
SetupModePanel() - Constructor for class weka.gui.experiment.SetupModePanel: Creates the setup panel with no initial experiment.
SetupPanel - Class in weka.gui.experiment: This panel controls the configuration of an experiment.
SetupPanel(Experiment) - Constructor for class weka.gui.experiment.SetupPanel: Creates the setup panel with the supplied initial experiment.
SetupPanel() - Constructor for class weka.gui.experiment.SetupPanel: Creates the setup panel with no initial experiment.
setUpper(int) - Method in class weka.core.Range: Sets the value of "last".
setUpper(int) - Method in class weka.core.SingleIndex: Sets the value of "last".
setUpperBoundMinSupport(double) - Method in class weka.associations.Apriori: Set the value of upperBoundMinSupport.
setUpperBoundMinSupport(double) - Method in class weka.associations.FPGrowth: Set the value of upperBoundMinSupport.
setUpperSize(int) - Method in class weka.experiment.LearningRateResultProducer: Set the value of UpperSize.
setUpVisualizableInstances(Instances) - Static method in class weka.gui.explorer.ClassifierPanel: Sets up the structure for the visualizable instances.
setUpVisualizableInstances(Instances, ClusterEvaluation) - Static method in class weka.gui.explorer.ClustererPanel: Sets up the structure for the visualizable instances.
setURL(String) - Method in class weka.core.converters.ArffLoader: Set the url to load from
setUrl(String) - Method in interface weka.core.converters.DatabaseConverter
setUrl(String) - Method in class weka.core.converters.DatabaseLoader: Sets the database URL
setUrl(String) - Method in class weka.core.converters.DatabaseSaver: Sets the database URL.
setURL(String) - Method in class weka.core.converters.LibSVMLoader: Set the url to load from.
setURL(String) - Method in class weka.core.converters.SVMLightLoader: Set the url to load from.
setURL(String) - Method in interface weka.core.converters.URLSourcedLoader: Set the url to load from
setURL(String) - Method in class weka.core.converters.XRFFLoader: Set the url to load from
setURL(String) - Method in class weka.gui.sql.ConnectionPanel: sets the URL.
setUseADTree(boolean) - Method in class weka.classifiers.bayes.BayesNet: Set whether ADTree structure is used or not
setUseAIC(boolean) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of useAIC.
setUseAIC(boolean) - Method in class weka.classifiers.trees.FT: Set the value of useAIC.
setUseAIC(boolean) - Method in class weka.classifiers.trees.lmt.LogisticBase: Set the value of useAIC.
setUseAIC(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of useAIC.
setUseArcReversal(boolean) - Method in class weka.classifiers.bayes.net.search.global.HillClimber: set use the arc reversal operation
setUseArcReversal(boolean) - Method in class weka.classifiers.bayes.net.search.local.HillClimber: set use the arc reversal operation
setUseBetterEncoding(boolean) - Method in class weka.filters.supervised.attribute.Discretize: Sets whether better encoding is to be used for MDL.
setUseCpuTime(boolean) - Method in class weka.core.Debug.Clock: enables/disables the use of CPU time (if measurement of CPU time is available).
setUseCrossOver(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
setUseCrossOver(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
setUseCrossValidation(boolean) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of useCrossValidation.
setUseCustomDimensions(boolean) - Method in class weka.gui.visualize.JComponentWriter: sets whether to use custom dimensions for the image
setUseEqualFrequency(boolean) - Method in class weka.classifiers.meta.RegressionByDiscretization: Set the value of UseEqualFrequency.
setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize: Set the value of UseEqualFrequency.
setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Set the value of UseEqualFrequency.
setUseErrorRate(boolean) - Method in class weka.classifiers.trees.BFTree: Set if use error rate in internal cross-validation.
setUseGini(boolean) - Method in class weka.classifiers.trees.BFTree: Set if use Gini index as splitting criterion.
setUseIBk(boolean) - Method in class weka.classifiers.rules.DecisionTable: Sets whether IBk should be used instead of the majority class
setUseK2Prior(boolean) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Sets the UseK2Prior.
setUseK2Prior(boolean) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Sets the UseK2Prior.
setUseKDTree(boolean) - Method in class weka.clusterers.XMeans: Sets whether to use the KDTree or not.
setUseKernelEstimator(boolean) - Method in class weka.classifiers.bayes.NaiveBayes: Sets if kernel estimator is to be used.
setUseKononenko(boolean) - Method in class weka.filters.supervised.attribute.Discretize: Sets whether Kononenko's MDL criterion is to be used.
setUseLaplace(boolean) - Method in class weka.classifiers.bayes.AODEsr: Sets if laplace correction is to be used.
setUseLaplace(boolean) - Method in class weka.classifiers.trees.J48: Set the value of useLaplace.
setUseLaplace(boolean) - Method in class weka.classifiers.trees.J48graft: Set the value of useLaplace.
setUseLeastValues(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Sets whether to use values with least or most instances
setUseLowerOrder(boolean) - Method in class weka.classifiers.functions.supportVector.PolyKernel: Sets whether to use lower-order terms.
setUseMEstimates(boolean) - Method in class weka.classifiers.bayes.AODE: Sets if m-estimates is to be used.
setUseMissing(boolean) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets the flag if missing values are treated as extra values.
setUseMutation(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
setUseMutation(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
setUseNormalization(boolean) - Method in class weka.classifiers.functions.supportVector.StringKernel: Sets whether to use normalization.
setUseOneSE(boolean) - Method in class weka.classifiers.trees.BFTree: Set if use the 1SE rule to choose final model.
setUseOneSE(boolean) - Method in class weka.classifiers.trees.SimpleCart: Set if use the 1SE rule to choose final model.
setUseORForMustContainList(boolean) - Method in class weka.associations.FPGrowth: Set whether to use OR rather than AND when considering must contain lists.
setUsePairwiseCoupling(boolean) - Method in class weka.classifiers.meta.MultiClassClassifier: Set whether to use pairwise coupling with 1-vs-1 classification to improve probability estimates.
setUseProb(boolean) - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
setUsePropertyIterator(boolean) - Method in class weka.experiment.Experiment: Sets whether the custom property iterator should be used.
setUsePropertyIterator(boolean) - Method in class weka.experiment.RemoteExperiment: Sets whether the custom property iterator should be used.
setUsePrune(boolean) - Method in class weka.classifiers.trees.SimpleCart: Set if use minimal cost-complexity pruning.
setUsePruning(boolean) - Method in class weka.classifiers.rules.JRip: Sets whether pruning is performed
setUser(String) - Method in interface weka.core.converters.DatabaseConverter
setUser(String) - Method in class weka.core.converters.DatabaseLoader: Sets the database user
setUser(String) - Method in class weka.core.converters.DatabaseSaver: Sets the database user.
setUser(String) - Method in class weka.gui.sql.ConnectionPanel: sets the User.
setUseRelativePath(boolean) - Method in class weka.core.converters.AbstractFileLoader: Set whether to use relative rather than absolute paths
setUseRelativePath(boolean) - Method in class weka.core.converters.AbstractFileSaver: Set whether to use relative rather than absolute paths
setUseRelativePath(boolean) - Method in interface weka.core.converters.FileSourcedConverter: Set whether to use relative rather than absolute paths
setUseRelativePath(boolean) - Method in class weka.gui.beans.SerializedModelSaver: Set whether to use relative paths for the directory.
setUseResampling(boolean) - Method in class weka.classifiers.meta.AdaBoostM1: Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.LogitBoost: Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set resampling mode
setUsername(String) - Method in class weka.experiment.DatabaseUtils: Set the database username.
setUserOptions(String[]) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation: sets the option the user supplied for the kernel
setUserOptions(String[]) - Method in class weka.core.CheckOptionHandler: Sets the user-supplied options (creates a copy)
setUseStars(boolean) - Method in class weka.core.AllJavadoc: sets whether to prefix the Javadoc with "*"
setUseStars(boolean) - Method in class weka.core.Javadoc: sets whether to prefix the Javadoc with "*"
setUseStoplist(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the words that are on a stoplist are to be ignored (The stop list is in weka.core.StopWords).
setUseSupervisedDiscretization(boolean) - Method in class weka.classifiers.bayes.NaiveBayes: Set whether supervised discretization is to be used.
setUseSupervisedDiscretization(boolean) - Method in class weka.classifiers.bayes.NaiveBayesUpdateable: Set whether supervised discretization is to be used.
setUseTournamentSelection(boolean) - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
setUseTournamentSelection(boolean) - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
setUseTraining(boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set if training data is to be used instead of hold out/test data
setUseTree(boolean) - Method in class weka.classifiers.trees.m5.Rule: Use an m5 tree rather than generate rules
setUseUnsmoothed(boolean) - Method in class weka.classifiers.trees.m5.M5Base: Use unsmoothed predictions
setUseVariant1(boolean) - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Sets whether to use variant 1
setValidating(boolean) - Method in class weka.core.xml.XMLDocument: sets whether to use a validating parser or not.
Note: this does clear the current DOM document!
setValidating(boolean) - Method in class weka.core.xml.XMLOptions: sets whether to use a validating parser or not.
setValidationChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the validation chunk size
setValidationSetSize(int) - Method in class weka.classifiers.functions.MultilayerPerceptron: This will set the size of the validation set.
setValidationThreshold(int) - Method in class weka.classifiers.functions.MultilayerPerceptron: This sets the threshold to use for when validation testing is being done.
setValue(double) - Method in class weka.classifiers.trees.adtree.PredictionNode: Sets the prediction value of the node.
setValue(int, double) - Method in class weka.core.BinarySparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, double) - Method in class weka.core.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, String) - Method in class weka.core.Instance: Sets a value of a nominal or string attribute to the given value.
setValue(Attribute, double) - Method in class weka.core.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(Attribute, String) - Method in class weka.core.Instance: Sets a value of an nominal or string attribute to the given value.
setValue(Object, PropertyPath.Path, Object) - Static method in class weka.core.PropertyPath: set the given value specified by the given path in the object
setValue(Object, String, Object) - Static method in class weka.core.PropertyPath: set the given value specified by the given path in the object
setValue(int, double) - Method in class weka.core.SparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(TechnicalInformation.Field, String) - Method in class weka.core.TechnicalInformation: sets the value for the given field, overwrites any previously existing one.
setValue(Object) - Method in class weka.gui.CostMatrixEditor: Sets the value of the CostMatrix to be edited.
setValue(Object) - Method in class weka.gui.GenericArrayEditor: Sets the current object array.
setValue(Object) - Method in class weka.gui.GenericObjectEditor: Sets the current Object.
setValue(Object) - Method in class weka.gui.SimpleDateFormatEditor: Sets the value of the date format to be edited.
setValueAt(Object, int, int) - Method in class weka.gui.arffviewer.ArffTableModel: sets the value in the cell at columnIndex and rowIndex to aValue.
setValueAt(Object, int, int, boolean) - Method in class weka.gui.arffviewer.ArffTableModel: sets the value in the cell at columnIndex and rowIndex to aValue.
setValueAt(Object, int, int) - Method in class weka.gui.SortedTableModel: Sets the value in the cell at columnIndex and rowIndex to aValue.
setValueAt(Object, int, int) - Method in class weka.gui.sql.ResultSetTableModel: sets the value in the cell at columnIndex and rowIndex to aValue.
setValueIndex(int) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets index of the indicator value.
setValueIndices(String) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets indices of the indicator values.
setValueIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Set which attributes are to be deleted (or kept if invert is true)
setValuesList(String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets the ranges for each attribute.
setValuesList(String, double[], double[], String) - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Sets the ranges for each attribute.
setValuesOutput(SelectedTag) - Method in class weka.associations.Tertius: Set the value of valuesOutput.
setValueSparse(int, double) - Method in class weka.core.BinarySparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.SparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setVarianceCovered(double) - Method in class weka.attributeSelection.PrincipalComponents: Sets the amount of variance to account for when retaining principal components
setVarianceCovered(double) - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Sets the amount of variance to account for when retaining principal components.
setVerbose(boolean) - Method in class weka.associations.Apriori: Sets verbose mode
setVerbose(boolean) - Method in class weka.attributeSelection.ExhaustiveSearch: set whether or not to output new best subsets as the search proceeds
setVerbose(boolean) - Method in class weka.attributeSelection.LinearForwardSelection: Set whether verbose output should be generated.
setVerbose(boolean) - Method in class weka.attributeSelection.RandomSearch: set whether or not to output new best subsets as the search proceeds
setVerbose(boolean) - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Set whether verbose output should be generated.
setVerbose(boolean) - Method in class weka.classifiers.meta.Dagging: Set the verbose state.
setVerboseOn() - Method in class weka.core.Debug.DBO: Set the verbose on flag on
setVerticalAdjustment(int) - Method in class weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.GraphPanel: Sets a new value for the vertical verticalAdjustment
setVisible(boolean) - Method in class weka.gui.Main: Shows or hides this component depending on the value of parameter b.
setVisible(boolean) - Method in class weka.gui.sql.SqlViewerDialog: displays the dialog if TRUE
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSink: Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSource: Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractEvaluator: Set the visual
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTestSetProducer: Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.Associator: Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.ClassAssigner
setVisual(BeanVisual) - Method in class weka.gui.beans.Classifier: Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.ClassValuePicker
setVisual(BeanVisual) - Method in class weka.gui.beans.Clusterer: Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.CostBenefitAnalysis
setVisual(BeanVisual) - Method in class weka.gui.beans.DataVisualizer: Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.Filter: Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.GraphViewer: Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.MetaBean: Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.ModelPerformanceChart: Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.PredictionAppender: Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.SerializedModelSaver: Set the visual for this data source.
setVisual(BeanVisual) - Method in class weka.gui.beans.StripChart: Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.TextViewer: Describe setVisual method here.
setVisual(BeanVisual) - Method in interface weka.gui.beans.Visible: Set a new visual representation
setVoteFlag(boolean) - Method in class weka.datagenerators.classifiers.classification.RDG1: Sets the vote flag.
setWeight(int) - Method in class weka.classifiers.bayes.AODE: Sets the weight for m-estimate
setWeight(double) - Method in class weka.core.Attribute: Sets the new attribute's weight
setWeight(double) - Method in class weka.core.Instance: Sets the weight of an instance.
setWeightByConfidence(boolean) - Method in class weka.classifiers.misc.VFI: Set weighting by confidence
setWeightByDistance(boolean) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the nearest neighbour weighting method
setWeightingDimensions(boolean[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Set the dimensions to be used in computing a weight for each instance generated
setWeightingDimensions(boolean[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Set which dimensions to use when computing a weight for the next instance to generate
setWeightingKernel(int) - Method in class weka.classifiers.lazy.LWL: Sets the kernel weighting method to use.
setWeightingValues(double[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Set the values of the dimensions (chosen via setWeightingDimensions) to be used when computing instance weights
setWeightingValues(double[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Set the values for the weighting dimensions to be used when computing the weight for the next instance to be generated
setWeightMethod(SelectedTag) - Method in class weka.classifiers.mi.MIWrapper: The new method for weighting the instances.
setWeightMethod(SelectedTag) - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: The new method for weighting the instances.
setWeights(String) - Method in class weka.classifiers.functions.LibLINEAR: Sets the parameters C of class i to weight[i]*C (default 1).
setWeights(String) - Method in class weka.classifiers.functions.LibSVM: Sets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
setWeightThreshold(int) - Method in class weka.classifiers.meta.AdaBoostM1: Set weight threshold
setWeightThreshold(int) - Method in class weka.classifiers.meta.LogitBoost: Set weight thresholding
setWeightTrimBeta(double) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of weightTrimBeta.
setWeightTrimBeta(double) - Method in class weka.classifiers.trees.FT: Set the value of weightTrimBeta.
setWeightTrimBeta(double) - Method in class weka.classifiers.trees.lmt.LogisticBase: Sets the option "weightTrimBeta".
setWeightTrimBeta(double) - Method in class weka.classifiers.trees.LMT: Set the value of weightTrimBeta.
setWholeDataErr(boolean) - Method in class weka.classifiers.rules.Ridor
setWindowSize(int) - Method in class weka.classifiers.lazy.IBk: Sets the maximum number of instances allowed in the training pool.
setWords(String) - Method in class weka.core.CheckScheme: Sets the comma-separated list of words to use for generating strings.
setWords(String) - Method in class weka.core.TestInstances: Sets the comma-separated list of words to use for generating strings.
setWordSeparators(String) - Method in class weka.core.CheckScheme: sets the word separators (chars) to use for assembling strings.
setWordSeparators(String) - Method in class weka.core.TestInstances: sets the word separators (chars) to use for assembling strings.
setWordsToKeep(int) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets the number of words (per class if there is a class attribute assigned) to attempt to keep.
setWordwrap(boolean) - Method in class weka.gui.LogWindow: toggles the wordwrap
override wordwrap from: http://forum.java.sun.com/thread.jspa?threadID=498535&messageID=2356174
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Associator: Sets the algorithm (associator) for this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Classifier: Sets the algorithm (classifier) for this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Clusterer: Sets the algorithm (clusterer) for this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Filter: Set the filter to be wrapped by this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Loader: Set the loader
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Saver: Set the saver
setWrappedAlgorithm(Object) - Method in interface weka.gui.beans.WekaWrapper: Set the algorithm.
setWriteOPTICSresults(boolean) - Method in class weka.clusterers.OPTICS: Sets the flag for writing actions
setX(double) - Method in class weka.classifiers.functions.neural.NeuralConnection
setX(int) - Method in class weka.gui.beans.BeanInstance: Sets the x coordinate of this bean
setX(int) - Method in class weka.gui.visualize.AttributePanel: shows which bar is the current x attribute.
setXAttribute(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the x attribute index
setXAttribute(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the x axis fixed dimension
setXBase(double) - Method in class weka.classifiers.meta.GridSearch: Set the value of the base for X.
setXExpression(String) - Method in class weka.classifiers.meta.GridSearch: Set the expression for the X value.
setXindex(int) - Method in class weka.gui.visualize.Plot2D: Set the index of the attribute to go on the x axis
setXindex(int) - Method in class weka.gui.visualize.PlotData2D: Set the x index of the data.
setXIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Set the index of the attribute for the x axis
setXLabelFreq(int) - Method in class weka.gui.beans.StripChart: Set the frequency for printing x label values
setXMax(double) - Method in class weka.classifiers.meta.GridSearch: Set the value of the Maximum of X.
setXMin(double) - Method in class weka.classifiers.meta.GridSearch: Set the value of the minimum of X.
setXML(Reader) - Method in class weka.core.xml.XMLInstances: reads the XML structure from the given reader
setXORMode(Color) - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
setXProperty(String) - Method in class weka.classifiers.meta.GridSearch: Set the X property.
setXStep(double) - Method in class weka.classifiers.meta.GridSearch: Set the value of the step size for X.
setXval(boolean) - Method in class weka.attributeSelection.AttributeSelection: do a cross validation
setXY(int, int) - Method in class weka.gui.beans.BeanInstance: Set the x and y coordinates of this bean
setY(double) - Method in class weka.classifiers.functions.neural.NeuralConnection
setY(int) - Method in class weka.gui.beans.BeanInstance: Sets the y coordinate of this bean
setY(int) - Method in class weka.gui.visualize.AttributePanel: shows which bar is the current y attribute.
setYAttribute(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the y attribute index
setYAttribute(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the y axis fixed dimension
setYBase(double) - Method in class weka.classifiers.meta.GridSearch: Set the value of the base for Y.
setYExpression(String) - Method in class weka.classifiers.meta.GridSearch: Set the expression for the Y value.
setYindex(int) - Method in class weka.gui.visualize.Plot2D: Set the index of the attribute to go on the y axis
setYindex(int) - Method in class weka.gui.visualize.PlotData2D: Set the y index of the data
setYIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Set the index of the attribute for the y axis
setYMax(double) - Method in class weka.classifiers.meta.GridSearch: Set the value of the Maximum of Y.
setYMin(double) - Method in class weka.classifiers.meta.GridSearch: Set the value of the minimum of Y.
setYProperty(String) - Method in class weka.classifiers.meta.GridSearch: Set the Y property (normally the classifier).
setYStep(double) - Method in class weka.classifiers.meta.GridSearch: Set the value of the step size for Y.
SEVERE - Static variable in class weka.core.Debug: the log level Severe
SFEntropyGain() - Method in class weka.classifiers.Evaluation: Returns the total SF, which is the null model entropy minus the scheme entropy.
SFMeanEntropyGain() - Method in class weka.classifiers.Evaluation: Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanPriorEntropy() - Method in class weka.classifiers.Evaluation: Returns the entropy per instance for the null model
SFMeanSchemeEntropy() - Method in class weka.classifiers.Evaluation: Returns the entropy per instance for the scheme
SFPriorEntropy() - Method in class weka.classifiers.Evaluation: Returns the total entropy for the null model
SFSchemeEntropy() - Method in class weka.classifiers.Evaluation: Returns the total entropy for the scheme
sgn(double) - Static method in class weka.classifiers.bayes.BayesianLogisticRegression: Sign for a given value.
shear(double, double) - Method in class weka.gui.visualize.PostscriptGraphics
shift(int, int, Instance) - Method in class weka.classifiers.trees.j48.Distribution: Shifts given instance from one bag to another one.
shift(int, int) - Method in class weka.core.matrix.IntVector: Shifts an element to another position.
shiftBeans(BeanInstance, boolean) - Method in class weka.gui.beans.MetaBean: Move coords of all inputs and outputs of this meta bean to the coords of the supplied BeanInstance.
shiftRange(int, int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution: Shifts all instances in given range from one bag to another one.
shiftToEnd(int) - Method in class weka.core.matrix.IntVector: Shifts an element to the end of the vector.
SHORT - Static variable in class weka.experiment.DatabaseUtils: Type mapping for SHORT used for reading experiment results.
show(Component, int, int) - Method in class weka.gui.GenericObjectEditor.JTreePopupMenu: Displays the menu, making sure it will fit on the screen.
showAttributes() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: displays all the attributes, returns the selected item or NULL if canceled
showChart() - Method in class weka.gui.beans.StripChart: Popup the chart panel
showDialog(Component, String) - Method in class weka.gui.ConverterFileChooser: Pops a custom file chooser dialog with a custom approve button.
showDialog() - Method in class weka.gui.experiment.OutputFormatDialog: Pops up the modal dialog and waits for cancel or a selection.
showDialog() - Method in class weka.gui.ListSelectorDialog: Pops up the modal dialog and waits for cancel or a selection.
showDialog() - Method in class weka.gui.PropertySelectorDialog: Pops up the modal dialog and waits for cancel or a selection.
showDialog() - Method in class weka.gui.ViewerDialog: Pops up the modal dialog and waits for Cancel or OK.
showDialog(Instances) - Method in class weka.gui.ViewerDialog: Pops up the modal dialog and waits for Cancel or OK.
showExplorer(String) - Method in class weka.gui.GUIChooser
showGUITipText() - Method in class weka.clusterers.OPTICS: Returns the tip text for this property.
showHistory() - Method in class weka.gui.sql.ConnectionPanel: displays the query history.
showHistory() - Method in class weka.gui.sql.QueryPanel: displays the query history.
showInputBox(Component, String, String, Object) - Static method in class weka.gui.ComponentHelper: pops up an input dialog
showKnowledgeFlow(String) - Method in class weka.gui.GUIChooser
showMemoryIsLow() - Method in class weka.core.Memory: Prints a warning message if memoryIsLow (and if GUI is present a dialog).
showMessageBox(Component, String, String, int, int) - Static method in class weka.gui.ComponentHelper: displays a message box with the given title, message, buttons and icon ant the dimension.
showOpenDialog(Component) - Method in class weka.gui.ConverterFileChooser: Pops up an "Open File" file chooser dialog.
showOutOfMemory() - Method in class weka.core.Memory: prints an error message if OutOfMemory (and if GUI is present a dialog), otherwise nothing happens.
showPopup() - Method in class weka.gui.GenericObjectEditor.CapabilitiesFilterDialog: if a JPopupMenu is set, it is displayed again.
showProperties() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: displays some properties of the instances
showPropertyDialog() - Method in class weka.gui.PropertyPanel: Displays the property edit dialog for the panel.
showResults() - Method in class weka.gui.beans.GraphViewer: Popup a result list from which the user can select a graph to view
showResults() - Method in class weka.gui.beans.TextViewer: Popup a component to display the selected text
showSaveDialog(Component) - Method in class weka.gui.ConverterFileChooser: Pops up an "Save File" file chooser dialog.
showTree() - Method in class weka.gui.HierarchyPropertyParser: Show the whole tree in text format
showValues() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: displays all the distinct values for an attribute
showWindow(Container) - Method in class weka.gui.Main: brings child frame to the top.
showWindow(Class) - Method in class weka.gui.Main: brings the first frame to the top that is of the specified window class.
shrinkageTipText() - Method in class weka.classifiers.meta.AdditiveRegression: Returns the tip text for this property
shrinkageTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
shrinkingTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
shuffleTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
sIB - Class in weka.clusterers: Cluster data using the sequential information bottleneck algorithm.

Note: only hard clustering scheme is supported.
sIB() - Constructor for class weka.clusterers.sIB
sigLevel - Variable in class weka.experiment.PairedStats: The significance level for comparisons
sigmaTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
sigmaTipText() - Method in class weka.classifiers.functions.supportVector.Puk: Returns the tip text for this property
SigmoidUnit - Class in weka.classifiers.functions.neural: This can be used by the neuralnode to perform all it's computations (as a sigmoid unit).
SigmoidUnit() - Constructor for class weka.classifiers.functions.neural.SigmoidUnit
sign() - Method in class weka.core.matrix.DoubleVector: Returns the signs of all elements in terms of -1, 0 and +1.
SIGNIFICANCE_LOSS - Static variable in class weka.experiment.ResultMatrix: loss
SIGNIFICANCE_TIE - Static variable in class weka.experiment.ResultMatrix: tie
SIGNIFICANCE_WIN - Static variable in class weka.experiment.ResultMatrix: win
significanceLevelTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
significanceLevelTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
SIGNIFICANT - Static variable in class weka.associations.Tertius: Way of handling missing values: missing as a particular value
simetricDif(ScatterSearchV1.Subset, ScatterSearchV1.Subset, int) - Method in class weka.attributeSelection.ScatterSearchV1
SimetricDiference(ScatterSearchV1.Subset, BitSet) - Method in class weka.attributeSelection.ScatterSearchV1: Calculate the Simetric Diference of two subsets
SimpleBatchFilter - Class in weka.filters: This filter is a superclass for simple batch filters.
SimpleBatchFilter() - Constructor for class weka.filters.SimpleBatchFilter
SimpleCart - Class in weka.classifiers.trees: Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method.

For more information, see:

Leo Breiman, Jerome H.
SimpleCart() - Constructor for class weka.classifiers.trees.SimpleCart
SimpleCLI - Class in weka.gui: Creates a very simple command line for invoking the main method of classes.
SimpleCLI() - Constructor for class weka.gui.SimpleCLI: Constructor
SimpleCLIPanel - Class in weka.gui: Creates a very simple command line for invoking the main method of classes.
SimpleCLIPanel() - Constructor for class weka.gui.SimpleCLIPanel: Constructor.
SimpleCLIPanel.CommandlineCompletion - Class in weka.gui: A class for commandline completion of classnames.
SimpleDateFormatEditor - Class in weka.gui: Class for editing SimpleDateFormat strings.
SimpleDateFormatEditor() - Constructor for class weka.gui.SimpleDateFormatEditor: Constructs a new SimpleDateFormatEditor.
SimpleEstimator - Class in weka.classifiers.bayes.net.estimate: SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned.
SimpleEstimator() - Constructor for class weka.classifiers.bayes.net.estimate.SimpleEstimator
SimpleFilter - Class in weka.filters: This filter contains common behavior of the SimpleBatchFilter and the SimpleStreamFilter.
SimpleFilter() - Constructor for class weka.filters.SimpleFilter
SimpleKMeans - Class in weka.clusterers: Cluster data using the k means algorithm
Valid options are:
SimpleKMeans() - Constructor for class weka.clusterers.SimpleKMeans: the default constructor
SimpleLinearRegression - Class in weka.classifiers.functions: Learns a simple linear regression model.
SimpleLinearRegression() - Constructor for class weka.classifiers.functions.SimpleLinearRegression
SimpleLinkedList - Class in weka.associations.tertius
SimpleLinkedList() - Constructor for class weka.associations.tertius.SimpleLinkedList
SimpleLinkedList.LinkedListInverseIterator - Class in weka.associations.tertius
SimpleLinkedList.LinkedListIterator - Class in weka.associations.tertius
SimpleLog() - Constructor for class weka.core.Debug.SimpleLog: default constructor, uses only stdout
SimpleLog(String) - Constructor for class weka.core.Debug.SimpleLog: Creates a logger that writes into the specified file.
SimpleLog(String, boolean) - Constructor for class weka.core.Debug.SimpleLog: Creates a logger that writes into the specified file.
SimpleLogger() - Constructor for class weka.gui.beans.FlowRunner.SimpleLogger
SimpleLogistic - Class in weka.classifiers.functions: Classifier for building linear logistic regression models.
SimpleLogistic() - Constructor for class weka.classifiers.functions.SimpleLogistic: Constructor for creating SimpleLogistic object with standard options.
SimpleLogistic(int, boolean, boolean) - Constructor for class weka.classifiers.functions.SimpleLogistic: Constructor for creating SimpleLogistic object.
SimpleMI - Class in weka.classifiers.mi: Reduces MI data into mono-instance data.
SimpleMI() - Constructor for class weka.classifiers.mi.SimpleMI
SimpleSetupPanel - Class in weka.gui.experiment: This panel controls the configuration of an experiment.
SimpleSetupPanel(Experiment) - Constructor for class weka.gui.experiment.SimpleSetupPanel: Creates the setup panel with the supplied initial experiment.
SimpleSetupPanel() - Constructor for class weka.gui.experiment.SimpleSetupPanel: Creates the setup panel with no initial experiment.
SimpleStreamFilter - Class in weka.filters: This filter is a superclass for simple stream filters.
SimpleStreamFilter() - Constructor for class weka.filters.SimpleStreamFilter
SimulatedAnnealing - Class in weka.classifiers.bayes.net.search.global: This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.

For more information see:

R.R.
SimulatedAnnealing() - Constructor for class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
SimulatedAnnealing - Class in weka.classifiers.bayes.net.search.local: This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure.

For more information see:

R.R.
SimulatedAnnealing() - Constructor for class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
SIN - Static variable in interface weka.core.mathematicalexpression.sym
SIN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
SINE - Static variable in class weka.datagenerators.clusterers.BIRCHCluster: Constant set for choice of pattern.
SingleAssociatorEnhancer - Class in weka.associations: Abstract utility class for handling settings common to meta associators that use a single base associator.
SingleAssociatorEnhancer() - Constructor for class weka.associations.SingleAssociatorEnhancer
SingleClassifierEnhancer - Class in weka.classifiers: Abstract utility class for handling settings common to meta classifiers that use a single base learner.
SingleClassifierEnhancer() - Constructor for class weka.classifiers.SingleClassifierEnhancer
SingleClustererEnhancer - Class in weka.clusterers: Meta-clusterer for enhancing a base clusterer.
SingleClustererEnhancer() - Constructor for class weka.clusterers.SingleClustererEnhancer
singleConsequence(Instances) - Static method in class weka.associations.CaRuleGeneration: generates a consequence of length 1 for a class association rule.
singleConsequence(Instances, int, FastVector) - Static method in class weka.associations.RuleGeneration: generates a consequence of length 1 for an association rule.
SingleIndex - Class in weka.core: Class representing a single cardinal number.
SingleIndex() - Constructor for class weka.core.SingleIndex: Default constructor.
SingleIndex(String) - Constructor for class weka.core.SingleIndex: Constructor to set initial index.
singletons(Instances) - Static method in class weka.associations.AprioriItemSet: Converts the header info of the given set of instances into a set of item sets (singletons).
singletons(Instances) - Static method in class weka.associations.CaRuleGeneration: Converts the header info of the given set of instances into a set of item sets (singletons).
singletons(Instances) - Static method in class weka.associations.ItemSet: Converts the header info of the given set of instances into a set of item sets (singletons).
singletons(Instances, Instances) - Static method in class weka.associations.LabeledItemSet: Converts the header info of the given set of instances into a set of item sets (singletons).
SINGULAR_DUMMY - Static variable in interface weka.gui.graphvisualizer.GraphConstants: SINGULAR_DUMMY node - node with only one outgoing edge i.e.
SingularValueDecomposition - Class in weka.core.matrix: Singular Value Decomposition.
SingularValueDecomposition(Matrix) - Constructor for class weka.core.matrix.SingularValueDecomposition: Construct the singular value decomposition
size() - Method in class weka.associations.tertius.SimpleLinkedList
size() - Method in class weka.classifiers.CostMatrix: The number of rows (and columns)
size() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of classes.
size() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns the size of the point set.
size() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Returns the number of keys in this hashtable.
size() - Method in class weka.classifiers.rules.JRip.RipperRule: the number of antecedents of the rule
size() - Method in class weka.classifiers.rules.Rule: The size of the rule.
size() - Method in interface weka.clusterers.forOPTICSAndDBScan.Databases.Database: Returns the size of the database (the number of dataObjects in the database)
size() - Method in class weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase: Returns the size of the database (the number of dataObjects in the database)
size() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.PriorityQueue: Returns the queue's size
size() - Method in class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue: Returns the queue's size
size() - Method in class weka.core.FastVector: Returns the vector's current size.
size() - Method in class weka.core.matrix.DoubleVector: Gets the size of the vector.
size() - Method in class weka.core.matrix.IntVector: Gets the size of the vector.
size() - Method in class weka.core.PropertyPath.Path: returns the number of path elements of this structure
size() - Method in class weka.core.Queue: Gets queue's size.
size() - Method in class weka.core.Tee: returns the number of streams currently in the list.
size() - Method in class weka.core.Trie: Returns the number of elements in this collection.
size() - Method in class weka.core.Trie.TrieNode: returns the number of stored strings, i.e., leaves
size() - Method in class weka.core.xml.MethodHandler: returns the number of currently stored Methods
sizePerTipText() - Method in class weka.classifiers.trees.BFTree: Returns the tip text for this property
sizePerTipText() - Method in class weka.classifiers.trees.SimpleCart: Returns the tip text for this property
skipIdenticalTipText() - Method in class weka.core.neighboursearch.LinearNNSearch: Returns the tip text for this property.
SlidingMidPointOfWidestSide - Class in weka.core.neighboursearch.kdtrees: The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
SlidingMidPointOfWidestSide() - Constructor for class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide
sm(double, double) - Static method in class weka.core.Utils: Tests if a is smaller than b.
SMALL - Static variable in class weka.core.Utils: The small deviation allowed in double comparisons.
SMO - Class in weka.classifiers.functions: Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
SMO() - Constructor for class weka.classifiers.functions.SMO
SMO.BinarySMO - Class in weka.classifiers.functions: Class for building a binary support vector machine.
smoothingParameterTipText() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns the tip text for this property
SMOreg - Class in weka.classifiers.functions: SMOreg implements the support vector machine for regression.
SMOreg() - Constructor for class weka.classifiers.functions.SMOreg
smOrEq(double, double) - Static method in class weka.core.Utils: Tests if a is smaller or equal to b.
SMOset - Class in weka.classifiers.functions.supportVector: Stores a set of integer of a given size.
SMOset(int) - Constructor for class weka.classifiers.functions.supportVector.SMOset: Creates a new set of the given size.
SMOTE - Class in weka.filters.supervised.instance: Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE).
SMOTE() - Constructor for class weka.filters.supervised.instance.SMOTE
SNAPSHOT - Static variable in class weka.core.Version: True if snapshot
SnowballStemmer - Class in weka.core.stemmers: A wrapper class for the Snowball stemmers.
SnowballStemmer() - Constructor for class weka.core.stemmers.SnowballStemmer: initializes the stemmer ("porter").
SnowballStemmer(String) - Constructor for class weka.core.stemmers.SnowballStemmer: initializes the stemmer with the given stemmer.
solve(Matrix) - Method in class weka.core.matrix.CholeskyDecomposition: Solve A*X = B
solve(Matrix) - Method in class weka.core.matrix.LUDecomposition: Solve A*X = B
solve(Matrix) - Method in class weka.core.matrix.Matrix: Solve A*X = B
solve(Matrix) - Method in class weka.core.matrix.QRDecomposition: Least squares solution of A*X = B
solve(double[]) - Method in class weka.core.Matrix: Deprecated.

Solve A*X = B using backward substitution.
solveTranspose(Matrix) - Method in class weka.core.matrix.Matrix: Solve X*A = B, which is also A'*X' = B'
solveTriangle(Matrix, double[], boolean, boolean[]) - Static method in class weka.core.Optimization: Solve the linear equation of TX=B where T is a triangle matrix It can be solved using back/forward substitution, with O(N^2) complexity
sort(Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
sort() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Sorts the point values of the discrete function.
sort(int) - Method in class weka.core.Instances: Sorts the instances based on an attribute.
sort(Attribute) - Method in class weka.core.Instances: Sorts the instances based on an attribute.
sort() - Method in class weka.core.matrix.DoubleVector: Sorts the array in place
sort() - Method in class weka.core.matrix.IntVector: Sorts the elements in place
sort(int[]) - Static method in class weka.core.Utils: Sorts a given array of integers in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(double[]) - Static method in class weka.core.Utils: Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(int) - Method in class weka.gui.SortedTableModel: sorts the table over the given column (ascending)
sort(int, boolean) - Method in class weka.gui.SortedTableModel: sorts the table over the given column, either ascending or descending
sortArray(double[]) - Method in class weka.classifiers.mi.MIOptimalBall: Sort the array.
sortClassesByRoot(String) - Static method in class weka.gui.GenericObjectEditor: parses the given string of classes separated by ", " and returns the a hashtable with as many entries as there are different root elements in the class names (the key is the root element).
SortContainer(Comparable, int) - Constructor for class weka.gui.SortedTableModel.SortContainer: Initializes the container.
SortedTableModel - Class in weka.gui: Represents a TableModel with sorting functionality.
SortedTableModel() - Constructor for class weka.gui.SortedTableModel: initializes with no model
SortedTableModel(TableModel) - Constructor for class weka.gui.SortedTableModel: initializes with the given model
SortedTableModel.SortContainer - Class in weka.gui: Helper class for sorting the columns.
sortInstances() - Method in class weka.gui.arffviewer.ArffPanel: sorts the instances via the currently selected column
sortInstances(int) - Method in class weka.gui.arffviewer.ArffSortedTableModel: sorts the instances via the given attribute
sortInstances(int) - Method in class weka.gui.arffviewer.ArffTableModel: sorts the instances via the given attribute
sortInstances() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: sorts the current selected attribute
sortTipText() - Method in class weka.filters.unsupervised.attribute.AddValues: Returns the tip text for this property
sortWithIndex() - Method in class weka.core.matrix.DoubleVector: Sorts the array in place with index returned
sortWithIndex(int, int, IntVector) - Method in class weka.core.matrix.DoubleVector: Sorts the array in place with index changed
sortWithNoMissingValues(double[]) - Static method in class weka.core.Utils: Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
Sourcable - Interface in weka.classifiers: Interface for classifiers that can be converted to Java source.
Sourcable - Interface in weka.filters: Interface for filters that can be converted to Java source.
sourceClass(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.GraftSplit: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NBTreeSplit: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Method not in use
SOUTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
spaceHorizontal(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: space out set of nodes evenly between left and right most node in the list
spaceVertical(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: space out set of nodes evenly between top and bottom most node in the list
SPARSE1 - Static variable in class weka.filters.unsupervised.attribute.RandomProjection: distribution type: sparse 1
SPARSE2 - Static variable in class weka.filters.unsupervised.attribute.RandomProjection: distribution type: sparse 2
sparseDataTipText() - Method in class weka.experiment.InstanceQuery: Returns the tip text for this property
sparseIndices() - Method in class weka.classifiers.functions.SMO: Returns the indices in sparse format.
sparseIndices() - Method in class weka.classifiers.mi.MISMO: Returns the indices in sparse format.
SparseInstance - Class in weka.core: Class for storing an instance as a sparse vector.
SparseInstance(Instance) - Constructor for class weka.core.SparseInstance: Constructor that generates a sparse instance from the given instance.
SparseInstance(SparseInstance) - Constructor for class weka.core.SparseInstance: Constructor that copies the info from the given instance.
SparseInstance(double, double[]) - Constructor for class weka.core.SparseInstance: Constructor that generates a sparse instance from the given parameters.
SparseInstance(double, double[], int[], int) - Constructor for class weka.core.SparseInstance: Constructor that inititalizes instance variable with given values.
SparseInstance(int) - Constructor for class weka.core.SparseInstance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
SparseToNonSparse - Class in weka.filters.unsupervised.instance: An instance filter that converts all incoming sparse instances into non-sparse format.
SparseToNonSparse() - Constructor for class weka.filters.unsupervised.instance.SparseToNonSparse
sparseWeights() - Method in class weka.classifiers.functions.SMO: Returns the weights in sparse format.
sparseWeights() - Method in class weka.classifiers.mi.MISMO: Returns the weights in sparse format.
SpecialFunctions - Class in weka.core: Class implementing some mathematical functions.
SpecialFunctions() - Constructor for class weka.core.SpecialFunctions
SPECIFIC_VALUE - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
SPegasos - Class in weka.classifiers.functions: Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
SPegasos() - Constructor for class weka.classifiers.functions.SPegasos
sphere - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the sphere size
splash(Image) - Static method in class weka.gui.SplashWindow: Open's a splash window using the specified image.
splash(URL) - Static method in class weka.gui.SplashWindow: Open's a splash window using the specified image.
SplashWindow - Class in weka.gui: A Splash window.
split(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Splits the given set of instances into subsets.
split() - Method in class weka.classifiers.trees.m5.RuleNode: Finds an attribute and split point for this node
splitAtt() - Method in class weka.classifiers.trees.m5.RuleNode: Get the index of the splitting attribute for this node
splitAttr() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the attribute used in this split
splitAttr() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Returns the attribute used in this split
splitAttr() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the attribute used in this split
SplitCriterion - Class in weka.classifiers.trees.j48: Abstract class for computing splitting criteria with respect to distributions of class values.
SplitCriterion() - Constructor for class weka.classifiers.trees.j48.SplitCriterion
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit: Computes entropy for given distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit: Computes entropy of test distribution with respect to training distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit: This method is a straightforward implementation of the gain ratio criterion for the given distribution.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit: This method computes the gain ratio in the same way C4.5 does.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit: This method is a straightforward implementation of the information gain criterion for the given distribution.
splitCritValue(Distribution, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit: This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit: This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion: Computes result of splitting criterion for given distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions.
splitCritValue(Distribution, Distribution, int) - Method in class weka.classifiers.trees.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions and given number of classes.
splitCritValue(Distribution, Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions and given default distribution.
splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.Antd
splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.NominalAntd: Implements the splitData function.
splitData(Instances, double, double) - Method in class weka.classifiers.rules.JRip.NumericAntd: Implements the splitData function.
splitEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit: Computes entropy after splitting without considering the class values.
SplitEvaluate - Interface in weka.classifiers.trees.m5: Interface for objects that determine a split point on an attribute
SplitEvaluator - Interface in weka.experiment: Interface to objects able to generate a fixed set of results for a particular split of a dataset.
splitEvaluatorTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
splitItemSet(int, int[]) - Method in class weka.associations.PriorEstimation: splits an item set into premise and consequence and constructs therefore an association rule.
splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.BallSplitter: Splits a node into two.
splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.MedianDistanceFromArbitraryPoint: Splits a ball into two.
splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.MedianOfWidestDimension: Splits a ball into two.
splitNode(BallNode, int) - Method in class weka.core.neighboursearch.balltrees.PointsClosestToFurthestChildren: Splits a ball into two.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Splits a node into two.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.KMeansInpiredMethod: Splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.MedianOfWidestDimension: Splits a node into two based on the median value of the dimension in which the points have the widest spread.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.MidPointOfWidestDimension: Splits a node into two based on the midpoint value of the dimension in which the points have the widest spread.
splitNode(KDTreeNode, int, double[][], double[][]) - Method in class weka.core.neighboursearch.kdtrees.SlidingMidPointOfWidestSide: Splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
splitOnResidualsTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
splitOptions(String) - Static method in class weka.core.Utils: Split up a string containing options into an array of strings, one for each option.
splitPoint() - Method in class weka.classifiers.trees.j48.GraftSplit
splitPointTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
Splitter - Class in weka.classifiers.trees.adtree: Abstract class representing a splitter node in an alternating tree.
Splitter() - Constructor for class weka.classifiers.trees.adtree.Splitter
splitVal() - Method in class weka.classifiers.trees.m5.RuleNode: Get the split point for this node
splitValue() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the split value
splitValue() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Returns the split value
splitValue() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the split value
SpreadSubsample - Class in weka.filters.supervised.instance: Produces a random subsample of a dataset.
SpreadSubsample() - Constructor for class weka.filters.supervised.instance.SpreadSubsample
sqDifference(int, double, double) - Method in class weka.core.EuclideanDistance: Returns the squared difference of two values of an attribute.
SqlViewer - Class in weka.gui.sql: Represents a little tool for querying SQL databases.
SqlViewer(JFrame) - Constructor for class weka.gui.sql.SqlViewer: initializes the SqlViewer.
SqlViewerDialog - Class in weka.gui.sql: A little dialog containing the SqlViewer.
SqlViewerDialog(JFrame) - Constructor for class weka.gui.sql.SqlViewerDialog: initializes the dialog
SQRT - Static variable in interface weka.core.mathematicalexpression.sym
sqrt() - Method in class weka.core.matrix.DoubleVector: Returns the square-root of all the elements in the vector
sqrt() - Method in class weka.core.matrix.Matrix: returns the square root of the matrix, i.e., X from the equation X*X = A.
Steps in the Calculation (see sqrtm in Matlab):
perform eigenvalue decomposition
[V,D]=eig(A) take the square root of all elements in D (only the ones with positive sign are considered for further computation)
S=sqrt(D) calculate the root
X=V*S/V, which can be also written as X=(V'\(V*S)')'
Note: since this method uses other high-level methods, it generates several instances of matrices.
SQRT - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
square() - Method in class weka.core.matrix.DoubleVector: Returns the squared vector
square(double) - Static method in class weka.core.matrix.Maths: Returns the square of a value
stableSort(double[]) - Static method in class weka.core.Utils: Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
Stack<T> - Class in weka.core.neighboursearch.covertrees: Class implementing a stack.
Stack() - Constructor for class weka.core.neighboursearch.covertrees.Stack: Constructor.
Stack(int) - Constructor for class weka.core.neighboursearch.covertrees.Stack: Constructor.
Stacking - Class in weka.classifiers.meta: Combines several classifiers using the stacking method.
Stacking() - Constructor for class weka.classifiers.meta.Stacking
StackingC - Class in weka.classifiers.meta: Implements StackingC (more efficient version of stacking).

For more information, see

A.K.
StackingC() - Constructor for class weka.classifiers.meta.StackingC: The constructor.
Standardize - Class in weka.filters.unsupervised.attribute: Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
Standardize() - Constructor for class weka.filters.unsupervised.attribute.Standardize
start() - Method in class weka.core.Debug.Clock: saves the current system time (or CPU time) in msec as start time
start() - Method in class weka.gui.beans.Loader: Start loading
start() - Method in interface weka.gui.beans.Startable: Start the flow running
start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Start the plotting thread
start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Start processing
start_production() - Method in class weka.core.mathematicalexpression.Parser: Indicates start production.
start_production() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Indicates start production.
start_state() - Method in class weka.core.mathematicalexpression.Parser: Indicates start state.
start_state() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Parser: Indicates start state.
Startable - Interface in weka.gui.beans: Interface to something that is a start point for a flow and can be launched programatically.
startApp() - Static method in class weka.gui.beans.KnowledgeFlow: Static method that can be called from a running program to launch the KnowledgeFlow
startClock() - Method in class weka.core.Debug: starts the clock
startLoading() - Method in class weka.gui.beans.Loader: Start loading data
startPlotThread() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Starts the plotting thread.
startPointTipText() - Method in class weka.attributeSelection.RankSearch: Returns the tip text for this property
StartSetHandler - Interface in weka.attributeSelection: Interface for search methods capable of doing something sensible given a starting set of attributes.
startSetTipText() - Method in class weka.attributeSelection.BestFirst: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.GreedyStepwise: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.RandomSearch: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
startUpComplete() - Method in interface weka.gui.beans.StartUpListener
StartUpListener - Interface in weka.gui.beans: Interface to something that can be notified of a successful startup
stateChanged(ChangeEvent) - Method in class weka.gui.arffviewer.ArffPanel: Invoked when the target of the listener has changed its state.
stateChanged(ChangeEvent) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: Invoked when the target of the listener has changed its state.
stateChanged(ChangeEvent) - Method in class weka.gui.LogWindow: Invoked when the target of the listener has changed its state.
stateChanged(ChangeEvent) - Method in class weka.gui.sql.ResultPanel: Invoked when the target of the listener has changed its state.
stateChanged(ChangeEvent) - Method in class weka.gui.ViewerDialog: Invoked when the target of the listener has changed its state.
Statistics - Class in weka.core: Class implementing some distributions, tests, etc.
Statistics() - Constructor for class weka.core.Statistics
Stats - Class in weka.classifiers.trees.j48: Class implementing a statistical routine needed by J48 to compute its error estimate.
Stats() - Constructor for class weka.classifiers.trees.j48.Stats
Stats - Class in weka.experiment: A class to store simple statistics
Stats() - Constructor for class weka.experiment.Stats
statusFrequencyTipText() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Return a tip text string for this property
statusMessage(String) - Method in class weka.gui.beans.FlowRunner.SimpleLogger
statusMessage(String) - Method in class weka.gui.beans.LogPanel: Sends the supplied message to the status area.
statusMessage(String) - Method in interface weka.gui.Logger: Sends the supplied message to the status line.
statusMessage(String) - Method in class weka.gui.LogPanel: Sends the supplied message to the status line.
statusMessage(String) - Method in class weka.gui.SysErrLog: Sends the supplied message to the status line.
stdDev - Variable in class weka.experiment.Stats: The std deviation of values at the last calculateDerived() call
stealPoints(MiddleOutConstructor.TempNode, Vector, Vector) - Method in class weka.core.neighboursearch.balltrees.MiddleOutConstructor: Removes points from old anchors that are nearer to the given new anchor and adds them to the list of points of the new anchor.
stem(String) - Method in class weka.core.stemmers.IteratedLovinsStemmer: Iterated stemming of the given word.
stem(String) - Method in class weka.core.stemmers.LovinsStemmer: Returns the stemmed version of the given word.
stem(String) - Method in class weka.core.stemmers.NullStemmer: Returns the word as it is.
stem(String) - Method in class weka.core.stemmers.SnowballStemmer: Returns the word in its stemmed form.
stem(String) - Method in interface weka.core.stemmers.Stemmer: Stems the given word and returns the stemmed version
Stemmer - Interface in weka.core.stemmers: Interface for all stemming algorithms.
stemmerTipText() - Method in class weka.core.stemmers.SnowballStemmer: Returns the tip text for this property.
stemmerTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
Stemming - Class in weka.core.stemmers: A helper class for using the stemmers.
Stemming() - Constructor for class weka.core.stemmers.Stemming
stemString(String) - Method in class weka.core.stemmers.LovinsStemmer: Stems everything in the given string.
STEP_FIELD_NAME - Static variable in class weka.experiment.LearningRateResultProducer: The name of the key field containing the learning rate step number
steplsqr(PaceMatrix, IntVector, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Stepwise least squares QR-decomposition of the problem A x = b
stepSizeTipText() - Method in class weka.attributeSelection.RankSearch: Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
stop() - Method in class weka.core.Debug.Clock: saves the current system (or CPU time) in msec as stop time
STOP - Static variable in class weka.core.Trie.TrieNode: the stop character
stop() - Method in class weka.gui.beans.AbstractDataSink: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractEvaluator: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTestSetProducer: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTrainingSetProducer: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.Associator: Stop any associator action
stop() - Method in interface weka.gui.beans.BeanCommon: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.ClassAssigner
stop() - Method in class weka.gui.beans.Classifier: Stop any classifier action
stop() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Try and stop any action
stop() - Method in class weka.gui.beans.ClassValuePicker
stop() - Method in class weka.gui.beans.Clusterer: Stop any clusterer action
stop() - Method in class weka.gui.beans.ClustererPerformanceEvaluator: Try and stop any action
stop() - Method in class weka.gui.beans.CostBenefitAnalysis: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.CrossValidationFoldMaker: Stop any action
stop() - Method in class weka.gui.beans.Filter: Stop all action if possible
stop() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Stop all action
stop() - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Stop any action (if possible).
stop() - Method in class weka.gui.beans.Loader: Stop any loading action.
stop() - Method in class weka.gui.beans.MetaBean: Stop all encapsulated beans
stop() - Method in class weka.gui.beans.PredictionAppender
stop() - Method in class weka.gui.beans.Saver: Stops the bean
stop() - Method in class weka.gui.beans.SerializedModelSaver: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.StripChart: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.TestSetMaker
stop() - Method in class weka.gui.beans.TextViewer: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.TrainingSetMaker: Stop any action
stop() - Method in class weka.gui.beans.TrainTestSplitMaker: Stop processing
stopAllFlows() - Method in class weka.gui.beans.FlowRunner
stopClock(String) - Method in class weka.core.Debug: stops the clock and prints the message associated with the time, but only if the logging is enabled.
stopMonitoring() - Method in class weka.gui.MemoryUsagePanel: stops the monitoring thread.
stoppingCriterion() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: This method implements the stopping criterion function.
stopPlotting() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Stop the plotting thread
stopPlotting() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Stops the plotting thread.
stopThreads() - Method in class weka.core.Memory: stops all the current threads, to make a restart possible
Stopwords - Class in weka.core: Class that can test whether a given string is a stop word.
Stopwords() - Constructor for class weka.core.Stopwords: initializes the stopwords (based on Rainbow).
stopwordsTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
store(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache: Stores the specified values in the cahce table for easy retrieval.
StratifiedRemoveFolds - Class in weka.filters.supervised.instance: This filter takes a dataset and outputs a specified fold for cross validation.
StratifiedRemoveFolds() - Constructor for class weka.filters.supervised.instance.StratifiedRemoveFolds
stratify(Instances, int, Random) - Static method in class weka.classifiers.rules.RuleStats: Stratify the given data into the given number of bags based on the class values.
stratify(int) - Method in class weka.core.Instances: Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).
StreamableFilter - Interface in weka.filters: Interface for filters can work with a stream of instances.
STRING - Static variable in class weka.core.Attribute: Constant set for attributes with string values.
STRING - Static variable in class weka.experiment.DatabaseUtils: Type mapping for STRING used for reading experiment results.
STRING - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: lexical states
STRING - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
stringAttributesTipText() - Method in class weka.core.converters.CSVLoader: Returns the tip text for this property.
StringCompare() - Constructor for class weka.core.ClassDiscovery.StringCompare
stringFreeStructure() - Method in class weka.core.Instances: Create a copy of the structure if the data has string or relational attributes, "cleanses" string types (i.e.
StringKernel - Class in weka.classifiers.functions.supportVector: Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].

For more information, see

Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.
StringKernel() - Constructor for class weka.classifiers.functions.supportVector.StringKernel: default constructor
StringKernel(Instances, int, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.StringKernel: creates a new StringKernel object.
StringLocator - Class in weka.core: This class locates and records the indices of String attributes, recursively in case of Relational attributes.
StringLocator(Instances) - Constructor for class weka.core.StringLocator: initializes the StringLocator with the given data
StringLocator(Instances, int, int) - Constructor for class weka.core.StringLocator: Initializes the StringLocator with the given data.
StringLocator(Instances, int[]) - Constructor for class weka.core.StringLocator: Initializes the AttributeLocator with the given data.
stringSize(FontMetrics) - Method in class weka.gui.treevisualizer.Edge: This will calculate how large a rectangle using the FontMetrics passed that the lines of the label will take up
stringSize(FontMetrics) - Method in class weka.gui.treevisualizer.Node: This will return the width and height of the rectangle that the text will fit into.
stringToLevel(String) - Static method in class weka.core.Debug.Log: turns the string representing a level, e.g., "FINE" or "ALL" into the corresponding level (case-insensitive).
stringToLevel(String) - Static method in class weka.core.Debug: turns the string representing a level, e.g., "FINE" or "ALL" into the corresponding level (case-insensitive).
StringToNominal - Class in weka.filters.unsupervised.attribute: Converts a string attribute (i.e.
StringToNominal() - Constructor for class weka.filters.unsupervised.attribute.StringToNominal
StringToWordVector - Class in weka.filters.unsupervised.attribute: Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
StringToWordVector() - Constructor for class weka.filters.unsupervised.attribute.StringToWordVector: Default constructor.
StringToWordVector(int) - Constructor for class weka.filters.unsupervised.attribute.StringToWordVector: Constructor that allows specification of the target number of words in the output.
stringValue(int) - Method in class weka.core.Instance: Returns the value of a nominal, string, date, or relational attribute for the instance as a string.
stringValue(Attribute) - Method in class weka.core.Instance: Returns the value of a nominal, string, date, or relational attribute for the instance as a string.
StripChart - Class in weka.gui.beans: Bean that can display a horizontally scrolling strip chart.
StripChart() - Constructor for class weka.gui.beans.StripChart
StripChartBeanInfo - Class in weka.gui.beans: Bean info class for the strip chart bean
StripChartBeanInfo() - Constructor for class weka.gui.beans.StripChartBeanInfo
StripChartCustomizer - Class in weka.gui.beans: GUI Customizer for the strip chart bean
StripChartCustomizer() - Constructor for class weka.gui.beans.StripChartCustomizer
StructureProducer - Interface in weka.gui.beans: Interface for something that can describe the structure of what is encapsulated in a named event type as an empty set of Instances (i.e.
STYLE_STDERR - Static variable in class weka.gui.LogWindow: the name of the style for stderr
STYLE_STDOUT - Static variable in class weka.gui.LogWindow: the name of the style for stdout
sub(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution: Subtracts given instance from given bag.
subFlowContains(BeanInstance) - Method in class weka.gui.beans.MetaBean
subList(int, int) - Method in class weka.core.neighboursearch.covertrees.Stack: Returns a sublist of the elements in the stack.
subpath(int) - Method in class weka.core.PropertyPath.Path: returns a subpath of the current structure, starting with the specified element index up to the end
subpath(int, int) - Method in class weka.core.PropertyPath.Path: returns a subpath of the current structure, starting with the specified element index up.
subsequenceLengthTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the tip text for this property
Subset(BitSet, double) - Constructor for class weka.attributeSelection.ScatterSearchV1.Subset
SubsetByExpression - Class in weka.filters.unsupervised.instance: Filters instances according to a user-specified expression.

Grammar:

boolexpr_list ::= boolexpr_list boolexpr_part | boolexpr_part;

boolexpr_part ::= boolexpr:e {: parser.setResult(e); :} ;

boolexpr ::= BOOLEAN
| true
| false
| expr < expr
| expr <= expr
| expr > expr
| expr >= expr
| expr = expr
| ( boolexpr )
| not boolexpr
| boolexpr and boolexpr
| boolexpr or boolexpr
| ATTRIBUTE is STRING
;

expr ::= NUMBER
| ATTRIBUTE
| ( expr )
| opexpr
| funcexpr
;

opexpr ::= expr + expr
| expr - expr
| expr * expr
| expr / expr
;

funcexpr ::= abs ( expr )
| sqrt ( expr )
| log ( expr )
| exp ( expr )
| sin ( expr )
| cos ( expr )
| tan ( expr )
| rint ( expr )
| floor ( expr )
| pow ( expr for base , expr for exponent )
| ceil ( expr )
;

Notes:
- NUMBER
any integer or floating point number
(but not in scientific notation!)
- STRING
any string surrounded by single quotes;
the string may not contain a single quote though.
- ATTRIBUTE
the following placeholders are recognized for
attribute values:
- CLASS for the class value in case a class attribute is set.
- ATTxyz with xyz a number from 1 to # of attributes in the
dataset, representing the value of indexed attribute.

Examples:
- extracting only mammals and birds from the 'zoo' UCI dataset:
(CLASS is 'mammal') or (CLASS is 'bird')
- extracting only animals with at least 2 legs from the 'zoo' UCI dataset:
(ATT14 >= 2)
- extracting only instances with non-missing 'wage-increase-second-year'
from the 'labor' UCI dataset:
not ismissing(ATT3)

Valid options are:
SubsetByExpression() - Constructor for class weka.filters.unsupervised.instance.SubsetByExpression
subsetDL(double, double, double) - Static method in class weka.classifiers.rules.RuleStats: Subset description length:
S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95
subsetEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Returns the estimate of optimal subset selection.
SubsetEvaluator - Interface in weka.attributeSelection: Interface for attribute subset evaluators.
subsetEvaluatorTipText() - Method in class weka.attributeSelection.FilteredSubsetEval: Returns the tip text for this property
subsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
subsetSizeEvaluatorTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the tip text for this property
SubsetSizeForwardSelection - Class in weka.attributeSelection: SubsetSizeForwardSelection:

Extension of LinearForwardSelection.
SubsetSizeForwardSelection() - Constructor for class weka.attributeSelection.SubsetSizeForwardSelection: Constructor
SubspaceCluster - Class in weka.datagenerators.clusterers: A data generator that produces data points in hyperrectangular subspace clusters.
SubspaceCluster() - Constructor for class weka.datagenerators.clusterers.SubspaceCluster: initializes the generator, sets the number of clusters to 0, since user has to specify them explicitly
SubspaceClusterDefinition - Class in weka.datagenerators.clusterers: A single cluster for the SubspaceCluster datagenerator
Valid options are:
SubspaceClusterDefinition() - Constructor for class weka.datagenerators.clusterers.SubspaceClusterDefinition: initializes the cluster, without a parent cluster (necessary for GOE)
SubspaceClusterDefinition(ClusterGenerator) - Constructor for class weka.datagenerators.clusterers.SubspaceClusterDefinition: initializes the cluster with default values
subSpaceSizeTipText() - Method in class weka.classifiers.meta.RandomSubSpace: Returns the tip text for this property
substitute(String) - Method in class weka.core.Environment: Substitute a variable names for their values in the given string.
substract(AlgVector) - Method in class weka.core.AlgVector: Returns the difference of this vector minus another.
subsumes(Rule) - Method in class weka.associations.tertius.Rule: Test if this rule subsumes another rule.
subsumptionTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
subtract(AprioriItemSet) - Method in class weka.associations.AprioriItemSet: Subtracts an item set from another one.
subtract(Distribution) - Method in class weka.classifiers.trees.j48.Distribution: Subtracts the given distribution from this one.
subtract(double, double) - Method in class weka.experiment.PairedStats: Removes an observed pair of values.
subtract(double[], double[]) - Method in class weka.experiment.PairedStats: Removes an array of observed pair of values.
subtract(double) - Method in class weka.experiment.Stats: Removes a value to the observed values (no checking is done that the value being removed was actually added).
subtract(double, double) - Method in class weka.experiment.Stats: Subtracts a value that has been seen n times from the observed values
subtreeRaisingTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
subtreeRaisingTipText() - Method in class weka.classifiers.trees.J48graft: Returns the tip text for this property
subvector(int, int) - Method in class weka.core.matrix.DoubleVector: Returns a subvector.
subvector(IntVector) - Method in class weka.core.matrix.DoubleVector: Returns a subvector.
subvector(int, int) - Method in class weka.core.matrix.IntVector: Returns a subvector.
subvector(IntVector) - Method in class weka.core.matrix.IntVector: Returns a subvector as indexed by an IntVector.
sum() - Method in class weka.core.matrix.DoubleVector: Returns the sum of all elements in the vector.
sum(double[]) - Static method in class weka.core.Utils: Computes the sum of the elements of an array of doubles.
sum(int[]) - Static method in class weka.core.Utils: Computes the sum of the elements of an array of integers.
sum - Variable in class weka.experiment.Stats: The sum of values seen
sum2(int, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Squared sum of a column or row in a matrix
sum2(boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Squared sum of columns or rows of a matrix
sum2() - Method in class weka.core.matrix.DoubleVector: Returns the squared sum of all elements in the vector.
sum2(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Returns ||u-v||^2
Summarizable - Interface in weka.core: Interface to something that provides a short textual summary (as opposed to toString() which is usually a fairly complete description) of itself.
sumOfWeights() - Method in class weka.core.Instances: Computes the sum of all the instances' weights.
sumSq - Variable in class weka.experiment.Stats: The sum of values squared seen
SupervisedFilter - Interface in weka.filters: Interface for filters that make use of a class attribute.
support() - Method in class weka.associations.ItemSet: Outputs the support for an item set.
support() - Method in class weka.associations.LabeledItemSet: Outputs the support for an item set.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.ChisqMixture: Contructs the set of support points for mixture estimation.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Contructs the set of support points for mixture estimation.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.NormalMixture: Contructs the set of support points for mixture estimation.
supports(Capabilities) - Method in class weka.core.Capabilities: Returns true if the currently set capabilities support at least all of the capabiliites of the given Capabilities object (checks only the enum!)
supportsCustomEditor() - Method in class weka.gui.CostMatrixEditor: Indicates whether the cost matrix can be edited in a GUI, which it can.
supportsCustomEditor() - Method in class weka.gui.FileEditor: Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.GenericArrayEditor: Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.GenericObjectEditor: Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.SimpleDateFormatEditor: Indicates whether the date format can be edited in a GUI, which it can.
supportsMaybe(Capabilities) - Method in class weka.core.Capabilities: Returns true if the currently set capabilities support (or have a dependency) at least all of the capabilities of the given Capabilities object (checks only the enum!)
svd() - Method in class weka.core.matrix.Matrix: Singular Value Decomposition
SVMAttributeEval - Class in weka.attributeSelection: SVMAttributeEval :

Evaluates the worth of an attribute by using an SVM classifier.
SVMAttributeEval() - Constructor for class weka.attributeSelection.SVMAttributeEval: Constructor
SVMLightLoader - Class in weka.core.converters: Reads a source that is in svm light format.

For more information about svm light see:

http://svmlight.joachims.org/
SVMLightLoader() - Constructor for class weka.core.converters.SVMLightLoader
SVMLightSaver - Class in weka.core.converters: Writes to a destination that is in svm light format.

For more information about svm light see:

http://svmlight.joachims.org/
Valid options are:
SVMLightSaver() - Constructor for class weka.core.converters.SVMLightSaver: Constructor.
SVMOutput(int, Instance) - Method in class weka.classifiers.functions.SMO.BinarySMO: Computes SVM output for given instance.
SVMOutput(Instance) - Method in class weka.classifiers.functions.supportVector.RegOptimizer
SVMTYPE_C_SVC - Static variable in class weka.classifiers.functions.LibSVM: SVM type C-SVC (classification)
SVMTYPE_EPSILON_SVR - Static variable in class weka.classifiers.functions.LibSVM: SVM type epsilon-SVR (regression)
SVMTYPE_L1LOSS_SVM_DUAL - Static variable in class weka.classifiers.functions.LibLINEAR: SVM solver type L1-loss support vector machines (dual)
SVMTYPE_L2_LR - Static variable in class weka.classifiers.functions.LibLINEAR: SVM solver type L2-regularized logistic regression
SVMTYPE_L2LOSS_SVM - Static variable in class weka.classifiers.functions.LibLINEAR: SVM solver type L2-loss support vector machines (primal)
SVMTYPE_L2LOSS_SVM_DUAL - Static variable in class weka.classifiers.functions.LibLINEAR: SVM solver type L2-loss support vector machines (dual)
SVMTYPE_MCSVM_CS - Static variable in class weka.classifiers.functions.LibLINEAR: SVM solver type multi-class support vector machines by Crammer and Singer
SVMTYPE_NU_SVC - Static variable in class weka.classifiers.functions.LibSVM: SVM type nu-SVC (classification)
SVMTYPE_NU_SVR - Static variable in class weka.classifiers.functions.LibSVM: SVM type nu-SVR (regression)
SVMTYPE_ONE_CLASS_SVM - Static variable in class weka.classifiers.functions.LibSVM: SVM type one-class SVM (classification)
SVMTypeTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
SVMTypeTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
swap(int, int) - Method in class weka.core.FastVector: Swaps two elements in the vector.
swap(int, int) - Method in class weka.core.Instances: Swaps two instances in the set.
swap(int, int) - Method in class weka.core.matrix.DoubleVector: Swaps the values stored at i and j
swap(int, int) - Method in class weka.core.matrix.IntVector: Swaps the values stored at i and j
SwapValues - Class in weka.filters.unsupervised.attribute: Swaps two values of a nominal attribute.
SwapValues() - Constructor for class weka.filters.unsupervised.attribute.SwapValues
switchToAdvanced(Experiment) - Method in class weka.gui.experiment.SetupModePanel: Switches to the advanced setup mode.
switchToSimple(Experiment) - Method in class weka.gui.experiment.SetupModePanel: Switches to the simple setup mode only if allowed to.
sym - Interface in weka.core.mathematicalexpression: CUP generated interface containing symbol constants.
sym - Interface in weka.filters.unsupervised.instance.subsetbyexpression: CUP generated interface containing symbol constants.
symmetricalUncertainty(double[][]) - Static method in class weka.core.ContingencyTables: Calculates the symmetrical uncertainty for base 2.
SymmetricalUncertAttributeEval - Class in weka.attributeSelection: SymmetricalUncertAttributeEval :

Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
SymmetricalUncertAttributeEval() - Constructor for class weka.attributeSelection.SymmetricalUncertAttributeEval: Constructor
Sync(BayesNet) - Method in class weka.classifiers.bayes.net.BIFReader: synchronizes the node ordering of this Bayes network with those in the other network (if possible).
synopsis() - Method in class weka.core.Option: Returns the option's synopsis.
SysErrLog - Class in weka.gui: This Logger just sends messages to System.err.
SysErrLog() - Constructor for class weka.gui.SysErrLog
SystemInfo - Class in weka.core: This class prints some information about the system setup, like Java version, JVM settings etc.
SystemInfo() - Constructor for class weka.core.SystemInfo: initializes the object and reads the system information

T

TAB_INSTANCES - Static variable in class weka.gui.arffviewer.ArffPanel: the name of the tab for instances that were set directly
tableChanged(TableModelEvent) - Method in class weka.gui.arffviewer.ArffTable: This fine grain notification tells listeners the exact range of cells, rows, or columns that changed.
tableChanged(TableModelEvent) - Method in class weka.gui.SortedTableModel: This fine grain notification tells listeners the exact range of cells, rows, or columns that changed.
TableEntry(int, double, double, double, KStarCache.TableEntry) - Constructor for class weka.classifiers.lazy.kstar.KStarCache.TableEntry: Constructor
tableExists(String) - Method in class weka.experiment.DatabaseUtils: Checks that a given table exists.
tableNameTipText() - Method in class weka.core.converters.DatabaseSaver: Returns the tip text for this property.
tabuListTipText() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch
tabuListTipText() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch
TabuSearch - Class in weka.classifiers.bayes.net.search.global: This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
TabuSearch() - Constructor for class weka.classifiers.bayes.net.search.global.TabuSearch
TabuSearch - Class in weka.classifiers.bayes.net.search.local: This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
TabuSearch() - Constructor for class weka.classifiers.bayes.net.search.local.TabuSearch
Tag - Class in weka.core: A Tag simply associates a numeric ID with a String description.
Tag() - Constructor for class weka.core.Tag: Creates a new default Tag
Tag(int, String) - Constructor for class weka.core.Tag: Creates a new Tag instance.
Tag(int, String, String) - Constructor for class weka.core.Tag: Creates a new Tag instance.
Tag(int, String, String, boolean) - Constructor for class weka.core.Tag
TAG_ATTRIBUTE - Static variable in class weka.core.xml.XMLInstances: the attribute element
TAG_ATTRIBUTES - Static variable in class weka.core.xml.XMLInstances: the attributes element
TAG_BODY - Static variable in class weka.core.xml.XMLInstances: the body element
TAG_DATASET - Static variable in class weka.core.xml.XMLInstances: the root element
TAG_HEADER - Static variable in class weka.core.xml.XMLInstances: the header element
TAG_INSTANCE - Static variable in class weka.core.xml.XMLInstances: the instance element
TAG_INSTANCES - Static variable in class weka.core.xml.XMLInstances: the data element
TAG_LABEL - Static variable in class weka.core.xml.XMLInstances: the label element
TAG_LABELS - Static variable in class weka.core.xml.XMLInstances: the labels element
TAG_METADATA - Static variable in class weka.core.xml.XMLInstances: the meta-data element
TAG_NOTES - Static variable in class weka.core.xml.XMLInstances: the notes element
TAG_OBJECT - Static variable in class weka.core.xml.XMLSerialization: the tag for an object
TAG_OPTION - Static variable in class weka.core.xml.XMLOptions: tag for a single option.
TAG_OPTIONS - Static variable in class weka.core.xml.XMLOptions: tag for a list of options.
TAG_PROPERTY - Static variable in class weka.core.xml.XMLInstances: the property element
TAG_VALUE - Static variable in class weka.core.xml.XMLInstances: the value element
TAGS_ALGORITHM - Static variable in class weka.filters.supervised.attribute.PLSFilter: the types of algorithm
TAGS_ALGORITHM - Static variable in class weka.filters.unsupervised.attribute.Wavelet: the types of algorithm
TAGS_ALGORITHMTYPE - Static variable in class weka.classifiers.mi.MILR: the types of algorithms
TAGS_ATTRIBUTETYPE - Static variable in class weka.filters.unsupervised.attribute.RemoveType: Tag allowing selection of attribute type to delete
TAGS_CLUSTERSUBTYPE - Static variable in class weka.datagenerators.clusterers.SubspaceCluster: the tags for the cluster types
TAGS_CLUSTERTYPE - Static variable in class weka.datagenerators.clusterers.SubspaceCluster: the tags for the cluster types
TAGS_CV_TYPE - Static variable in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: the score types
TAGS_DSTRS_TYPE - Static variable in class weka.filters.unsupervised.attribute.RandomProjection: The types of distributions that can be used for calculating the random matrix
TAGS_ESTIMATOR - Static variable in class weka.classifiers.functions.PaceRegression: estimator types
TAGS_EVAL - Static variable in class weka.classifiers.meta.ThresholdSelector: The evaluation modes
TAGS_EVALUATION - Static variable in class weka.classifiers.meta.GridSearch: evaluation
TAGS_EVALUATION - Static variable in class weka.classifiers.rules.DecisionTable
TAGS_FILTER - Static variable in class weka.classifiers.functions.GaussianProcesses: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.functions.SMO: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.functions.SMOreg: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MDD: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MIDD: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MIEMDD: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MIOptimalBall: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MISMO: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.classifiers.mi.MISVM: The filter to apply to the training data
TAGS_FILTER - Static variable in class weka.filters.unsupervised.attribute.StringToWordVector: Specifies whether document's (instance's) word frequencies are to be normalized.
TAGS_FORMAT - Static variable in class weka.core.Debug.Clock: the output formats
TAGS_GUI - Static variable in class weka.gui.Main: GUI tags.
TAGS_HYPER_METHOD - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
TAGS_INPUTORDER - Static variable in class weka.datagenerators.clusterers.BIRCHCluster: the input order tags
TAGS_KERNELTYPE - Static variable in class weka.classifiers.functions.LibSVM: the different kernel types
TAGS_LINK_TYPE - Static variable in class weka.clusterers.HierarchicalClusterer
TAGS_MATRIX_SOURCE - Static variable in class weka.attributeSelection.CostSensitiveASEvaluation: Specify possible sources of the cost matrix
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.meta.CostSensitiveClassifier: Specify possible sources of the cost matrix
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.meta.MetaCost: Specify possible sources of the cost matrix
TAGS_MEASURE - Static variable in class weka.classifiers.meta.ThresholdSelector: the measure to use
TAGS_METHOD - Static variable in class weka.classifiers.meta.MultiClassClassifier: The error correction modes
TAGS_MISSING - Static variable in class weka.classifiers.lazy.KStar: Define possible missing value handling methods
TAGS_MODEL - Static variable in class weka.classifiers.trees.FT: possible model types.
TAGS_OPTIMIZE - Static variable in class weka.classifiers.meta.ThresholdSelector: How to determine which class value to optimize for
TAGS_PADDING - Static variable in class weka.filters.unsupervised.attribute.Wavelet: the types of padding
TAGS_PATTERN - Static variable in class weka.datagenerators.clusterers.BIRCHCluster: the pattern tags
TAGS_PREPROCESSING - Static variable in class weka.filters.supervised.attribute.PLSFilter: the types of preprocessing
TAGS_PRIOR - Static variable in class weka.classifiers.bayes.BayesianLogisticRegression
TAGS_PRUNETYPE - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost: The pruning types
TAGS_PRUNING - Static variable in class weka.classifiers.functions.supportVector.StringKernel: Pruning methods
TAGS_PRUNING - Static variable in class weka.classifiers.trees.BFTree: pruning strategy
TAGS_RANGE - Static variable in class weka.classifiers.meta.ThresholdSelector: Type of correction applied to threshold range
TAGS_RULES - Static variable in class weka.classifiers.meta.Vote: combination rules
TAGS_SCORE_TYPE - Static variable in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm: the score types
TAGS_SEARCH_METHOD - Static variable in class weka.attributeSelection.LinearForwardSelection
TAGS_SEARCHPATH - Static variable in class weka.classifiers.trees.ADTree: The search modes
TAGS_SELECTION - Static variable in class weka.associations.Apriori: Metric types.
TAGS_SELECTION - Static variable in class weka.associations.FPGrowth.AssociationRule: Tags for display in the GUI
TAGS_SELECTION - Static variable in class weka.attributeSelection.BestFirst: search directions
TAGS_SELECTION - Static variable in class weka.attributeSelection.RaceSearch
TAGS_SELECTION - Static variable in class weka.attributeSelection.ScatterSearchV1
TAGS_SELECTION - Static variable in class weka.classifiers.functions.LinearRegression: Attribute selection methods
TAGS_SELECTION - Static variable in class weka.classifiers.functions.SPegasos: Loss functions to choose from
TAGS_SVMTYPE - Static variable in class weka.classifiers.functions.LibLINEAR: SVM solver types
TAGS_SVMTYPE - Static variable in class weka.classifiers.functions.LibSVM: SVM types
TAGS_TESTMETHOD - Static variable in class weka.classifiers.mi.MIWrapper: the test methods
TAGS_TRANSFORMMETHOD - Static variable in class weka.classifiers.mi.SimpleMI: the transformation methods
TAGS_TRAVERSAL - Static variable in class weka.classifiers.meta.GridSearch: traversal
TAGS_TYPE - Static variable in class weka.attributeSelection.LinearForwardSelection
TAGS_TYPE - Static variable in class weka.attributeSelection.SubsetSizeForwardSelection
TAGS_TYPE - Static variable in class weka.filters.unsupervised.attribute.Add: the attribute type.
TAGS_WEIGHTING - Static variable in class weka.classifiers.lazy.IBk: possible instance weighting methods.
TAGS_WEIGHTMETHOD - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: weight methods
TAN - Class in weka.classifiers.bayes.net.search.global: This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.

For more information see:

N.
TAN() - Constructor for class weka.classifiers.bayes.net.search.global.TAN
TAN - Class in weka.classifiers.bayes.net.search.local: This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree.

For more information see:

N.
TAN() - Constructor for class weka.classifiers.bayes.net.search.local.TAN
TAN - Static variable in interface weka.core.mathematicalexpression.sym
TAN - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
target(double[], double[][], int, double[]) - Method in class weka.classifiers.mi.MINND: Compute the target function to minimize in gradient descent The formula is:
1/2*sum[i=1..p](f(X, Xi)-var(Y, Yi))^2
where p is the number of exemplars and Y is the class label.
TargetMetaInfo - Class in weka.core.pmml: Class to encapsulate information about a Target.
Task - Interface in weka.experiment: Interface to something that can be remotely executed as a task.
taskFinished() - Method in class weka.gui.LogPanel: Record a task ending
taskFinished() - Method in interface weka.gui.TaskLogger: Tells the task logger that a task has completed
taskFinished() - Method in class weka.gui.WekaTaskMonitor: Tells the panel that a task has completed
TaskLogger - Interface in weka.gui: Interface for objects that display log and display information on running tasks.
taskStarted() - Method in class weka.gui.LogPanel: Record the starting of a new task
taskStarted() - Method in interface weka.gui.TaskLogger: Tells the task logger that a new task has been started
taskStarted() - Method in class weka.gui.WekaTaskMonitor: Tells the panel that a new task has been started
TaskStatusInfo - Class in weka.experiment: A class holding information for tasks being executed on RemoteEngines.
TaskStatusInfo() - Constructor for class weka.experiment.TaskStatusInfo
tauVal(double[][]) - Static method in class weka.core.ContingencyTables: Computes Goodman and Kruskal's tau-value for a contingency table.
TechnicalInformation - Class in weka.core: Used for paper references in the Javadoc and for BibTex generation.
TechnicalInformation(TechnicalInformation.Type) - Constructor for class weka.core.TechnicalInformation: Initializes the information with the given type
TechnicalInformation(TechnicalInformation.Type, String) - Constructor for class weka.core.TechnicalInformation: Initializes the information with the given type
TechnicalInformation.Field - Enum in weka.core: the possible fields
TechnicalInformation.Type - Enum in weka.core: the different types of information
TechnicalInformationHandler - Interface in weka.core: For classes that are based on some kind of publications.
TechnicalInformationHandlerJavadoc - Class in weka.core: Generates Javadoc comments from the TechnicalInformationHandler's data.
TechnicalInformationHandlerJavadoc() - Constructor for class weka.core.TechnicalInformationHandlerJavadoc: default constructor
Tee - Class in weka.core: This class pipelines print/println's to several PrintStreams.
Tee() - Constructor for class weka.core.Tee: initializes the object, with a default printstream.
Tee(PrintStream) - Constructor for class weka.core.Tee: initializes the object with the given default printstream, e.g., System.out.
Tertius - Class in weka.associations: Finds rules according to confirmation measure (Tertius-type algorithm).

For more information see:

P.
Tertius() - Constructor for class weka.associations.Tertius: Constructor that sets the options to the default values.
test(Attribute) - Method in class weka.core.Capabilities: Test the given attribute, whether it can be processed by the handler, given its capabilities.
test(Attribute, boolean) - Method in class weka.core.Capabilities: Test the given attribute, whether it can be processed by the handler, given its capabilities.
test(Instances) - Method in class weka.core.Capabilities: Tests the given data, whether it can be processed by the handler, given its capabilities.
test(Instances, int, int) - Method in class weka.core.Capabilities: Tests a certain range of attributes of the given data, whether it can be processed by the handler, given its capabilities.
test(String[]) - Static method in class weka.core.Instances: Method for testing this class.
Test - Class in weka.datagenerators: Class to represent a test.
Test(int, double, Instances) - Constructor for class weka.datagenerators.Test: Constructor
Test(int, double, Instances, boolean) - Constructor for class weka.datagenerators.Test: Constructor
TEST - Static variable in class weka.gui.beans.BatchClustererEvent
testCapabilities(Instances, int) - Method in class weka.estimators.Estimator: Test if the estimator can handle the data.
testCV(int, int) - Method in class weka.core.Instances: Creates the test set for one fold of a cross-validation on the dataset.
Tester - Interface in weka.experiment: Interface for different kinds of Testers in the Experimenter.
TestInstances - Class in weka.core: Generates artificial datasets for testing.
TestInstances() - Constructor for class weka.core.TestInstances: the default constructor
TESTMETHOD_ARITHMETIC - Static variable in class weka.classifiers.mi.MIWrapper: arithmetic average
TESTMETHOD_GEOMETRIC - Static variable in class weka.classifiers.mi.MIWrapper: geometric average
TESTMETHOD_MAXPROB - Static variable in class weka.classifiers.mi.MIWrapper: max probability of positive bag
TestSetEvent - Class in weka.gui.beans: Event encapsulating a test set
TestSetEvent(Object, Instances) - Constructor for class weka.gui.beans.TestSetEvent: Creates a new TestSetEvent
TestSetEvent(Object, Instances, int, int) - Constructor for class weka.gui.beans.TestSetEvent: Creates a new TestSetEvent
TestSetEvent(Object, Instances, int, int, int, int) - Constructor for class weka.gui.beans.TestSetEvent: Creates a new TestSetEvent
TestSetListener - Interface in weka.gui.beans: Interface to something that can accpet test set events
TestSetMaker - Class in weka.gui.beans: Bean that accepts data sets and produces test sets
TestSetMaker() - Constructor for class weka.gui.beans.TestSetMaker
TestSetMakerBeanInfo - Class in weka.gui.beans: Bean info class for the test set maker bean.
TestSetMakerBeanInfo() - Constructor for class weka.gui.beans.TestSetMakerBeanInfo
TestSetProducer - Interface in weka.gui.beans: Interface to something that can produce test sets
testType() - Method in class weka.classifiers.trees.j48.GraftSplit: returns the test type
testWithFail(Attribute) - Method in class weka.core.Capabilities: tests the given attribute by calling the test(Attribute,boolean) method and throws an exception if the test fails.
testWithFail(Attribute, boolean) - Method in class weka.core.Capabilities: tests the given attribute by calling the test(Attribute,boolean) method and throws an exception if the test fails.
testWithFail(Instances, int, int) - Method in class weka.core.Capabilities: tests the given data by calling the test(Instances,int,int) method and throws an exception if the test fails.
testWithFail(Instances) - Method in class weka.core.Capabilities: tests the given data by calling the test(Instances) method and throws an exception if the test fails.
TEXT - Static variable in class weka.experiment.DatabaseUtils: Type mapping for TEXT used for reading, e.g., text blobs.
TextDirectoryLoader - Class in weka.core.converters: Loads all text files in a directory and uses the subdirectory names as class labels.
TextDirectoryLoader() - Constructor for class weka.core.converters.TextDirectoryLoader: default constructor
TextEvent - Class in weka.gui.beans: Event that encapsulates some textual information
TextEvent(Object, String, String) - Constructor for class weka.gui.beans.TextEvent: Creates a new TextEvent instance.
TextListener - Interface in weka.gui.beans: Interface to something that can process a TextEvent
TextViewer - Class in weka.gui.beans: Bean that collects and displays pieces of text
TextViewer() - Constructor for class weka.gui.beans.TextViewer
TextViewerBeanInfo - Class in weka.gui.beans: Bean info class for the text viewer
TextViewerBeanInfo() - Constructor for class weka.gui.beans.TextViewerBeanInfo
TFTransformTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
theoryDL(int) - Method in class weka.classifiers.rules.RuleStats: The description length of the theory for a given rule.
Threshold - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Threshold for binary classification of probabilisitic estimate
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.CostCurve: attribute name: Threshold
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: Threshold
ThresholdCurve - Class in weka.classifiers.evaluation: Generates points illustrating prediction tradeoffs that can be obtained by varying the threshold value between classes.
ThresholdCurve() - Constructor for class weka.classifiers.evaluation.ThresholdCurve
ThresholdDataEvent - Class in weka.gui.beans: Event encapsulating classifier performance data based on varying a threshold over the classifier's predicted probabilities
ThresholdDataEvent(Object, PlotData2D) - Constructor for class weka.gui.beans.ThresholdDataEvent
ThresholdDataEvent(Object, PlotData2D, Attribute) - Constructor for class weka.gui.beans.ThresholdDataEvent
ThresholdDataListener - Interface in weka.gui.beans: Interface to something that can accept ThresholdDataEvents
ThresholdSelector - Class in weka.classifiers.meta: A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
ThresholdSelector() - Constructor for class weka.classifiers.meta.ThresholdSelector: Constructor.
thresholdTipText() - Method in class weka.attributeSelection.GreedyStepwise: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.ScatterSearchV1: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
thresholdTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
thresholdTipText() - Method in class weka.classifiers.functions.PaceRegression: Returns the tip text for this property
thresholdTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
thresholdTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
ThresholdVisualizePanel - Class in weka.gui.visualize: This panel is a VisualizePanel, with the added ablility to display the area under the ROC curve if an ROC curve is chosen.
ThresholdVisualizePanel() - Constructor for class weka.gui.visualize.ThresholdVisualizePanel: default constructor
TIE_STRING - Variable in class weka.experiment.ResultMatrix: tie string
TIME - Static variable in class weka.experiment.DatabaseUtils: Type mapping for TIME used for reading TIME columns.
times(int, int, int, PaceMatrix, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Multiplication between a row (or part of a row) of the first matrix and a column (or part or a column) of the second matrix.
TIMES - Static variable in interface weka.core.mathematicalexpression.sym
times(double) - Method in class weka.core.matrix.DoubleVector: Multiplies a scalar
times(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Multiplies another DoubleVector element by element
times(double) - Method in class weka.core.matrix.Matrix: Multiply a matrix by a scalar, C = s*A
times(Matrix) - Method in class weka.core.matrix.Matrix: Linear algebraic matrix multiplication, A * B
TIMES - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
timesEquals(double) - Method in class weka.classifiers.functions.pace.DiscreteFunction: All function values are multiplied by a double
timesEquals(double) - Method in class weka.core.matrix.DoubleVector: Multiply a vector by a scalar in place, u = s * u
timesEquals(DoubleVector) - Method in class weka.core.matrix.DoubleVector: Multiplies another DoubleVector element by element in place
timesEquals(double) - Method in class weka.core.matrix.Matrix: Multiply a matrix by a scalar in place, A = s*A
TimeSeriesDelta - Class in weka.filters.unsupervised.attribute: An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
TimeSeriesDelta() - Constructor for class weka.filters.unsupervised.attribute.TimeSeriesDelta
TimeSeriesTranslate - Class in weka.filters.unsupervised.attribute: An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
TimeSeriesTranslate() - Constructor for class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Timestamp() - Constructor for class weka.core.Debug.Timestamp: creates a timestamp with the current date and time and the default format.
Timestamp(String) - Constructor for class weka.core.Debug.Timestamp: creates a timestamp with the current date and time and the specified format.
Timestamp(Date) - Constructor for class weka.core.Debug.Timestamp: creates a timestamp with the given date and the default format.
Timestamp(Date, String) - Constructor for class weka.core.Debug.Timestamp: creates a timestamp with the given date and format.
TIMESTAMP - Static variable in class weka.experiment.DatabaseUtils: Type mapping for TIMESTAMP used for reading java.sql.Timestamp columns
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer: The name of the result field containing the timestamp
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer: The name of the result field containing the timestamp
TO_BE_RUN - Static variable in class weka.experiment.TaskStatusInfo
toArray() - Method in class weka.core.FastVector: Returns all the elements of this vector as an array
toArray() - Method in class weka.core.Trie: Returns an array containing all of the elements in this collection.
toArray(T[]) - Method in class weka.core.Trie: Returns an array containing all of the elements in this collection; the runtime type of the returned array is that of the specified array.
toArray() - Method in class weka.core.xml.XMLOptions: returns the current DOM document as string array.
toArray() - Method in class weka.gui.CheckBoxList.CheckBoxListModel: Returns an array containing all of the elements in this list in the correct order.
toBibTex() - Method in class weka.core.TechnicalInformation: Returns a BibTex string representing this technical information.
toClassDetailsString() - Method in class weka.classifiers.Evaluation: Generates a breakdown of the accuracy for each class (with default title), incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
toClassDetailsString(String) - Method in class weka.classifiers.Evaluation: Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
toCommandLine() - Method in class weka.core.xml.XMLOptions: returns the given DOM document as command line.
toCumulativeMarginDistributionString() - Method in class weka.classifiers.Evaluation: Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
toDoubleArray() - Method in class weka.core.BinarySparseInstance: Returns the values of each attribute as an array of doubles.
toDoubleArray() - Method in class weka.core.Instance: Returns the values of each attribute as an array of doubles.
toDoubleArray() - Method in class weka.core.SparseInstance: Returns the values of each attribute as an array of doubles.
tokenize(String) - Method in class weka.core.tokenizers.AlphabeticTokenizer: Sets the string to tokenize.
tokenize(String) - Method in class weka.core.tokenizers.NGramTokenizer: Sets the string to tokenize.
tokenize(String) - Method in class weka.core.tokenizers.Tokenizer: Sets the string to tokenize.
tokenize(Tokenizer, String[]) - Static method in class weka.core.tokenizers.Tokenizer: initializes the given tokenizer with the given options and runs the tokenizer over all the remaining strings in the options array.
tokenize(String) - Method in class weka.core.tokenizers.WordTokenizer: Sets the string to tokenize.
tokenize(String) - Method in class weka.gui.HierarchyPropertyParser: Tokenize the given string based on the seperator and put the tokens into an array of strings
Tokenizer - Class in weka.core.tokenizers: A superclass for all tokenizer algorithms.
Tokenizer() - Constructor for class weka.core.tokenizers.Tokenizer
tokenizerTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
Tolerance - Variable in class weka.classifiers.bayes.BayesianLogisticRegression: Tolerance criteria for the stopping criterion.
toleranceParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
toleranceParameterTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
toleranceParameterTipText() - Method in class weka.classifiers.mi.MISMO: Returns the tip text for this property
toleranceTipText() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Returns the tip text for this property
toleranceTipText() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Returns the tip text for this property
toMatlab() - Method in class weka.classifiers.CostMatrix: converts the Matrix into a single line Matlab string: matrix is enclosed by parentheses, rows are separated by semicolon and single cells by blanks, e.g., [1 2; 3 4].
toMatlab() - Method in class weka.core.matrix.Matrix: converts the Matrix into a single line Matlab string: matrix is enclosed by parentheses, rows are separated by semicolon and single cells by blanks, e.g., [1 2; 3 4].
toMatlab() - Method in class weka.core.Matrix: Deprecated.

converts the Matrix into a single line Matlab string: matrix is enclosed by parentheses, rows are separated by semicolon and single cells by blanks, e.g., [1 2; 3 4].
toMatrixString() - Method in class weka.classifiers.Evaluation: Calls toMatrixString() with a default title.
toMatrixString(String) - Method in class weka.classifiers.Evaluation: Outputs the performance statistics as a classification confusion matrix.
toMegaByte(long) - Static method in class weka.core.Memory: returns the amount of bytes as MB
toNominalString(Instances) - Method in class weka.associations.gsp.Element: Returns a String representation of an Element where the numeric value of each event/item is represented by its respective nominal value.
toNominalString(Instances) - Method in class weka.associations.gsp.Sequence: Returns a String representation of a Sequences where the numeric value of each event/item is represented by its respective nominal value.
toOptionList(Tag[]) - Static method in class weka.core.Tag: returns a list that can be used in the listOption methods to list all the available ID strings, e.g.: <0|1|2> or <what|ever>
toOptionSynopsis(Tag[]) - Static method in class weka.core.Tag: returns a string that can be used in the listOption methods to list all the available options, i.e., "\t\tID = Text\n" for each option
toOutput() - Method in class weka.gui.visualize.JComponentWriter: saves the current component to the currently set file.
toOutput(JComponentWriter, JComponent, File) - Static method in class weka.gui.visualize.JComponentWriter: outputs the given component with the given writer in the specified file
toOutput(JComponentWriter, JComponent, File, int, int) - Static method in class weka.gui.visualize.JComponentWriter: outputs the given component with the given writer in the specified file.
TopDownConstructor - Class in weka.core.neighboursearch.balltrees: The class implementing the TopDown construction method of ball trees.
TopDownConstructor() - Constructor for class weka.core.neighboursearch.balltrees.TopDownConstructor: Creates a new instance of TopDownConstructor.
topOfTree() - Method in class weka.classifiers.trees.m5.Rule: Returns the top of the tree.
toPrologString() - Method in class weka.datagenerators.Test: Returns the test represented by a string in Prolog notation.
toResultsString() - Method in class weka.attributeSelection.AttributeSelection: get a description of the attribute selection
toSource(String) - Method in class weka.classifiers.meta.AdaBoostM1: Returns the boosted model as Java source code.
toSource(String) - Method in class weka.classifiers.meta.LogitBoost: Returns the boosted model as Java source code.
toSource(String) - Method in class weka.classifiers.rules.OneR: Returns a string that describes the classifier as source.
toSource(String) - Method in class weka.classifiers.rules.ZeroR: Returns a string that describes the classifier as source.
toSource(String) - Method in interface weka.classifiers.Sourcable: Returns a string that describes the classifier as source.
toSource(String) - Method in class weka.classifiers.trees.DecisionStump: Returns the decision tree as Java source code.
toSource(String) - Method in class weka.classifiers.trees.Id3: Returns a string that describes the classifier as source.
toSource(String) - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns source code for the tree as an if-then statement.
toSource(String) - Method in class weka.classifiers.trees.J48: Returns tree as an if-then statement.
toSource(String) - Method in class weka.classifiers.trees.J48graft: Returns tree as an if-then statement.
toSource(String) - Method in class weka.classifiers.trees.REPTree: Returns the tree as if-then statements.
toSource(String) - Method in class weka.core.Capabilities: turns the capabilities object into source code.
toSource(String, int) - Method in class weka.core.Capabilities: turns the capabilities object into source code.
toSource(String, Instances) - Method in class weka.filters.AllFilter: Returns a string that describes the filter as source.
toSource(String, Instances) - Method in interface weka.filters.Sourcable: Returns a string that describes the filter as source.
toSource(String, Instances) - Method in class weka.filters.unsupervised.attribute.Center: Returns a string that describes the filter as source.
toSource(String, Instances) - Method in class weka.filters.unsupervised.attribute.Normalize: Returns a string that describes the filter as source.
toSource(String, Instances) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Returns a string that describes the filter as source.
toSource(String, Instances) - Method in class weka.filters.unsupervised.attribute.Standardize: Returns a string that describes the filter as source.
toString() - Method in class weka.associations.Apriori: Outputs the size of all the generated sets of itemsets and the rules.
toString(Instances) - Method in class weka.associations.AprioriItemSet: Returns the contents of an item set as a string.
toString() - Method in class weka.associations.AssociatorEvaluation: returns the current result
toString() - Method in class weka.associations.FilteredAssociator: Output a representation of this associator
toString() - Method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE
toString() - Method in class weka.associations.FPGrowth.AssociationRule: Get a textual description of this rule.
toString() - Method in class weka.associations.FPGrowth.BinaryItem: A string representation of this item.
toString(boolean) - Method in class weka.associations.FPGrowth.BinaryItem: A string representation of this item.
toString() - Method in class weka.associations.FPGrowth: Output the association rules.
toString() - Method in class weka.associations.GeneralizedSequentialPatterns: Returns a String containing the result information of the algorithm.
toString() - Method in class weka.associations.gsp.Element: Returns a String representation of an Element.
toString() - Method in class weka.associations.gsp.Sequence: Returns a String representation of a Sequence.
toString(Instances) - Method in class weka.associations.ItemSet: Returns the contents of an item set as a string.
toString() - Method in class weka.associations.PredictiveApriori: Outputs the association rules.
toString() - Method in class weka.associations.tertius.AttributeValueLiteral
toString() - Method in class weka.associations.tertius.Body: Gives a String representation of this set of literals as a conjunction.
toString() - Method in class weka.associations.tertius.Head: Gives a String representation of this set of literals as a disjunction.
toString() - Method in class weka.associations.tertius.Literal
toString() - Method in class weka.associations.tertius.LiteralSet: Gives a String representation for this set of literals.
toString() - Method in class weka.associations.tertius.Predicate
toString() - Method in class weka.associations.tertius.Rule: Retrun a String for this rule.
toString() - Method in class weka.associations.tertius.SimpleLinkedList
toString() - Method in class weka.associations.Tertius: Outputs the best rules found with their confirmation value and number of counter-instances.
toString() - Method in class weka.attributeSelection.BestFirst.Link2
toString() - Method in class weka.attributeSelection.BestFirst: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.CfsSubsetEval: returns a string describing CFS
toString() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns a string describing classifierSubsetEval
toString(Instances, int) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Convert a hash entry to a string
toString() - Method in class weka.attributeSelection.ConsistencySubsetEval: returns a description of the evaluator
toString() - Method in class weka.attributeSelection.CostSensitiveASEvaluation: Output a representation of this evaluator
toString() - Method in class weka.attributeSelection.ExhaustiveSearch: prints a description of the search
toString() - Method in class weka.attributeSelection.FilteredAttributeEval: Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.FilteredSubsetEval: Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.GainRatioAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.GeneticSearch: returns a description of the search
toString() - Method in class weka.attributeSelection.GreedyStepwise: returns a description of the search.
toString() - Method in class weka.attributeSelection.InfoGainAttributeEval: Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.LFSMethods.Link2
toString() - Method in class weka.attributeSelection.LinearForwardSelection: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.OneRAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.PrincipalComponents: Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.RaceSearch: Returns a string represenation
toString() - Method in class weka.attributeSelection.RandomSearch: prints a description of the search
toString() - Method in class weka.attributeSelection.Ranker: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.RankSearch: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.ReliefFAttributeEval: Return a description of the ReliefF attribute evaluator.
toString() - Method in class weka.attributeSelection.ScatterSearchV1: returns a description of the search.
toString() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.SVMAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns a string describing the wrapper
toString() - Method in class weka.classifiers.bayes.AODE: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.AODEsr: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.BayesianLogisticRegression: Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.bayes.BayesNet: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Prints out the internal model built by the classifier.
toString() - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary: Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.bayes.DMNBtext: Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.bayes.HNB: returns a string representation of the classifier
toString() - Method in class weka.classifiers.bayes.NaiveBayes: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable: Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.bayes.NaiveBayesSimple: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.net.BayesNetGenerator: Returns either the net (if BIF format) or the generated instances
toString() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Display a representation of this estimator
toString() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeNode
toString() - Method in class weka.classifiers.bayes.net.MarginCalculator
toString() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm: a string representation of the algorithm
toString() - Method in class weka.classifiers.bayes.WAODE: returns a string representation of the classifier
toString() - Method in class weka.classifiers.BVDecompose: Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.BVDecomposeSegCVSub: Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.CostMatrix: Converts a matrix to a string.
toString() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Calls toString() with a default title.
toString(String) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Outputs the performance statistics as a classification confusion matrix.
toString() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.NumericPrediction: Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.TwoClassStats: Returns a string containing the various performance measures for the current object
toString() - Method in class weka.classifiers.functions.GaussianProcesses: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.IsotonicRegression: Returns a description of this classifier as a string
toString() - Method in class weka.classifiers.functions.LeastMedSq: Returns a string representing the best LinearRegression classifier found.
toString() - Method in class weka.classifiers.functions.LibLINEAR: returns a string representation
toString() - Method in class weka.classifiers.functions.LibSVM: returns a string representation
toString() - Method in class weka.classifiers.functions.LinearRegression: Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.functions.Logistic: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.functions.MultilayerPerceptron
toString() - Method in class weka.classifiers.functions.pace.ChisqMixture: Converts to a string
toString() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Converts the discrete function to string.
toString() - Method in class weka.classifiers.functions.pace.MixtureDistribution: Converts to a string
toString() - Method in class weka.classifiers.functions.pace.NormalMixture: Converts to a string
toString() - Method in class weka.classifiers.functions.pace.PaceMatrix: Converts matrix to string
toString(int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Converts matrix to string
toString() - Method in class weka.classifiers.functions.PaceRegression: Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.functions.PLSClassifier: returns a string representation of the classifier
toString() - Method in class weka.classifiers.functions.RBFNetwork: Returns a description of this classifier as a String
toString() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns a description of this classifier as a string
toString() - Method in class weka.classifiers.functions.SimpleLogistic: Returns a description of the logistic model (attributes/coefficients).
toString() - Method in class weka.classifiers.functions.SMO.BinarySMO: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.SMO: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.SMOreg: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.SPegasos: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation: returns the current result
toString() - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel: returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.PolyKernel: returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.PrecomputedKernelMatrixKernel: returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.Puk: returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.RBFKernel: returns a string representation for the Kernel
toString() - Method in class weka.classifiers.functions.supportVector.RegOptimizer: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.VotedPerceptron: Returns textual description of classifier.
toString() - Method in class weka.classifiers.functions.Winnow: Returns textual description of the classifier.
toString() - Method in class weka.classifiers.lazy.IB1: Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.IBk: Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.KStar: Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.LBR: Returns a description of the classifier.
toString() - Method in class weka.classifiers.lazy.LWL: Returns a description of this classifier.
toString() - Method in class weka.classifiers.meta.AdaBoostM1: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.AdditiveRegression: Returns textual description of the classifier.
toString() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Bagging: Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.ClassificationViaClustering: Returns a string representation of the classifier.
toString() - Method in class weka.classifiers.meta.ClassificationViaRegression: Prints the classifiers.
toString() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.CVParameterSelection: Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.meta.Dagging: Returns description of the classifier.
toString() - Method in class weka.classifiers.meta.Decorate: Returns description of the Decorate classifier.
toString() - Method in class weka.classifiers.meta.END: Returns description of the committee.
toString() - Method in class weka.classifiers.meta.FilteredClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Grading: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.GridSearch: returns a string representation of the classifier
toString() - Method in class weka.classifiers.meta.LogitBoost: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.MetaCost: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.MultiBoostAB: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.MultiClassClassifier: Prints the classifiers.
toString() - Method in class weka.classifiers.meta.MultiScheme: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.nestedDichotomies.ClassBalancedND: Outputs the classifier as a string.
toString() - Method in class weka.classifiers.meta.nestedDichotomies.DataNearBalancedND: Outputs the classifier as a string.
toString() - Method in class weka.classifiers.meta.nestedDichotomies.ND: Outputs the classifier as a string.
toString() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Prints the classifiers.
toString() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.RandomCommittee: Returns description of the committee.
toString() - Method in class weka.classifiers.meta.RandomSubSpace: Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns a description of the classifier.
toString() - Method in class weka.classifiers.meta.RotationForest: Returns description of the Rotation Forest classifier.
toString() - Method in class weka.classifiers.meta.Stacking: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.StackingC: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.ThresholdSelector: Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.meta.Vote: Output a representation of this classifier
toString() - Method in class weka.classifiers.mi.CitationKNN: returns a string representation of the classifier
toString() - Method in class weka.classifiers.mi.MDD: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MIBoost: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MIDD: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MIEMDD: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MILR: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.MISMO: Prints out the classifier.
toString() - Method in class weka.classifiers.mi.MIWrapper: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.mi.SimpleMI: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.misc.HyperPipes: Returns a description of this classifier.
toString() - Method in class weka.classifiers.misc.SerializedClassifier: Returns a string representation of the classifier
toString() - Method in class weka.classifiers.misc.VFI: Returns a description of this classifier.
toString() - Method in class weka.classifiers.pmml.consumer.GeneralRegression: Return a textual description of this general regression.
toString() - Method in class weka.classifiers.pmml.consumer.NeuralNetwork
toString() - Method in class weka.classifiers.pmml.consumer.Regression: Return a textual description of this Regression model.
toString(String, String) - Method in class weka.classifiers.rules.ConjunctiveRule: Prints this rule with the specified class label
toString() - Method in class weka.classifiers.rules.ConjunctiveRule: Prints this rule
toString() - Method in class weka.classifiers.rules.DecisionTable: Returns a description of the classifier.
toString(Instances, int) - Method in class weka.classifiers.rules.DecisionTableHashKey: Convert a hash entry to a string
toString() - Method in class weka.classifiers.rules.DTNB
toString() - Method in class weka.classifiers.rules.JRip.Antd
toString() - Method in class weka.classifiers.rules.JRip.NominalAntd: Prints this antecedent
toString() - Method in class weka.classifiers.rules.JRip.NumericAntd: Prints this antecedent
toString(Attribute) - Method in class weka.classifiers.rules.JRip.RipperRule: Prints this rule
toString() - Method in class weka.classifiers.rules.JRip: Prints the all the rules of the rule learner.
toString() - Method in class weka.classifiers.rules.NNge: Returns a description of this classifier.
toString() - Method in class weka.classifiers.rules.OneR: Returns a description of the classifier
toString() - Method in class weka.classifiers.rules.part.ClassifierDecList: Prints rules.
toString() - Method in class weka.classifiers.rules.part.MakeDecList: Outputs the classifier into a string.
toString() - Method in class weka.classifiers.rules.PART: Returns a description of the classifier
toString() - Method in class weka.classifiers.rules.Prism: Prints a description of the classifier.
toString() - Method in class weka.classifiers.rules.Ridor: Prints the all the rules of the rule learner.
toString() - Method in class weka.classifiers.rules.ZeroR: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.ADTree: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.BFTree: Prints the decision tree using the protected toString method from below.
toString() - Method in class weka.classifiers.trees.DecisionStump: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.ft.FTtree: Returns a description of the Functional tree (tree structure and logistic models)
toString() - Method in class weka.classifiers.trees.FT: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.Id3: Prints the decision tree using the private toString method from below.
toString() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTreeG: Prints tree structure.
toString() - Method in class weka.classifiers.trees.j48.ClassifierTree: Prints tree structure.
toString(Instances) - Method in class weka.classifiers.trees.j48.GraftSplit: method for returning information about this GraftSplit
toString() - Method in class weka.classifiers.trees.j48.NBTreeClassifierTree: Prints tree structure.
toString() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Return a textual description of the node
toString() - Method in class weka.classifiers.trees.J48: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.J48graft: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.LADTree: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns a description of the logistic model tree (tree structure and logistic models)
toString() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns a description of the logistic model (i.e., attributes and coefficients).
toString() - Method in class weka.classifiers.trees.LMT: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.m5.Impurity: Converts an Impurity object to a string
toString() - Method in class weka.classifiers.trees.m5.M5Base: Returns a description of the classifier
toString() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Returns a textual description of this linear model
toString() - Method in class weka.classifiers.trees.m5.Rule: Return a description of the m5 tree or rule
toString() - Method in class weka.classifiers.trees.m5.RuleNode: print the linear model at this node
toString() - Method in class weka.classifiers.trees.m5.Values: Converts the stats to a string
toString(Instances) - Method in class weka.classifiers.trees.m5.YongSplitInfo: Converts the spliting information to string
toString() - Method in class weka.classifiers.trees.NBTree: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.RandomForest: Outputs a description of this classifier.
toString() - Method in class weka.classifiers.trees.RandomTree: Outputs the decision tree.
toString() - Method in class weka.classifiers.trees.REPTree: Outputs the decision tree.
toString() - Method in class weka.classifiers.trees.SimpleCart: Prints the decision tree using the protected toString method from below.
toString() - Method in class weka.classifiers.trees.UserClassifier
toString() - Method in class weka.clusterers.CLOPE: return a string describing this clusterer
toString() - Method in class weka.clusterers.Cobweb: Returns a description of the clusterer as a string.
toString() - Method in class weka.clusterers.DBSCAN: Returns a description of the clusterer
toString() - Method in class weka.clusterers.EM: Outputs the generated clusters into a string.
toString() - Method in class weka.clusterers.FarthestFirst: return a string describing this clusterer
toString() - Method in class weka.clusterers.FilteredClusterer: Output a representation of this clusterer.
toString() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject
toString() - Method in class weka.clusterers.forOPTICSAndDBScan.DataObjects.ManhattanDataObject
toString() - Method in class weka.clusterers.HierarchicalClusterer
toString() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns a description of the clusterer.
toString() - Method in class weka.clusterers.OPTICS: Returns a description of the clusterer
toString() - Method in class weka.clusterers.sIB
toString() - Method in class weka.clusterers.SimpleKMeans: return a string describing this clusterer
toString() - Method in class weka.clusterers.XMeans: Return a string describing this clusterer.
toString() - Method in class weka.core.AlgVector: Converts a vector to a string
toString() - Method in class weka.core.Attribute: Returns a description of this attribute in ARFF format.
toString() - Method in class weka.core.AttributeExpression
toString() - Method in class weka.core.AttributeLocator: returns a string representation of this object
toString() - Method in class weka.core.AttributeStats: Returns a human readable representation of this AttributeStats instance.
toString() - Method in class weka.core.BinarySparseInstance: Returns the description of one instance in sparse format.
toString() - Method in enum weka.core.Capabilities.Capability: returns the display string of the capability
toString() - Method in class weka.core.Capabilities: returns a string representation of the capabilities
toString() - Method in class weka.core.Debug.Clock: returns the elapsed time, getStop() - getStart(), as string
toString() - Method in class weka.core.Debug.Log: returns a string representation of the logger
toString() - Method in class weka.core.Debug.Random: returns a string representation of this number generator
toString() - Method in class weka.core.Debug.SimpleLog: returns a string representation of the logger
toString() - Method in class weka.core.Debug.Timestamp: returns the timestamp as string in the specified format
toString() - Method in class weka.core.Instance: Returns the description of one instance.
toString(int) - Method in class weka.core.Instance: Returns the description of one value of the instance as a string.
toString(Attribute) - Method in class weka.core.Instance: Returns the description of one value of the instance as a string.
toString() - Method in class weka.core.Instances: Returns the dataset as a string in ARFF format.
toString() - Method in class weka.core.matrix.DoubleVector: Convert the DoubleVecor to a string
toString(int, boolean) - Method in class weka.core.matrix.DoubleVector: Convert the DoubleVecor to a string
toString() - Method in class weka.core.matrix.IntVector: Converts the IntVecor to a string
toString(int, boolean) - Method in class weka.core.matrix.IntVector: Convert the IntVecor to a string
toString() - Method in class weka.core.matrix.LinearRegression: returns the coefficients in a string representation
toString() - Method in class weka.core.matrix.Matrix: Converts a matrix to a string.
toString() - Method in class weka.core.Matrix: Deprecated.

Converts a matrix to a string
toString() - Method in class weka.core.NormalizableDistance: Returns an empty string.
toString() - Method in class weka.core.pmml.BuiltInArithmetic
toString(String) - Method in class weka.core.pmml.BuiltInArithmetic
toString() - Method in class weka.core.pmml.BuiltInMath
toString() - Method in class weka.core.pmml.BuiltInString
toString(String) - Method in class weka.core.pmml.Constant
toString() - Method in class weka.core.pmml.DefineFunction
toString(String) - Method in class weka.core.pmml.DefineFunction
toString() - Method in class weka.core.pmml.DerivedFieldMetaInfo
toString(String) - Method in class weka.core.pmml.Discretize
toString() - Method in class weka.core.pmml.Expression
toString(String) - Method in class weka.core.pmml.Expression
toString() - Method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure
toString(double, double) - Method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure
toString() - Method in class weka.core.pmml.FieldMetaInfo.Interval
toString() - Method in enum weka.core.pmml.FieldMetaInfo.Optype
toString() - Method in enum weka.core.pmml.FieldMetaInfo.Value.Property
toString() - Method in class weka.core.pmml.FieldMetaInfo.Value
toString(String) - Method in class weka.core.pmml.FieldRef
toString() - Method in class weka.core.pmml.Function
toString(String) - Method in class weka.core.pmml.Function
toString() - Method in class weka.core.pmml.MiningFieldMetaInfo: Return a textual representation of this MiningField.
toString() - Method in class weka.core.pmml.MiningSchema: Get a textual description of the mining schema.
toString(String) - Method in class weka.core.pmml.NormContinuous
toString(String) - Method in class weka.core.pmml.NormDiscrete
toString() - Method in class weka.core.PropertyPath.Path: returns the structure again as a dot-path
toString() - Method in class weka.core.PropertyPath.PathElement: returns the element once again as string
toString() - Method in class weka.core.Queue: Produces textual description of queue.
toString() - Method in class weka.core.Range: Constructs a representation of the current range.
toString() - Method in class weka.core.SelectedTag: returns the selected tag in string representation
toString() - Method in class weka.core.SingleIndex: Constructs a representation of the current range.
toString() - Method in class weka.core.SparseInstance: Returns the description of one instance in sparse format.
toString() - Method in class weka.core.stemmers.LovinsStemmer: returns a string representation of the stemmer
toString() - Method in class weka.core.stemmers.NullStemmer: returns a string representation of the stemmer
toString() - Method in class weka.core.stemmers.SnowballStemmer: returns a string representation of the stemmer.
toString() - Method in class weka.core.Stopwords: returns the current stopwords in a string
toString() - Method in class weka.core.SystemInfo: returns a string representation of all the system properties
toString() - Method in class weka.core.Tag: returns the IDStr
toString() - Method in enum weka.core.TechnicalInformation.Field: returns the display string of the Type
toString() - Method in class weka.core.TechnicalInformation: Returns a plain-text string representing this technical information.
toString() - Method in enum weka.core.TechnicalInformation.Type: returns the display string of the Type
toString() - Method in class weka.core.Tee: returns only the classname and the number of streams.
toString() - Method in class weka.core.TestInstances: returns a string representation of the object
toString() - Method in class weka.core.Trie: returns the trie in string representation
toString() - Method in class weka.core.Trie.TrieNode: returns the node in a string representation
toString() - Method in class weka.core.Version: returns the current version as string
toString() - Method in class weka.core.xml.MethodHandler: returns the internal Hashtable (propety/class - method relationship) in a string representation
toString() - Method in class weka.core.xml.XMLDocument: returns the current DOM document as XML-string.
toString() - Method in class weka.core.xml.XMLOptions: returns the object in a string representation (as indented XML output).
toString() - Method in class weka.core.xml.XMLSerializationMethodHandler: returns the read and write method handlers as string
toString() - Method in class weka.datagenerators.ClusterDefinition: returns a string representation of the cluster
toString() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Make a string from the cluster features.
toString() - Method in class weka.datagenerators.Test: Returns the test represented by a string.
toString() - Method in class weka.estimators.DDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DiscreteEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DKConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DNConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KernelEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KKConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.MahalanobisEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.NDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.NNConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.NormalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.PoissonEstimator: Display a representation of this estimator
toString() - Method in class weka.experiment.AveragingResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CrossValidationResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DatabaseResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DensityBasedClustererSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.Experiment: Gets a string representation of the experiment configuration.
toString() - Method in class weka.experiment.LearningRateResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.PairedStats: Returns statistics on the paired comparison.
toString() - Method in class weka.experiment.PropertyNode: Returns a string description of this property.
toString() - Method in class weka.experiment.RandomSplitResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.RegressionSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.RemoteExperiment: Overides toString in Experiment
toString() - Method in class weka.experiment.ResultMatrix: returns the matrix as a string
toString() - Method in class weka.experiment.Stats: Returns a string summarising the stats so far.
toString() - Method in class weka.filters.Filter: Returns a description of the filter, by default only the classname.
toString() - Method in class weka.gui.arffviewer.ArffViewer: returns only the classname
toString() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns only the classname
toString() - Method in class weka.gui.GenericObjectEditor.GOETreeNode: returns a string representation of this treenode.
toString() - Method in class weka.gui.graphvisualizer.GraphEdge
toString() - Method in class weka.gui.SortedTableModel.SortContainer: Returns a string representation of the sort container.
toString() - Method in class weka.gui.sql.event.ConnectionEvent: returns the event in a string representation
toString() - Method in class weka.gui.sql.event.HistoryChangedEvent: returns the event in a string representation
toString() - Method in class weka.gui.sql.event.QueryExecuteEvent: returns the event in a string representation
toString() - Method in class weka.gui.sql.event.ResultChangedEvent: returns the event in a string representation
toStringHeader() - Method in class weka.experiment.ResultMatrix: returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixCSV: returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixGnuPlot: returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixHTML: returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixLatex: returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixPlainText: returns the header of the matrix as a string
toStringHeader() - Method in class weka.experiment.ResultMatrixSignificance: returns the header of the matrix as a string
toStringKey() - Method in class weka.experiment.ResultMatrix: returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixCSV: returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixGnuPlot: returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixHTML: returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixLatex: returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixPlainText: returns returns a key for all the col names, for better readability if the names got cut off
toStringKey() - Method in class weka.experiment.ResultMatrixSignificance: returns returns a key for all the col names, for better readability if the names got cut off
toStringMatrix() - Method in class weka.experiment.ResultMatrix: returns the matrix as a string
toStringMatrix() - Method in class weka.experiment.ResultMatrixCSV: returns the matrix in CSV format
toStringMatrix() - Method in class weka.experiment.ResultMatrixGnuPlot: returns the matrix in CSV format
toStringMatrix() - Method in class weka.experiment.ResultMatrixHTML: returns the matrix in an HTML table
toStringMatrix() - Method in class weka.experiment.ResultMatrixLatex: returns the matrix as latex table
toStringMatrix() - Method in class weka.experiment.ResultMatrixPlainText: returns the matrix as plain text
toStringMatrix() - Method in class weka.experiment.ResultMatrixSignificance: returns the matrix as plain text
toStringMetric(int, int, int, int) - Method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE
toStringRanking() - Method in class weka.experiment.ResultMatrix: returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixCSV: returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixGnuPlot: returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixHTML: returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixLatex: returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixPlainText: returns the ranking in a string representation
toStringRanking() - Method in class weka.experiment.ResultMatrixSignificance: returns the ranking in a string representation
toStringSummary() - Method in class weka.experiment.ResultMatrix: returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixCSV: returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixGnuPlot: returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixHTML: returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixLatex: returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixPlainText: returns the summary as string
toStringSummary() - Method in class weka.experiment.ResultMatrixSignificance: returns the summary as string
toSummaryString() - Method in class weka.associations.AssociatorEvaluation: returns a summary string of the evaluation with a no title
toSummaryString(String) - Method in class weka.associations.AssociatorEvaluation: returns a summary string of the evaluation with a default title
toSummaryString() - Method in class weka.classifiers.Evaluation: Calls toSummaryString() with no title and no complexity stats
toSummaryString(boolean) - Method in class weka.classifiers.Evaluation: Calls toSummaryString() with a default title.
toSummaryString(String, boolean) - Method in class weka.classifiers.Evaluation: Outputs the performance statistics in summary form.
toSummaryString() - Method in class weka.classifiers.functions.supportVector.KernelEvaluation: returns a summary string of the evaluation with a no title
toSummaryString(String) - Method in class weka.classifiers.functions.supportVector.KernelEvaluation: returns a summary string of the evaluation with a default title
toSummaryString() - Method in class weka.classifiers.meta.CVParameterSelection: A concise description of the model.
toSummaryString() - Method in class weka.classifiers.meta.GridSearch: Returns a string that summarizes the object.
toSummaryString() - Method in class weka.classifiers.rules.PART: Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.trees.J48: Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.trees.J48graft: Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.trees.NBTree: Returns a superconcise version of the model
toSummaryString() - Method in class weka.core.Instances: Generates a string summarizing the set of instances.
toSummaryString() - Method in interface weka.core.Summarizable: Returns a string that summarizes the object.
total() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of predictions that were made (actually the sum of the weights of predictions where the class value was known).
total() - Method in class weka.classifiers.trees.j48.Distribution: Returns total number of (possibly fractional) instances.
TOTAL_UNIFORM - Static variable in class weka.datagenerators.clusterers.SubspaceCluster: cluster type: total uniform
totalCost() - Method in class weka.classifiers.Evaluation: Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
totalCount - Variable in class weka.core.AttributeStats: The total number of values (i.e.
totalForSubset(int) - Method in class weka.classifiers.trees.j48.GraftSplit
totalForSubsetOfInterest() - Method in class weka.classifiers.trees.j48.GraftSplit
toXML(int, int, int, int) - Method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE
toXML() - Method in class weka.associations.FPGrowth.AssociationRule
toXML() - Method in class weka.associations.FPGrowth.BinaryItem
toXML(Object) - Method in class weka.core.xml.XMLSerialization: extracts all accesible properties from the given object
toXMLBIF03() - Method in class weka.classifiers.bayes.BayesNet: Returns a description of the classifier in XML BIF 0.3 format.
toXMLBIF03() - Method in class weka.classifiers.bayes.net.EditableBayesNet: returns network in XMLBIF format
toXMLBIF03(FastVector) - Method in class weka.classifiers.bayes.net.EditableBayesNet: return fragment of network in XMLBIF format
toXMLBIF03() - Method in class weka.classifiers.bayes.net.MarginCalculator
TP_RATE - Static variable in class weka.classifiers.meta.ThresholdSelector: true-positive rate
TP_RATE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: True Positive Rate
trace() - Method in class weka.core.matrix.Matrix: Matrix trace.
trainCV(int, int) - Method in class weka.core.Instances: Creates the training set for one fold of a cross-validation on the dataset.
trainCV(int, int, Random) - Method in class weka.core.Instances: Creates the training set for one fold of a cross-validation on the dataset.
TRAINING - Static variable in class weka.gui.beans.BatchClustererEvent
TrainingSetEvent - Class in weka.gui.beans: Event encapsulating a training set
TrainingSetEvent(Object, Instances) - Constructor for class weka.gui.beans.TrainingSetEvent: Creates a new TrainingSetEvent
TrainingSetEvent(Object, Instances, int, int) - Constructor for class weka.gui.beans.TrainingSetEvent: Creates a new TrainingSetEvent
TrainingSetEvent(Object, Instances, int, int, int, int) - Constructor for class weka.gui.beans.TrainingSetEvent: Creates a new TrainingSetEvent
TrainingSetListener - Interface in weka.gui.beans: Interface to something that can accept and process training set events
TrainingSetMaker - Class in weka.gui.beans: Bean that accepts a data sets and produces a training set
TrainingSetMaker() - Constructor for class weka.gui.beans.TrainingSetMaker
TrainingSetMakerBeanInfo - Class in weka.gui.beans: Bean info class for the training set maker bean
TrainingSetMakerBeanInfo() - Constructor for class weka.gui.beans.TrainingSetMakerBeanInfo
TrainingSetProducer - Interface in weka.gui.beans: Interface to something that can produce a training set
trainingTimeTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
trainPercentTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
trainPercentTipText() - Method in class weka.gui.beans.TrainTestSplitMaker: Tip text info for this property
TrainTestSplitMaker - Class in weka.gui.beans: Bean that accepts data sets, training sets, test sets and produces both a training and test set by randomly spliting the data
TrainTestSplitMaker() - Constructor for class weka.gui.beans.TrainTestSplitMaker
TrainTestSplitMakerBeanInfo - Class in weka.gui.beans: Bean info class for the train test split maker bean
TrainTestSplitMakerBeanInfo() - Constructor for class weka.gui.beans.TrainTestSplitMakerBeanInfo
TrainTestSplitMakerCustomizer - Class in weka.gui.beans: GUI customizer for the train test split maker bean
TrainTestSplitMakerCustomizer() - Constructor for class weka.gui.beans.TrainTestSplitMakerCustomizer
transactionsMustContainTipText() - Method in class weka.associations.FPGrowth: Returns the tip text for this property
transform(Instances) - Method in class weka.classifiers.mi.SimpleMI: Implements MITransform (3 type of transformation) 1.arithmatic average; 2.geometric centor; 3.merge minima and maxima attribute value together
transform(AffineTransform) - Method in class weka.gui.visualize.PostscriptGraphics
transformAllValuesTipText() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns the tip text for this property
transformAllValuesTipText() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns the tip text for this property
transformBackToOriginalTipText() - Method in class weka.attributeSelection.PrincipalComponents: Returns the tip text for this property
transformedData(Instances) - Method in interface weka.attributeSelection.AttributeTransformer: Transform the supplied data set (assumed to be the same format as the training data)
transformedData(Instances) - Method in class weka.attributeSelection.LatentSemanticAnalysis: Transform the supplied data set (assumed to be the same format as the training data)
transformedData(Instances) - Method in class weka.attributeSelection.PrincipalComponents: Gets the transformed training data.
transformedHeader() - Method in interface weka.attributeSelection.AttributeTransformer: Returns just the header for the transformed data (ie.
transformedHeader() - Method in class weka.attributeSelection.LatentSemanticAnalysis: Returns just the header for the transformed data (ie.
transformedHeader() - Method in class weka.attributeSelection.PrincipalComponents: Returns just the header for the transformed data (ie.
TRANSFORMMETHOD_ARITHMETIC - Static variable in class weka.classifiers.mi.SimpleMI: arithmetic average
TRANSFORMMETHOD_GEOMETRIC - Static variable in class weka.classifiers.mi.SimpleMI: geometric average
TRANSFORMMETHOD_MINIMAX - Static variable in class weka.classifiers.mi.SimpleMI: using minimax combined features of a bag
transformMethodTipText() - Method in class weka.classifiers.mi.SimpleMI: Returns the tip text for this property
translate(int, int) - Method in class weka.gui.visualize.PostscriptGraphics: Translates the origin of the graphics context to the point (x, y) in the current coordinate system.
translate(double, double) - Method in class weka.gui.visualize.PostscriptGraphics
translateDBColumnType(String) - Method in class weka.experiment.DatabaseUtils: translates the column data type string to an integer value that indicates which data type / get()-Method to use in order to retrieve values from the database (see DatabaseUtils.Properties, InstanceQuery()).
translationTipText() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns the tip text for this property.
transpose() - Method in class weka.core.matrix.Matrix: Matrix transpose.
transpose() - Method in class weka.core.Matrix: Deprecated.

Returns the transpose of a matrix.
transProb() - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute: Calculates the probability of the indexed nominal attribute of the test instance transforming into the indexed nominal attribute of the training instance.
transProb() - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Calculates the transformation probability of the attribute indexed "m_AttrIndex" in test instance "m_Test" to the same attribute in the train instance "m_Train".
TRAVERSAL_BY_COLUMN - Static variable in class weka.classifiers.meta.GridSearch: column-wise grid traversal
TRAVERSAL_BY_ROW - Static variable in class weka.classifiers.meta.GridSearch: row-wise grid traversal
traversalTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
TREE - Static variable in interface weka.core.Drawable
TreeBuild - Class in weka.gui.treevisualizer: This class will parse a dotty file and construct a tree structure from it with Edge's and Node's
TreeBuild() - Constructor for class weka.gui.treevisualizer.TreeBuild: Upon construction this will only setup the color table for quick reference of a color.
TreeDisplayEvent - Class in weka.gui.treevisualizer: An event containing the user selection from the tree display
TreeDisplayEvent(int, String) - Constructor for class weka.gui.treevisualizer.TreeDisplayEvent: Constructs an event with the specified command and what the command is applied to.
TreeDisplayListener - Interface in weka.gui.treevisualizer: Interface implemented by classes that wish to recieve user selection events from a tree displayer.
treeErrors() - Method in class weka.classifiers.trees.lmt.LMTNode: Updates the numIncorrectTree field for all nodes.
treeErrors() - Method in class weka.classifiers.trees.SimpleCart: Updates the numIncorrectTree field for all nodes.
TreePerformanceStats - Class in weka.core.neighboursearch: The class that measures the performance of a tree based nearest neighbour search algorithm.
TreePerformanceStats() - Constructor for class weka.core.neighboursearch.TreePerformanceStats: Default constructor.
treeToString(int) - Method in class weka.classifiers.trees.m5.RuleNode: Recursively builds a textual description of the tree
TreeVisualizePlugin - Interface in weka.gui.visualize.plugins: Interface implemented by classes loaded dynamically to visualize classifier results in the explorer.
TreeVisualizer - Class in weka.gui.treevisualizer: Class for displaying a Node structure in Swing.
TreeVisualizer(TreeDisplayListener, String, NodePlace) - Constructor for class weka.gui.treevisualizer.TreeVisualizer: Constructs Displayer to display a tree provided in a dot format.
TreeVisualizer(TreeDisplayListener, Node, NodePlace) - Constructor for class weka.gui.treevisualizer.TreeVisualizer: Constructs Displayer with the specified Node as the top of the tree, and uses the NodePlacer to place the Nodes.
TRIANGLEDOWN_SHAPE - Static variable in class weka.gui.visualize.Plot2D
TRIANGLEUP_SHAPE - Static variable in class weka.gui.visualize.Plot2D
Trie - Class in weka.core: A class representing a Trie data structure for strings.
Trie() - Constructor for class weka.core.Trie: initializes the data structure
Trie.TrieIterator - Class in weka.core: Represents an iterator over a trie
Trie.TrieNode - Class in weka.core: Represents a node in the trie.
TrieIterator(Trie.TrieNode) - Constructor for class weka.core.Trie.TrieIterator: initializes the iterator
TrieNode(char) - Constructor for class weka.core.Trie.TrieNode: initializes the node
TrieNode(Character) - Constructor for class weka.core.Trie.TrieNode: initializes the node
trim(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Trims the small values of the estaimte
trim(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Trims the small values of the estaimte
trim() - Method in class weka.gui.LogWindow: trims the JTextPane, if too big
trimToSize() - Method in class weka.core.FastVector: Sets the vector's capacity to its size.
TRUE - Static variable in interface weka.core.mathematicalexpression.sym
TRUE - Static variable in interface weka.filters.unsupervised.instance.subsetbyexpression.sym
TRUE_NEG - Static variable in class weka.classifiers.meta.ThresholdSelector: true-negative
TRUE_NEG_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: True Negatives
TRUE_POS - Static variable in class weka.classifiers.meta.ThresholdSelector: true-positive
TRUE_POS_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: attribute name: True Positives
trueNegativeRate(int) - Method in class weka.classifiers.Evaluation: Calculate the true negative rate with respect to a particular class.
truePositiveRate(int) - Method in class weka.classifiers.Evaluation: Calculate the true positive rate with respect to a particular class.
TStartTipText() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
TStartTipText() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
turnChecksOff() - Method in class weka.classifiers.functions.LinearRegression: Turns off checks for missing values, etc.
turnChecksOff() - Method in class weka.classifiers.functions.SMO: Turns off checks for missing values, etc.
turnChecksOff() - Method in class weka.classifiers.mi.MISMO: Turns off checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.functions.LinearRegression: Turns on checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.functions.SMO: Turns on checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.mi.MISMO: Turns on checks for missing values, etc.
TwoClassStats - Class in weka.classifiers.evaluation: Encapsulates performance functions for two-class problems.
TwoClassStats(double, double, double, double) - Constructor for class weka.classifiers.evaluation.TwoClassStats: Creates the TwoClassStats with the given initial performance values.
TwoWayNominalSplit - Class in weka.classifiers.trees.adtree: Class representing a two-way split on a nominal attribute, of the form: either 'is some_value' or 'is not some_value'.
TwoWayNominalSplit(int, int) - Constructor for class weka.classifiers.trees.adtree.TwoWayNominalSplit: Creates a new two-way nominal splitter.
TwoWayNumericSplit - Class in weka.classifiers.trees.adtree: Class representing a two-way split on a numeric attribute, of the form: either 'is < some_value' or 'is >= some_value'.
TwoWayNumericSplit(int, double) - Constructor for class weka.classifiers.trees.adtree.TwoWayNumericSplit: Creates a new two-way numeric splitter.
type() - Method in class weka.core.Attribute: Returns the attribute's type as an integer.
typeIsNumeric(int) - Static method in class weka.gui.sql.ResultSetHelper: returns whether the SQL type is numeric (and therefore the justification should be right).
typeName(int) - Static method in class weka.experiment.DatabaseUtils: Returns the name associated with a SQL type.
typeTipText() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the tip text for this property
typeTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the tip text for this property
typeToClass(int) - Static method in class weka.gui.sql.ResultSetHelper: Returns the class associated with a SQL type.

U

uminus() - Method in class weka.core.matrix.Matrix: Unary minus
UnassignedClassException - Exception in weka.core: Exception that is raised when trying to use some data that has no class assigned to it, but a class is needed to perform the operation.
UnassignedClassException() - Constructor for exception weka.core.UnassignedClassException: Creates a new UnassignedClassException with no message.
UnassignedClassException(String) - Constructor for exception weka.core.UnassignedClassException: Creates a new UnassignedClassException.
UnassignedDatasetException - Exception in weka.core: Exception that is raised when trying to use something that has no reference to a dataset, when one is required.
UnassignedDatasetException() - Constructor for exception weka.core.UnassignedDatasetException: Creates a new UnassignedDatasetException with no message.
UnassignedDatasetException(String) - Constructor for exception weka.core.UnassignedDatasetException: Creates a new UnassignedDatasetException.
unbackQuoteChars(String) - Static method in class weka.core.Utils: The inverse operation of backQuoteChars().
unclassified() - Method in class weka.classifiers.Evaluation: Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
UNCLASSIFIED - Static variable in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
UNCONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is not connected to any others.
UNDEFINED - Static variable in interface weka.clusterers.forOPTICSAndDBScan.DataObjects.DataObject
undefinedDistribution - Static variable in class weka.core.matrix.Maths: Distribution type: undefined
undo() - Method in class weka.classifiers.bayes.net.EditableBayesNet: undo the last edit action performed on the network.
undo() - Method in interface weka.core.Undoable: undoes the last action
undo() - Method in class weka.gui.arffviewer.ArffPanel: performs an undo action
undo() - Method in class weka.gui.arffviewer.ArffSortedTableModel: undoes the last action
undo() - Method in class weka.gui.arffviewer.ArffTableModel: undoes the last action
undo() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: undoes the last action
undo() - Method in class weka.gui.explorer.PreprocessPanel: Reverts to the last backed up version of the dataset.
Undoable - Interface in weka.core: Interface implemented by classes that support undo.
UNHANDLED_DIALOG - Static variable in class weka.gui.ConverterFileChooser: unhandled type of dialog
UNIFORM_RANDOM - Static variable in class weka.datagenerators.clusterers.SubspaceCluster: cluster type: uniform/random
unique() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Makes each individual point value unique
uniqueCount - Variable in class weka.core.AttributeStats: The number of values that only appear once
UNKNOWN_NOMINAL_VALUE - Static variable in class weka.core.pmml.MappingInfo: Index for incoming nominal values that are not defined in the mining schema.
unnormalizedKernel(char[], char[]) - Method in class weka.classifiers.functions.supportVector.StringKernel: evaluates the unnormalized kernel between s and t.
unpivoting(IntVector, int) - Method in class weka.core.matrix.DoubleVector: Returns a vector from the pivoting indices.
unprunedTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
unprunedTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
unprunedTipText() - Method in class weka.classifiers.trees.J48graft: Returns the tip text for this property
unprunedTipText() - Method in class weka.classifiers.trees.m5.M5Base: Returns the tip text for this property
unquote(String) - Static method in class weka.core.Utils: unquotes are previously quoted string (but only if necessary), i.e., it removes the single quotes around it.
UNSET - Static variable in class weka.filters.unsupervised.attribute.ClassAssigner: unset the class attribute.
unsorted() - Method in class weka.core.matrix.DoubleVector: Returns true if vector not sorted
UnsupervisedAttributeEvaluator - Class in weka.attributeSelection: Abstract unsupervised attribute evaluator.
UnsupervisedAttributeEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedAttributeEvaluator
UnsupervisedFilter - Interface in weka.filters: Interface for filters that do not need a class attribute.
UnsupervisedSubsetEvaluator - Class in weka.attributeSelection: Abstract unsupervised attribute subset evaluator.
UnsupervisedSubsetEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedSubsetEvaluator
UnsupportedAttributeTypeException - Exception in weka.core: Exception that is raised by an object that is unable to process some of the attribute types it has been passed.
UnsupportedAttributeTypeException() - Constructor for exception weka.core.UnsupportedAttributeTypeException: Creates a new UnsupportedAttributeTypeException with no message.
UnsupportedAttributeTypeException(String) - Constructor for exception weka.core.UnsupportedAttributeTypeException: Creates a new UnsupportedAttributeTypeException.
UnsupportedClassTypeException - Exception in weka.core: Exception that is raised by an object that is unable to process the class type of the data it has been passed.
UnsupportedClassTypeException() - Constructor for exception weka.core.UnsupportedClassTypeException: Creates a new UnsupportedClassTypeException with no message.
UnsupportedClassTypeException(String) - Constructor for exception weka.core.UnsupportedClassTypeException: Creates a new UnsupportedClassTypeException.
upDate(Instances) - Method in class weka.associations.tertius.LiteralSet: Update the number of counter-instances of this set in the dataset.
upDate(Instances) - Method in class weka.associations.tertius.Rule: Update the number of counter-instances of this rule in the dataset.
update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.GaussianPriorImpl: Update function specific to Laplace Prior.
update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.LaplacePriorImpl: Update function specific to Laplace Prior.
update(int, Instances, double, double, double[], double) - Method in class weka.classifiers.bayes.blr.Prior: Interface for the update functions for different types of priors.
update(MarginCalculator.JunctionTreeNode) - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator: marginalize junciontTreeNode node over all nodes outside the separator set
update(Instance) - Method in interface weka.core.DistanceFunction: Update the distance function (if necessary) for the newly added instance.
update(double) - Method in class weka.core.matrix.FlexibleDecimalFormat
update(Instance) - Method in class weka.core.neighboursearch.BallTree: Adds one instance to the BallTree.
update(Instance) - Method in class weka.core.neighboursearch.CoverTree: Adds an instance to the cover tree.
update(Instance) - Method in class weka.core.neighboursearch.KDTree: Adds one instance to the KDTree.
update(Instance) - Method in class weka.core.neighboursearch.LinearNNSearch: Updates the LinearNNSearch to cater for the new added instance.
update(Instance) - Method in class weka.core.neighboursearch.NearestNeighbourSearch: Updates the NearNeighbourSearch algorithm for the new added instance.
update(Instance) - Method in class weka.core.NormalizableDistance: Update the distance function (if necessary) for the newly added instance.
update(String) - Method in class weka.experiment.DatabaseUtils: Executes a SQL DDL query or an INSERT, DELETE or UPDATE.
update(Graphics) - Method in class weka.gui.SplashWindow: Updates the display area of the window.
UpdateableClassifier - Interface in weka.classifiers: Interface to incremental classification models that can learn using one instance at a time.
UpdateableClusterer - Interface in weka.clusterers: Interface to incremental cluster models that can learn using one instance at a time.
updateChildPropertySheet() - Method in class weka.gui.GenericObjectEditor.GOEPanel: Updates the child property sheet, and creates if needed.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.AODE: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.AODEsr: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.BayesNet: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.DMNBtext.DNBBinary
updateClassifier(Instance) - Method in class weka.classifiers.bayes.DMNBtext: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.NaiveBayes: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.NaiveBayesMultinomialUpdateable: Updates the classifier with the given instance.
updateClassifier(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Updates the classifier with the given instance.
updateClassifier(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Updates the classifier with the given instance.
updateClassifier(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Updates the classifier with the given instance.
updateClassifier(BayesNet, Instance) - Method in class weka.classifiers.bayes.net.estimate.SimpleEstimator: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.functions.SPegasos: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.functions.Winnow: Updates the classifier with a new learning example
updateClassifier(Instance) - Method in class weka.classifiers.lazy.IB1: Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.lazy.IBk: Adds the supplied instance to the training set.
updateClassifier(Instance) - Method in class weka.classifiers.lazy.KStar: Adds the supplied instance to the training set
updateClassifier(Instance) - Method in class weka.classifiers.lazy.LWL: Adds the supplied instance to the training set.
updateClassifier(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.misc.HyperPipes: Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.rules.NNge: Updates the classifier using the given instance.
updateClassifier(Instance) - Method in interface weka.classifiers.UpdateableClassifier: Updates a classifier using the given instance.
updateClusterer(Instance) - Method in class weka.clusterers.Cobweb: Adds an instance to the clusterer.
updateClusterer(Instance) - Method in interface weka.clusterers.UpdateableClusterer: Adds an instance to the clusterer.
upDateCounter(Instance) - Method in class weka.associations.ItemSet: Updates counter of item set with respect to given transaction.
upDateCounter(Instance, Instance) - Method in class weka.associations.LabeledItemSet: Updates counter of item set with respect to given transaction.
upDateCounters(FastVector, Instances) - Static method in class weka.associations.ItemSet: Updates counters for a set of item sets and a set of instances.
upDateCounters(FastVector, Instances, Instances) - Static method in class weka.associations.LabeledItemSet: Updates counter of a specific item set
updateCounters(ItemSet) - Method in class weka.associations.PriorEstimation: updates the support count of an item set
updateFinished() - Method in class weka.clusterers.Cobweb: Singals the end of the updating.
updateFinished() - Method in interface weka.clusterers.UpdateableClusterer: Signals the end of the updating.
updateFrameTitle() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: sets the title of the parent frame, if one was provided
updateFromChild() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator: marginalize junciontTreeNode node over all nodes outside the separator set of the child clique
updateFromParent() - Method in class weka.classifiers.bayes.net.MarginCalculator.JunctionTreeSeparator: marginalize junciontTreeNode node over all nodes outside the separator set of the parent clique
updateJavadoc() - Method in class weka.core.Javadoc: generates the Javadoc and returns it applied to the source file if one was provided, otherwise an empty string.
updateNormalization(Instance) - Method in class weka.classifiers.mi.CitationKNN: Updates the normalization of each attribute.
updatePointCount(int) - Method in class weka.core.neighboursearch.PerformanceStats: adds the given number to the point count.
updatePriors(Instance) - Method in class weka.classifiers.Evaluation: Updates the class prior probabilities (when incrementally training)
UpdateQueue - Class in weka.clusterers.forOPTICSAndDBScan.Utils: UpdateQueue.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 27, 2004
Time: 5:36:35 PM
$ Revision 1.4 $
UpdateQueue() - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueue: Creates a new PriorityQueue (backed on a binary heap) with the ability to efficiently update the priority of the stored objects in the heap.
UpdateQueueElement - Class in weka.clusterers.forOPTICSAndDBScan.Utils: UpdateQueueElement.java
Authors: Rainer Holzmann, Zhanna Melnikova-Albrecht, Matthias Schubert
Date: Aug 31, 2004
Time: 6:43:18 PM
$ Revision 1.4 $
UpdateQueueElement(double, Object, String) - Constructor for class weka.clusterers.forOPTICSAndDBScan.Utils.UpdateQueueElement
updateRanges(Instance, int, double[][]) - Method in class weka.core.NormalizableDistance: Updates the minimum and maximum and width values for all the attributes based on a new instance.
updateRanges(Instance, double[][]) - Method in class weka.core.NormalizableDistance: Updates the ranges given a new instance.
updateRanges(Instance) - Method in class weka.core.NormalizableDistance: Update the ranges if a new instance comes.
updateRangesFirst(Instance, int, double[][]) - Method in class weka.core.NormalizableDistance: Used to initialize the ranges.
UpdateReferenceSet(int, int) - Method in class weka.attributeSelection.ScatterSearchV1: Update the ReferenceSet putting the new obtained Solutions there
updateResult(String) - Method in class weka.gui.ResultHistoryPanel: Tells any component currently displaying the named result that the contents of the result text in the StringBuffer have been updated.
updateSupportCount(FastVector, FastVector) - Static method in class weka.associations.gsp.Sequence: Updates the support count of a set of Sequence candidates according to a given set of data sequences.
updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.LinearUnit: This function will calculate what the change in weights should be and also update them.
updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to update the weight values at this unit.
updateWeights(NeuralNode, double, double) - Method in interface weka.classifiers.functions.neural.NeuralMethod: This function will calculate what the change in weights should be and also update them.
updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralNode: Call this function to update the weight values at this unit.
updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.SigmoidUnit: This function will calculate what the change in weights should be and also update them.
upperBoundMinSupportTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
upperBoundMinSupportTipText() - Method in class weka.associations.FPGrowth: Returns the tip text for this property
upperNumericBoundIsOpen() - Method in class weka.core.Attribute: Returns whether the upper numeric bound of the attribute is open.
upperSizeTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
URLSourcedLoader - Interface in weka.core.converters: Interface to a loader that can load from a http url
urlTipText() - Method in class weka.core.converters.DatabaseLoader: the tip text for this property
urlTipText() - Method in class weka.core.converters.DatabaseSaver: Returns the tip text for this property.
USE_DYNAMIC - Static variable in class weka.gui.GenericPropertiesCreator: name of property whether to use the dynamic approach or the old GenericObjectEditor.props file
useADTreeTipText() - Method in class weka.classifiers.bayes.BayesNet
useAICTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
useAICTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
useAICTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
useArcReversalTipText() - Method in class weka.classifiers.bayes.net.search.global.HillClimber
useArcReversalTipText() - Method in class weka.classifiers.bayes.net.search.local.HillClimber
useBetterEncodingTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
useCrossOverTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
useCrossOverTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
useCrossValidationTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSink: Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSource: Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.AbstractEvaluator: Use the default images for an evaluator
useDefaultVisual() - Method in class weka.gui.beans.AbstractTestSetProducer: Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer: Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.Associator: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.AttributeSummarizer: Use the default appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.ClassAssigner
useDefaultVisual() - Method in class weka.gui.beans.Classifier: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.ClassValuePicker
useDefaultVisual() - Method in class weka.gui.beans.Clusterer: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.CostBenefitAnalysis
useDefaultVisual() - Method in class weka.gui.beans.DataVisualizer: Use the default appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.Filter: Use the default visual appearance
useDefaultVisual() - Method in class weka.gui.beans.GraphViewer: Use the default visual appearance
useDefaultVisual() - Method in class weka.gui.beans.InstanceStreamToBatchMaker: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.MetaBean: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.ModelPerformanceChart: Use the default appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.PredictionAppender: Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.SerializedModelSaver: Use the default images for this bean.
useDefaultVisual() - Method in class weka.gui.beans.StripChart: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.TextViewer: Use the default visual appearance for this bean
useDefaultVisual() - Method in interface weka.gui.beans.Visible: Use the default visual representation
useDynamic() - Method in class weka.gui.GenericPropertiesCreator: gets whether the dynamic approach should be used or not
useEqualFrequencyTipText() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns the tip text for this property
useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns the tip text for this property
useErrorRateTipText() - Method in class weka.classifiers.trees.BFTree: Returns the tip text for this property
useFilter(Instances, Filter) - Static method in class weka.filters.Filter: Filters an entire set of instances through a filter and returns the new set.
useGiniTipText() - Method in class weka.classifiers.trees.BFTree: Returns the tip text for this property
useIBkTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
useKDTreeTipText() - Method in class weka.clusterers.XMeans: Returns the tip text for this property.
useKernelEstimatorTipText() - Method in class weka.classifiers.bayes.NaiveBayes: Returns the tip text for this property
useKononenkoTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
useLaplaceTipText() - Method in class weka.classifiers.bayes.AODEsr: Returns the tip text for this property
useLaplaceTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
useLaplaceTipText() - Method in class weka.classifiers.trees.J48graft: Returns the tip text for this property
useLeastValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveFrequentValues: Returns the tip text for this property
useLowerOrderTipText() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Returns the tip text for this property
useMEstimatesTipText() - Method in class weka.classifiers.bayes.AODE: Returns the tip text for this property
useMissingTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the tip text for this property
useMutationTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
useMutationTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
useNoPriors() - Method in class weka.classifiers.Evaluation: disables the use of priors, e.g., in case of de-serialized schemes that have no access to the original training set, but are evaluated on a set set.
useNormalizationTipText() - Method in class weka.classifiers.functions.supportVector.StringKernel: Returns the tip text for this property
useOneSETipText() - Method in class weka.classifiers.trees.BFTree: Returns the tip text for this property
useOneSETipText() - Method in class weka.classifiers.trees.SimpleCart: Returns the tip text for this property
useORForMustContainListTipText() - Method in class weka.associations.FPGrowth: Returns the tip text for this property
usePairwiseCouplingTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
useProbTipText() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
usePruneTipText() - Method in class weka.classifiers.trees.SimpleCart: Return the tip text for this property
usePruningTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
UserClassifier - Class in weka.classifiers.trees: Interactively classify through visual means.
UserClassifier() - Constructor for class weka.classifiers.trees.UserClassifier: Constructor
userCommand(TreeDisplayEvent) - Method in class weka.classifiers.trees.UserClassifier: Receives user choices from the tree view, and then deals with these choices.
userCommand(TreeDisplayEvent) - Method in interface weka.gui.treevisualizer.TreeDisplayListener: Gets called when the user selects something, in the tree display.
userDataEvent(VisualizePanelEvent) - Method in class weka.classifiers.trees.UserClassifier: This receives shapes from the data view.
userDataEvent(VisualizePanelEvent) - Method in interface weka.gui.visualize.VisualizePanelListener: This method receives an object containing the shapes, instances inside and outside these shapes and the attributes these shapes were created in.
useRelativePathTipText() - Method in class weka.core.converters.AbstractFileLoader: Tip text suitable for displaying int the GUI
useRelativePathTipText() - Method in class weka.core.converters.AbstractFileSaver: Tip text suitable for displaying int the GUI
useResamplingTipText() - Method in class weka.classifiers.meta.AdaBoostM1: Returns the tip text for this property
useResamplingTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
useResamplingTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
usernameTipText() - Method in class weka.experiment.DatabaseUtils: Returns the tip text for this property.
UserRequestAcceptor - Interface in weka.gui.beans: Interface to something that can accept requests from a user to perform some action
userTipText() - Method in class weka.core.converters.DatabaseLoader: the tip text for this property
userTipText() - Method in class weka.core.converters.DatabaseSaver: Returns the tip text for this property.
useStemmer(Stemmer, String[]) - Static method in class weka.core.stemmers.Stemming: Applies the given stemmer according to the given options.
useStoplistTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
useSupervisedDiscretizationTipText() - Method in class weka.classifiers.bayes.NaiveBayes: Returns the tip text for this property
useTournamentSelectionTipText() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
useTournamentSelectionTipText() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
useTrainingTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the tip text for this property
useUnsmoothedTipText() - Method in class weka.classifiers.trees.m5.M5Base: Returns the tip text for this property
useVariant1TipText() - Method in class weka.classifiers.functions.supportVector.RegSMOImproved: Returns the tip text for this property
Utils - Class in weka.core: Class implementing some simple utility methods.
Utils() - Constructor for class weka.core.Utils

V

VAL_ANIMATEDICONPATH - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the animatedIconPath property
VAL_ASSOCIATEDCONNECTIONS - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the associatedConnections property
VAL_BEAN - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the bean property
VAL_BEANCONTEXT - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the beanContext property
VAL_BLUE - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the blue property
VAL_CELLS - Static variable in class weka.core.xml.XMLBasicSerialization: the matrix cells
VAL_COLOR - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the color property
VAL_CUSTOM_NAME - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the customName property
VAL_DATE - Static variable in class weka.core.xml.XMLInstances: the value for date
VAL_DIR - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the dir property
VAL_EVENTNAME - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the eventname property
VAL_FILE - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the file property
VAL_FONT - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the font property
VAL_GREEN - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the green property
VAL_HEIGHT - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the height property
VAL_HIDDEN - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the hidden property
VAL_ICONPATH - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the iconpath property
VAL_ID - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the id property
VAL_INPUTS - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the input property
VAL_INPUTSID - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the input id property
VAL_KEY - Static variable in class weka.core.xml.XMLBasicSerialization: the value for a mapping-key, e.g., Maps
VAL_LOADER - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the loader property
VAL_LOCATION - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the location property
VAL_MAPPING - Static variable in class weka.core.xml.XMLBasicSerialization: the value for mapping, e.g., Maps
VAL_NAME - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the value property
VAL_NO - Static variable in class weka.core.xml.XMLDocument: the value "no".
VAL_NO - Static variable in class weka.core.xml.XMLSerialization: the value "no" for the primitive and array attribute
VAL_NOMINAL - Static variable in class weka.core.xml.XMLInstances: the value for nominal
VAL_NORMAL - Static variable in class weka.core.xml.XMLInstances: the value for normal
VAL_NUMERIC - Static variable in class weka.core.xml.XMLInstances: the value for numeric
VAL_OPTIONS - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the options property
VAL_ORIGINALCOORDS - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the originalCoords property
VAL_OUTPUTS - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the outputs id property
VAL_OUTPUTSID - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the outputs property
VAL_PREFIX - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the prefix property
VAL_RED - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the red property
VAL_RELATIONAL - Static variable in class weka.core.xml.XMLInstances: the value for relational
VAL_RELATIONNAMEFORFILENAME - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the relationNameForFilename property (Saver)
VAL_RELATIVE_PATH - Static variable in class weka.gui.beans.xml.XMLBeans
VAL_ROOT - Static variable in class weka.core.xml.XMLSerialization: the value of the name for the root node
VAL_SAVER - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the saver property
VAL_SIZE - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the size property
VAL_SOURCEID - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the source property
VAL_SPARSE - Static variable in class weka.core.xml.XMLInstances: the value for sparse
VAL_STRING - Static variable in class weka.core.xml.XMLInstances: the value for string
VAL_STYLE - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the style property
VAL_SUBFLOW - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the subFlow property
VAL_TARGETID - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the target property
VAL_TEXT - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the text property
VAL_TYPE_CLASSIFIER - Static variable in class weka.core.xml.XMLOptions: a value of the type attribute.
VAL_TYPE_FLAG - Static variable in class weka.core.xml.XMLOptions: a value of the type attribute.
VAL_TYPE_HYPHENS - Static variable in class weka.core.xml.XMLOptions: a value of the type attribute.
VAL_TYPE_OPTIONHANDLER - Static variable in class weka.core.xml.XMLOptions: a value of the type attribute.
VAL_TYPE_QUOTES - Static variable in class weka.core.xml.XMLOptions: a value of the type attribute.
VAL_TYPE_SINGLE - Static variable in class weka.core.xml.XMLOptions: a value of the type attribute.
VAL_VALUE - Static variable in class weka.core.xml.XMLBasicSerialization: the value for mapping-value, e.g., Maps
VAL_WIDTH - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the width property
VAL_X - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the x property
VAL_Y - Static variable in class weka.gui.beans.xml.XMLBeans: the value of the y property
VAL_YES - Static variable in class weka.core.xml.XMLDocument: the value "yes".
VAL_YES - Static variable in class weka.core.xml.XMLSerialization: the value "yes" for the primitive and array attribute
validateFileFormat(Tag) - Method in class weka.gui.beans.SerializedModelSaver: Validate the file format.
validationChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
validationSetSizeTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
validationThresholdTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
value - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: scale factor or stop parameter
value(int) - Method in class weka.core.Attribute: Returns a value of a nominal or string attribute.
value(int) - Method in class weka.core.BinarySparseInstance: Returns an instance's attribute value in internal format.
value(int) - Method in class weka.core.Instance: Returns an instance's attribute value in internal format.
value(Attribute) - Method in class weka.core.Instance: Returns an instance's attribute value in internal format.
value(int) - Method in class weka.core.SparseInstance: Returns an instance's attribute value in internal format.
value - Variable in class weka.experiment.PropertyNode: The current property value
valueIndicesTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
valueIsSmallerEqual(Instance, int, double) - Method in class weka.core.EuclideanDistance: Returns true if the value of the given dimension is smaller or equal the value to be compared with.
valueOf(String) - Static method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.Capabilities.Capability: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.logging.Logger.Level: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.pmml.FieldMetaInfo.Optype: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.pmml.FieldMetaInfo.Value.Property: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.RevisionUtils.Type: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.TechnicalInformation.Field: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum weka.core.TechnicalInformation.Type: Returns the enum constant of this type with the specified name.
values() - Static method in enum weka.associations.FPGrowth.AssociationRule.METRIC_TYPE: Returns an array containing the constants of this enum type, in the order they are declared.
Values - Class in weka.classifiers.trees.m5: Stores some statistics.
Values(int, int, int, Instances) - Constructor for class weka.classifiers.trees.m5.Values: Constructs an object which stores some statistics of the instances such as sum, squared sum, variance, standard deviation
values() - Static method in enum weka.core.Capabilities.Capability: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.logging.Logger.Level: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.pmml.FieldMetaInfo.Interval.Closure: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.pmml.FieldMetaInfo.Optype: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.pmml.FieldMetaInfo.Value.Property: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.RevisionUtils.Type: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.TechnicalInformation.Field: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum weka.core.TechnicalInformation.Type: Returns an array containing the constants of this enum type, in the order they are declared.
valuesListTipText() - Method in class weka.datagenerators.clusterers.SubspaceClusterDefinition: Returns the tip text for this property
valuesOutputTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
valueSparse(int) - Method in class weka.core.BinarySparseInstance: Returns an instance's attribute value in internal format.
valueSparse(int) - Method in class weka.core.Instance: Returns an instance's attribute value in internal format.
valuesToString() - Method in class weka.associations.tertius.Rule: Return a String giving the confirmation and optimistic estimate of this rule.
VARIABLE - Static variable in interface weka.core.mathematicalexpression.sym
variance(int) - Method in class weka.core.Instances: Computes the variance for a numeric attribute.
variance(Attribute) - Method in class weka.core.Instances: Computes the variance for a numeric attribute.
variance(double[]) - Static method in class weka.core.Utils: Computes the variance for an array of doubles.
varianceCoveredTipText() - Method in class weka.attributeSelection.PrincipalComponents: Returns the tip text for this property
varianceCoveredTipText() - Method in class weka.filters.unsupervised.attribute.PrincipalComponents: Returns the tip text for this property.
VaryNode - Class in weka.classifiers.bayes.net: Part of ADTree implementation.
VaryNode(int) - Constructor for class weka.classifiers.bayes.net.VaryNode: Creates new VaryNode
VERBOSE - Static variable in class weka.core.ClassDiscovery: whether to output some debug information.
VERBOSE - Static variable in class weka.gui.GenericPropertiesCreator: whether to output some debug information
verboseTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.LinearForwardSelection: Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.RandomSearch: Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.SubsetSizeForwardSelection: Returns the tip text for this property
verboseTipText() - Method in class weka.classifiers.meta.Dagging: Returns the tip text for this property
Version - Class in weka.core: This class contains the version number of the current WEKA release and some methods for comparing another version string.
Version() - Constructor for class weka.core.Version
VERSION - Static variable in class weka.core.Version: the complete version
VERSION_FILE - Static variable in class weka.core.Version: the version file
VFI - Class in weka.classifiers.misc: Classification by voting feature intervals.
VFI() - Constructor for class weka.classifiers.misc.VFI
ViewerDialog - Class in weka.gui: A downsized version of the ArffViewer, displaying only one Instances-Object.
ViewerDialog(Frame) - Constructor for class weka.gui.ViewerDialog: initializes the dialog with the given parent
Visible - Interface in weka.gui.beans: Interface to something that has a visible (via BeanVisual) reprentation
VisualizableErrorEvent - Class in weka.gui.beans: Event encapsulating error information for a learning scheme that can be visualized in the DataVisualizer
VisualizableErrorEvent(Object, PlotData2D) - Constructor for class weka.gui.beans.VisualizableErrorEvent
VisualizableErrorListener - Interface in weka.gui.beans: Interface to something that can accept VisualizableErrorEvents
VisualizePanel - Class in weka.gui.explorer: A slightly extended MatrixPanel for better support in the Explorer.
VisualizePanel() - Constructor for class weka.gui.explorer.VisualizePanel
VisualizePanel - Class in weka.gui.visualize: This panel allows the user to visualize a dataset (and if provided) a classifier's/clusterer's predictions in two dimensions.
VisualizePanel(VisualizePanelListener) - Constructor for class weka.gui.visualize.VisualizePanel: This constructor allows a VisualizePanelListener to be set.
VisualizePanel() - Constructor for class weka.gui.visualize.VisualizePanel: Constructor
VisualizePanelEvent - Class in weka.gui.visualize: This event Is fired to a listeners 'userDataEvent' function when The user on the VisualizePanel clicks submit.
VisualizePanelEvent(FastVector, Instances, Instances, int, int) - Constructor for class weka.gui.visualize.VisualizePanelEvent: This constructor creates the event with all the parameters set.
VisualizePanelListener - Interface in weka.gui.visualize: Interface implemented by a class that is interested in receiving submited shapes from a visualize panel.
VisualizePlugin - Interface in weka.gui.visualize.plugins: Interface implemented by classes loaded dynamically to visualize classifier results in the explorer.
VisualizeUtils - Class in weka.gui.visualize: This class contains utility routines for visualization
VisualizeUtils() - Constructor for class weka.gui.visualize.VisualizeUtils
VLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
Vote - Class in weka.classifiers.meta: Class for combining classifiers.
Vote() - Constructor for class weka.classifiers.meta.Vote
VotedPerceptron - Class in weka.classifiers.functions: Implementation of the voted perceptron algorithm by Freund and Schapire.
VotedPerceptron() - Constructor for class weka.classifiers.functions.VotedPerceptron
voteFlagTipText() - Method in class weka.datagenerators.classifiers.classification.RDG1: Returns the tip text for this property

W

waitUntilFinished() - Method in class weka.gui.beans.FlowRunner: Waits until all flows have finished executing before returning
WAODE - Class in weka.classifiers.bayes: WAODE contructs the model called Weightily Averaged One-Dependence Estimators.

For more information, see

L.
WAODE() - Constructor for class weka.classifiers.bayes.WAODE
WARNING - Static variable in class weka.core.Debug: the log level Warning
Wavelet - Class in weka.filters.unsupervised.attribute: A filter for wavelet transformation.

For more information see:

Wikipedia (2004).
Wavelet() - Constructor for class weka.filters.unsupervised.attribute.Wavelet: default constructor
weight() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the weight assigned to this prediction.
weight() - Method in class weka.classifiers.evaluation.NumericPrediction: Gets the weight assigned to this prediction.
weight() - Method in interface weka.classifiers.evaluation.Prediction: Gets the weight assigned to this prediction.
weight(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList: Returns the weight a rule assigns to an instance.
weight() - Method in class weka.core.Attribute: Returns the attribute's weight.
weight() - Method in class weka.core.Instance: Returns the instance's weight.
WEIGHT_INVERSE - Static variable in class weka.classifiers.lazy.IBk: weight by 1/distance.
WEIGHT_NONE - Static variable in class weka.classifiers.lazy.IBk: no weighting.
WEIGHT_SIMILARITY - Static variable in class weka.classifiers.lazy.IBk: weight by 1-distance.
weightByConfidenceTipText() - Method in class weka.classifiers.misc.VFI: Returns the tip text for this property
weightByDistanceTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
weightedAreaUnderROC() - Method in class weka.classifiers.Evaluation: Calculates the weighted (by class size) AUC.
weightedFalseNegativeRate() - Method in class weka.classifiers.Evaluation: Calculates the weighted (by class size) false negative rate.
weightedFalsePositiveRate() - Method in class weka.classifiers.Evaluation: Calculates the weighted (by class size) false positive rate.
weightedFMeasure() - Method in class weka.classifiers.Evaluation: Calculates the weighted (by class size) F-Measure.
WeightedInstancesHandler - Interface in weka.core: Interface to something that makes use of the information provided by instance weights.
weightedPrecision() - Method in class weka.classifiers.Evaluation: Calculates the weighted (by class size) false precision.
weightedRecall() - Method in class weka.classifiers.Evaluation: Calculates the weighted (by class size) recall.
weightedTrueNegativeRate() - Method in class weka.classifiers.Evaluation: Calculates the weighted (by class size) true negative rate.
weightedTruePositiveRate() - Method in class weka.classifiers.Evaluation: Calculates the weighted (by class size) true positive rate.
weightingKernelTipText() - Method in class weka.classifiers.lazy.LWL: Returns the tip text for this property.
WEIGHTMETHOD_1 - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: weight method: 1.0
WEIGHTMETHOD_INVERSE1 - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: weight method: 1.0 / Total # of prop.
WEIGHTMETHOD_INVERSE2 - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: weight method: Total # of prop.
WEIGHTMETHOD_ORIGINAL - Static variable in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: weight method: keep the weight to be the same as the original value
weightMethodTipText() - Method in class weka.classifiers.mi.MIWrapper: Returns the tip text for this property
weightMethodTipText() - Method in class weka.filters.unsupervised.attribute.MultiInstanceToPropositional: Returns the tip text for this property
weights(Instance) - Method in class weka.classifiers.trees.j48.BinC45Split: Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.C45Split: Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.GraftSplit
weights(Instance) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Always returns null because there is only one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.NBTreeSplit: Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.NoSplit: Always returns null because there is only one subset.
weights(Instance) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Method not in use
weightsTipText() - Method in class weka.classifiers.functions.LibLINEAR: Returns the tip text for this property
weightsTipText() - Method in class weka.classifiers.functions.LibSVM: Returns the tip text for this property
weightThresholdTipText() - Method in class weka.classifiers.meta.AdaBoostM1: Returns the tip text for this property
weightThresholdTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
weightTipText() - Method in class weka.classifiers.bayes.AODE: Returns the tip text for this property
weightTrimBetaTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
weightTrimBetaTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
weightTrimBetaTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
weightValue(int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to get the weight value on a particular connection.
weightValue(int) - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to get the weight value on a particular connection.
weka.associations - package weka.associations
weka.associations.gsp - package weka.associations.gsp
weka.associations.tertius - package weka.associations.tertius
weka.attributeSelection - package weka.attributeSelection
weka.classifiers - package weka.classifiers
weka.classifiers.bayes - package weka.classifiers.bayes
weka.classifiers.bayes.blr - package weka.classifiers.bayes.blr
weka.classifiers.bayes.net - package weka.classifiers.bayes.net
weka.classifiers.bayes.net.estimate - package weka.classifiers.bayes.net.estimate
weka.classifiers.bayes.net.search - package weka.classifiers.bayes.net.search
weka.classifiers.bayes.net.search.ci - package weka.classifiers.bayes.net.search.ci
weka.classifiers.bayes.net.search.fixed - package weka.classifiers.bayes.net.search.fixed
weka.classifiers.bayes.net.search.global - package weka.classifiers.bayes.net.search.global
weka.classifiers.bayes.net.search.local - package weka.classifiers.bayes.net.search.local
weka.classifiers.evaluation - package weka.classifiers.evaluation
weka.classifiers.functions - package weka.classifiers.functions
weka.classifiers.functions.neural - package weka.classifiers.functions.neural
weka.classifiers.functions.pace - package weka.classifiers.functions.pace
weka.classifiers.functions.supportVector - package weka.classifiers.functions.supportVector
weka.classifiers.lazy - package weka.classifiers.lazy
weka.classifiers.lazy.kstar - package weka.classifiers.lazy.kstar
weka.classifiers.meta - package weka.classifiers.meta
weka.classifiers.meta.nestedDichotomies - package weka.classifiers.meta.nestedDichotomies
weka.classifiers.mi - package weka.classifiers.mi
weka.classifiers.mi.supportVector - package weka.classifiers.mi.supportVector
weka.classifiers.misc - package weka.classifiers.misc
weka.classifiers.pmml.consumer - package weka.classifiers.pmml.consumer
weka.classifiers.rules - package weka.classifiers.rules
weka.classifiers.rules.part - package weka.classifiers.rules.part
weka.classifiers.trees - package weka.classifiers.trees
weka.classifiers.trees.adtree - package weka.classifiers.trees.adtree
weka.classifiers.trees.ft - package weka.classifiers.trees.ft
weka.classifiers.trees.j48 - package weka.classifiers.trees.j48
weka.classifiers.trees.lmt - package weka.classifiers.trees.lmt
weka.classifiers.trees.m5 - package weka.classifiers.trees.m5
weka.classifiers.xml - package weka.classifiers.xml
weka.clusterers - package weka.clusterers
weka.clusterers.forOPTICSAndDBScan.Databases - package weka.clusterers.forOPTICSAndDBScan.Databases
weka.clusterers.forOPTICSAndDBScan.DataObjects - package weka.clusterers.forOPTICSAndDBScan.DataObjects
weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI - package weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI
weka.clusterers.forOPTICSAndDBScan.Utils - package weka.clusterers.forOPTICSAndDBScan.Utils
weka.core - package weka.core
weka.core.converters - package weka.core.converters
weka.core.logging - package weka.core.logging
weka.core.mathematicalexpression - package weka.core.mathematicalexpression
weka.core.matrix - package weka.core.matrix
weka.core.neighboursearch - package weka.core.neighboursearch
weka.core.neighboursearch.balltrees - package weka.core.neighboursearch.balltrees
weka.core.neighboursearch.covertrees - package weka.core.neighboursearch.covertrees
weka.core.neighboursearch.kdtrees - package weka.core.neighboursearch.kdtrees
weka.core.pmml - package weka.core.pmml
weka.core.stemmers - package weka.core.stemmers
weka.core.tokenizers - package weka.core.tokenizers
weka.core.xml - package weka.core.xml
weka.datagenerators - package weka.datagenerators
weka.datagenerators.classifiers.classification - package weka.datagenerators.classifiers.classification
weka.datagenerators.classifiers.regression - package weka.datagenerators.classifiers.regression
weka.datagenerators.clusterers - package weka.datagenerators.clusterers
weka.estimators - package weka.estimators
weka.experiment - package weka.experiment
weka.experiment.xml - package weka.experiment.xml
weka.filters - package weka.filters
weka.filters.supervised.attribute - package weka.filters.supervised.attribute
weka.filters.supervised.instance - package weka.filters.supervised.instance
weka.filters.unsupervised.attribute - package weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance - package weka.filters.unsupervised.instance
weka.filters.unsupervised.instance.subsetbyexpression - package weka.filters.unsupervised.instance.subsetbyexpression
weka.gui - package weka.gui
weka.gui.arffviewer - package weka.gui.arffviewer
weka.gui.beans - package weka.gui.beans
weka.gui.beans.xml - package weka.gui.beans.xml
weka.gui.boundaryvisualizer - package weka.gui.boundaryvisualizer
weka.gui.experiment - package weka.gui.experiment
weka.gui.explorer - package weka.gui.explorer
weka.gui.graphvisualizer - package weka.gui.graphvisualizer
weka.gui.hierarchyvisualizer - package weka.gui.hierarchyvisualizer
weka.gui.sql - package weka.gui.sql
weka.gui.sql.event - package weka.gui.sql.event
weka.gui.streams - package weka.gui.streams
weka.gui.treevisualizer - package weka.gui.treevisualizer
weka.gui.visualize - package weka.gui.visualize
weka.gui.visualize.plugins - package weka.gui.visualize.plugins
WekaException - Exception in weka.core: Class for Weka-specific exceptions.
WekaException() - Constructor for exception weka.core.WekaException: Creates a new WekaException with no message.
WekaException(String) - Constructor for exception weka.core.WekaException: Creates a new WekaException.
wekaStaticWrapper(Sourcable, String) - Static method in class weka.classifiers.Evaluation: Wraps a static classifier in enough source to test using the weka class libraries.
wekaStaticWrapper(Sourcable, String, Instances, Instances) - Static method in class weka.filters.Filter: generates source code from the filter
WekaTaskMonitor - Class in weka.gui: This panel records the number of weka tasks running and displays a simple bird animation while their are active tasks
WekaTaskMonitor() - Constructor for class weka.gui.WekaTaskMonitor: Constructor
WekaWrapper - Interface in weka.gui.beans: Interface to something that can wrap around a class of Weka algorithms (classifiers, filters etc).
WEST_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.BinC45Split: Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.C45Split: Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.GraftSplit
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Always returns 0 because only there is only one subset.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.NBTreeSplit: Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.NoSplit: Always returns 0 because only there is only one subset.
whichSubset(Instance) - Method in class weka.classifiers.trees.lmt.ResidualSplit
wholeDataErrTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
width() - Method in class weka.core.matrix.ExponentialFormat
width() - Method in class weka.core.matrix.FlexibleDecimalFormat
width() - Method in class weka.core.matrix.FloatingPointFormat
WIDTH - Static variable in class weka.core.neighboursearch.KDTree: The index of WIDTH (MAX-MIN) value in attributes' range array.
WIDTH - Static variable in class weka.core.neighboursearch.kdtrees.KDTreeNodeSplitter: Index of width value (max-min) in an array of attributes' range.
WIDTH - Static variable in class weka.gui.arffviewer.ArffViewerMainPanel: default width
WIDTH - Static variable in class weka.gui.sql.SqlViewer: the width property in the history file.
WIN_STRING - Variable in class weka.experiment.ResultMatrix: win string
windowActivated(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer: invoked when a window is activated
windowClosed(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer: invoked when a window is closed
windowClosing(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer: invoked when a window is in the process of closing
windowDeactivated(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer: invoked when a window is deactivated
windowDeiconified(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer: invoked when a window is deiconified
windowIconified(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer: invoked when a window is iconified
windowListChanged() - Method in class weka.gui.Main: is called when window list changed somehow (add or remove).
windowOpened(WindowEvent) - Method in class weka.gui.arffviewer.ArffViewer: invoked when a window is has been opened
windowSizeTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property.
Winnow - Class in weka.classifiers.functions: Implements Winnow and Balanced Winnow algorithms by Littlestone.

For more information, see

N.
Winnow() - Constructor for class weka.classifiers.functions.Winnow
WITHIN_BATCH - Static variable in class weka.gui.beans.IncrementalClassifierEvent
wordsToKeepTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
WordTokenizer - Class in weka.core.tokenizers: A simple tokenizer that is using the java.util.StringTokenizer class to tokenize the strings.
WordTokenizer() - Constructor for class weka.core.tokenizers.WordTokenizer
WrapperSubsetEval - Class in weka.attributeSelection: WrapperSubsetEval:

Evaluates attribute sets by using a learning scheme.
WrapperSubsetEval() - Constructor for class weka.attributeSelection.WrapperSubsetEval: Constructor.
write(Writer) - Method in class weka.classifiers.CostMatrix: Writes out a matrix.
write(Instances) - Method in class weka.core.converters.ConverterUtils.DataSink: writes the given data either via the saver or to the defined output stream (depending on the constructor).
write(String, Instances) - Static method in class weka.core.converters.ConverterUtils.DataSink: writes the data to the given file.
write(Saver, Instances) - Static method in class weka.core.converters.ConverterUtils.DataSink: writes the data via the given saver.
write(OutputStream, Instances) - Static method in class weka.core.converters.ConverterUtils.DataSink: writes the data to the given stream (always in ARFF format).
write(Writer) - Method in class weka.core.matrix.Matrix: Writes out a matrix.
write(Writer) - Method in class weka.core.Matrix: Deprecated.

Writes out a matrix.
write(String, Object) - Static method in class weka.core.SerializationHelper: serializes the given object to the specified file.
write(OutputStream, Object) - Static method in class weka.core.SerializationHelper: serializes the given object to the specified stream.
write(String) - Method in class weka.core.Stopwords: Writes the current stopwords to the given file
write(File) - Method in class weka.core.Stopwords: Writes the current stopwords to the given file
write(BufferedWriter) - Method in class weka.core.Stopwords: Writes the current stopwords to the given writer.
write(byte[], int, int) - Method in class weka.core.Tee: Writes len bytes from the specified byte array starting at offset off to this stream.
write(int) - Method in class weka.core.Tee: Writes the specified byte to this stream.
write(String, Object) - Static method in class weka.core.xml.KOML: writes the XML-serialized object to the given file
write(File, Object) - Static method in class weka.core.xml.KOML: write the XML-serialized object to the given file
write(OutputStream, Object) - Static method in class weka.core.xml.KOML: writes the XML-serialized object to a stream
write(String) - Method in class weka.core.xml.XMLDocument: writes the current DOM document into the given file.
write(File) - Method in class weka.core.xml.XMLDocument: writes the current DOM document into the given file.
write(OutputStream) - Method in class weka.core.xml.XMLDocument: writes the current DOM document into the given stream.
write(Writer) - Method in class weka.core.xml.XMLDocument: writes the current DOM document into the given writer.
write(String, Object) - Method in class weka.core.xml.XMLSerialization: writes the given object into the file
write(File, Object) - Method in class weka.core.xml.XMLSerialization: writes the given object into the file
write(OutputStream, Object) - Method in class weka.core.xml.XMLSerialization: writes the given object into the stream
write(Writer, Object) - Method in class weka.core.xml.XMLSerialization: writes the given object into the writer
write() - Method in class weka.core.xml.XMLSerializationMethodHandler: returns the handler for write methods
write(String, Object) - Static method in class weka.core.xml.XStream: writes the XML-serialized object to the given file
write(File, Object) - Static method in class weka.core.xml.XStream: write the XML-serialized object to the given file
write(OutputStream, Object) - Static method in class weka.core.xml.XStream: writes the XML-serialized object to the given output stream
write(Writer, Object) - Static method in class weka.core.xml.XStream: writes the XML-serialized object to the given Writer
write(String, Experiment) - Static method in class weka.experiment.Experiment: Writes the experiment to disk.
writeAll(String, Object[]) - Static method in class weka.core.SerializationHelper: serializes the given objects to the specified file.
writeAll(OutputStream, Object[]) - Static method in class weka.core.SerializationHelper: serializes the given objects to the specified stream.
writeBatch() - Method in class weka.core.converters.AbstractSaver: Writes to a file in batch mode To be overridden.
writeBatch() - Method in class weka.core.converters.ArffSaver: Writes a Batch of instances
writeBatch() - Method in class weka.core.converters.C45Saver: Writes a Batch of instances
writeBatch() - Method in class weka.core.converters.CSVSaver: Writes a Batch of instances
writeBatch() - Method in class weka.core.converters.DatabaseSaver: Writes a Batch of instances.
writeBatch() - Method in class weka.core.converters.LibSVMSaver: Writes a Batch of instances
writeBatch() - Method in interface weka.core.converters.Saver: Writes to a destination in batch mode
writeBatch() - Method in class weka.core.converters.SerializedInstancesSaver: Writes a Batch of instances.
writeBatch() - Method in class weka.core.converters.SVMLightSaver: Writes a Batch of instances.
writeBatch() - Method in class weka.core.converters.XRFFSaver: Writes a Batch of instances
writeBeanConnection(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given BeanConncetion to a DOM structure.
writeBeanInstance(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given BeanInstance to a DOM structure.
writeBeanLoader(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given Loader (a bean) to a DOM structure.
writeBeanSaver(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given Saver (a bean) to a DOM structure.
writeBeanVisual(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given BeanVisual to a DOM structure.
writeCollection(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization: adds the given Collection to a DOM structure.
writeColor(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given Color to a DOM structure.
writeColorUIResource(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given ColorUIResource to a DOM structure.
writeCostMatrixOld(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization: adds the given CostMatrix (old) to a DOM structure.
writeCurve(String, Estimator, double, double, int) - Static method in class weka.estimators.EstimatorUtils: Output of an n points of a density curve.
writeCurve(String, Estimator, Estimator, double, double, double, int) - Static method in class weka.estimators.EstimatorUtils: Output of an n points of a density curve.
writeDefaultListModel(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization: adds the given DefaultListModel to a DOM structure.
writeDimension(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given Dimension to a DOM structure.
writeDOT(String, String, FastVector, FastVector) - Static method in class weka.gui.graphvisualizer.DotParser: This method saves a graph in a file in DOT format.
writeFont(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given Font to a DOM structure.
writeFontUIResource(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given FontUIResource to a DOM structure.
writeIncremental(Instance) - Method in class weka.core.converters.AbstractSaver: Method for incremental saving.
writeIncremental(Instance) - Method in class weka.core.converters.ArffSaver: Saves an instances incrementally.
writeIncremental(Instance) - Method in class weka.core.converters.C45Saver: Saves an instances incrementally.
writeIncremental(Instance) - Method in class weka.core.converters.CSVSaver: Saves an instances incrementally.
writeIncremental(Instance) - Method in class weka.core.converters.DatabaseSaver: Saves an instances incrementally.
writeIncremental(Instance) - Method in class weka.core.converters.LibSVMSaver: Saves an instances incrementally.
writeIncremental(Instance) - Method in interface weka.core.converters.Saver: Writes to a destination in incremental mode.
writeIncremental(Instance) - Method in class weka.core.converters.SVMLightSaver: Saves an instances incrementally.
writeLoader(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given Loader to a DOM structure.
writeMap(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization: adds the given Map to a DOM structure.
writeMatrix(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization: adds the given Matrix to a DOM structure.
writeMatrixOld(Element, Object, String) - Method in class weka.core.xml.XMLBasicSerialization: adds the given Matrix (old) to a DOM structure.
writeMetaBean(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given MetaBean to a DOM structure.
writeOPTICSresultsTipText() - Method in class weka.clusterers.OPTICS: Returns the tip text for this property
writePoint(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given Point to a DOM structure.
writePropertyNode(Element, Object, String) - Method in class weka.experiment.xml.XMLExperiment: adds the given PropertyNode to a DOM structure.
writeSaver(Element, Object, String) - Method in class weka.gui.beans.xml.XMLBeans: adds the given Saver to a DOM structure.
writeToFile(String, Object) - Static method in class weka.core.Debug: Writes the given object to the specified file.
writeToFile(String, String) - Static method in class weka.core.Debug: Writes the given message to the specified file.
writeToFile(String, Object, boolean) - Static method in class weka.core.Debug: Writes the given object to the specified file.
writeToFile(String, String, boolean) - Static method in class weka.core.Debug: Writes the given message to the specified file.
writeToXML(Element, Object, String) - Method in class weka.core.xml.XMLSerialization: adds the given Object to a DOM structure.
writeXMLBIF03(String, String, FastVector, FastVector) - Static method in class weka.gui.graphvisualizer.BIFParser: This method writes a graph in XMLBIF ver.

X

x - Variable in class weka.gui.graphvisualizer.GraphNode: The x and y position of the node
X_SHAPE - Static variable in class weka.gui.visualize.Plot2D
XBaseTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
XExpressionTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
xLabelFreqTipText() - Method in class weka.gui.beans.StripChart: GUI Tip text
xlogx(int) - Static method in class weka.core.Utils: Returns c*log2(c) for a given integer value c.
XMaxTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
XMeans - Class in weka.clusterers: Cluster data using the X-means algorithm.

X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region.
XMeans() - Constructor for class weka.clusterers.XMeans: the default constructor.
XMinTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
XMLBasicSerialization - Class in weka.core.xml: This serializer contains some read/write methods for common classes that are not beans-conform.
XMLBasicSerialization() - Constructor for class weka.core.xml.XMLBasicSerialization: initializes the serialization
XMLBeans - Class in weka.gui.beans.xml: This class serializes and deserializes a KnowledgeFlow setup to and fro XML.
XMLBeans(JComponent, BeanContextSupport) - Constructor for class weka.gui.beans.xml.XMLBeans: initializes the serialization for layouts
XMLBeans(JComponent, BeanContextSupport, int) - Constructor for class weka.gui.beans.xml.XMLBeans: initializes the serialization for different types of data
XMLClassifier - Class in weka.classifiers.xml: This class serializes and deserializes a Classifier instance to and fro XML.
XMLClassifier() - Constructor for class weka.classifiers.xml.XMLClassifier: initializes the serialization
XMLDocument - Class in weka.core.xml: This class offers some methods for generating, reading and writing XML documents.
It can only handle UTF-8.
XMLDocument() - Constructor for class weka.core.xml.XMLDocument: initializes the factory with non-validating parser.
XMLDocument(String) - Constructor for class weka.core.xml.XMLDocument: Creates a new instance of XMLDocument.
XMLDocument(File) - Constructor for class weka.core.xml.XMLDocument: Creates a new instance of XMLDocument.
XMLDocument(InputStream) - Constructor for class weka.core.xml.XMLDocument: Creates a new instance of XMLDocument.
XMLDocument(Reader) - Constructor for class weka.core.xml.XMLDocument: Creates a new instance of XMLDocument.
XMLExperiment - Class in weka.experiment.xml: This class serializes and deserializes an Experiment instance to and fro XML.
It omits the options from the Experiment, since these are handled by the get/set-methods.
XMLExperiment() - Constructor for class weka.experiment.xml.XMLExperiment: initializes the serialization
XMLInstances - Class in weka.core.xml: XML representation of the Instances class.
XMLInstances() - Constructor for class weka.core.xml.XMLInstances: the default constructor
XMLInstances(Instances) - Constructor for class weka.core.xml.XMLInstances: generates the XML structure based on the given data
XMLInstances(Reader) - Constructor for class weka.core.xml.XMLInstances: generates the Instances directly from the reader containing the XML data.
XMLOptions - Class in weka.core.xml: A class for transforming options listed in XML to a regular WEKA command line string.
XMLOptions() - Constructor for class weka.core.xml.XMLOptions: Creates a new instance of XMLOptions.
XMLOptions(String) - Constructor for class weka.core.xml.XMLOptions: Creates a new instance of XMLOptions.
XMLOptions(File) - Constructor for class weka.core.xml.XMLOptions: Creates a new instance of XMLOptions.
XMLOptions(InputStream) - Constructor for class weka.core.xml.XMLOptions: Creates a new instance of XMLOptions.
XMLOptions(Reader) - Constructor for class weka.core.xml.XMLOptions: Creates a new instance of XMLOptions.
xmlRules() - Method in class weka.associations.FPGrowth
XMLSerialization - Class in weka.core.xml: With this class objects can be serialized to XML instead into a binary format.
XMLSerialization() - Constructor for class weka.core.xml.XMLSerialization: initializes the serialization
XMLSerializationMethodHandler - Class in weka.core.xml: This class handles relationships between display names of properties (or classes) and Methods that are associated with them.
XMLSerializationMethodHandler(Object) - Constructor for class weka.core.xml.XMLSerializationMethodHandler: initializes the method handling, executes also clear(), which adds initial methods automatically.
XPropertyTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
XRFFLoader - Class in weka.core.converters: Reads a source that is in the XML version of the ARFF format.
XRFFLoader() - Constructor for class weka.core.converters.XRFFLoader
XRFFSaver - Class in weka.core.converters: Writes to a destination that is in the XML version of the ARFF format.
XRFFSaver() - Constructor for class weka.core.converters.XRFFSaver: Constructor
xStats - Variable in class weka.experiment.PairedStats: The stats associated with the data in column 1
XStepTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
XStream - Class in weka.core.xml: This class is a helper class for XML serialization using XStream .
XStream() - Constructor for class weka.core.xml.XStream
XSTREAM - Static variable in class weka.gui.beans.SerializedModelSaver
XVALTAGS_SELECTION - Static variable in class weka.attributeSelection.RaceSearch
xySum - Variable in class weka.experiment.PairedStats: The sum of the products

Y

y - Variable in class weka.gui.graphvisualizer.GraphNode: The x and y position of the node
YBaseTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
YExpressionTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
YMaxTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
YMinTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
YongSplitInfo - Class in weka.classifiers.trees.m5: Stores split information.
YongSplitInfo(int, int, int) - Constructor for class weka.classifiers.trees.m5.YongSplitInfo: Constructs an object which contains the split information
YPropertyTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
yStats - Variable in class weka.experiment.PairedStats: The stats associated with the data in column 2
YStepTipText() - Method in class weka.classifiers.meta.GridSearch: Returns the tip text for this property
yybegin(int) - Method in class weka.core.mathematicalexpression.Scanner: Enters a new lexical state
yybegin(int) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Enters a new lexical state
yycharat(int) - Method in class weka.core.mathematicalexpression.Scanner: Returns the character at position pos from the matched text.
yycharat(int) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Returns the character at position pos from the matched text.
yyclose() - Method in class weka.core.mathematicalexpression.Scanner: Closes the input stream.
yyclose() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Closes the input stream.
YYEOF - Static variable in class weka.core.mathematicalexpression.Scanner: This character denotes the end of file
YYEOF - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: This character denotes the end of file
YYINITIAL - Static variable in class weka.core.mathematicalexpression.Scanner: lexical states
YYINITIAL - Static variable in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner
yylength() - Method in class weka.core.mathematicalexpression.Scanner: Returns the length of the matched text region.
yylength() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Returns the length of the matched text region.
yypushback(int) - Method in class weka.core.mathematicalexpression.Scanner: Pushes the specified amount of characters back into the input stream.
yypushback(int) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Pushes the specified amount of characters back into the input stream.
yyreset(Reader) - Method in class weka.core.mathematicalexpression.Scanner: Resets the scanner to read from a new input stream.
yyreset(Reader) - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Resets the scanner to read from a new input stream.
yystate() - Method in class weka.core.mathematicalexpression.Scanner: Returns the current lexical state.
yystate() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Returns the current lexical state.
yytext() - Method in class weka.core.mathematicalexpression.Scanner: Returns the text matched by the current regular expression.
yytext() - Method in class weka.filters.unsupervised.instance.subsetbyexpression.Scanner: Returns the text matched by the current regular expression.

Z

ZeroR - Class in weka.classifiers.rules: Class for building and using a 0-R classifier.
ZeroR() - Constructor for class weka.classifiers.rules.ZeroR
zipit(String, String) - Method in class weka.experiment.OutputZipper: Saves a string to either an individual gzipped file or as an entry in a zip file.

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