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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.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.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.
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
AlgorithmListPanel.ObjectCellRenderer() - Constructor for class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
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
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.
ArffLoader.ArffReader(Reader) - Constructor for class weka.core.converters.ArffLoader.ArffReader: Reads the data completely from the reader.
ArffLoader.ArffReader(Reader, int) - Constructor for class weka.core.converters.ArffLoader.ArffReader: Reads only the header and reserves the specified space for instances.
ArffLoader.ArffReader(Reader, Instances, int) - Constructor for class weka.core.converters.ArffLoader.ArffReader: Reads the data without header according to the specified template.
ArffLoader.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.
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
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.
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
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.Link2(Object[], double) - Constructor for class weka.attributeSelection.BestFirst.Link2: Constructor
BestFirst.LinkedList2 - Class in weka.attributeSelection: Class for handling a linked list.
BestFirst.LinkedList2(int) - Constructor for class weka.attributeSelection.BestFirst.LinkedList2
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
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

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

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.CheckBoxListModel() - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel

initializes the model with no data.

CheckBoxList.CheckBoxListModel(Object[]) - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel

Constructs a CheckBoxListModel from an array of objects and then applies setModel to it.

CheckBoxList.CheckBoxListModel(Vector) - Constructor for class weka.gui.CheckBoxList.CheckBoxListModel

Constructs a CheckBoxListModel from a Vector and then applies setModel to it.

CheckBoxList.CheckBoxListRenderer - Class in weka.gui

A specialized CellRenderer for the CheckBoxList

CheckBoxList.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.EstTypes() - Constructor for class weka.estimators.CheckEstimator.EstTypes

Constructor

CheckEstimator.EstTypes(boolean, boolean, boolean) - Constructor for class weka.estimators.CheckEstimator.EstTypes

Constructor

CheckEstimator.PostProcessor - Class in weka.estimators

a class for postprocessing the test-data

CheckEstimator.PostProcessor() - Constructor for class weka.estimators.CheckEstimator.PostProcessor

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

CheckScheme.PostProcessor() - Constructor for class weka.core.CheckScheme.PostProcessor

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.

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.

ClassDiscovery.StringCompare() - Constructor for class weka.core.ClassDiscovery.StringCompare

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.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.PrecomputedKernelMatrixKernel

Frees the memory used by the kernel.

clean() - Method in class weka.classifiers.functions.supportVector.StringKernel

Frees the memory used by the kernel.

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

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

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.

ConsistencySubsetEval.hashKey(Instance, int) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey

Constructor for a hashKey

ConsistencySubsetEval.hashKey(double[]) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey

Constructor for a hashKey

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.DataSink(String) - Constructor for class weka.core.converters.ConverterUtils.DataSink

initializes the sink to save the data to the given file.

ConverterUtils.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).

ConverterUtils.DataSink(OutputStream) - Constructor for class weka.core.converters.ConverterUtils.DataSink

initializes the sink to save the data in the stream (always in ARFF format).

ConverterUtils.DataSource - Class in weka.core.converters

Helper class for loading data from files and URLs.

ConverterUtils.DataSource(String) - Constructor for class weka.core.converters.ConverterUtils.DataSource

Tries to load the data from the file.

ConverterUtils.DataSource(Instances) - Constructor for class weka.core.converters.ConverterUtils.DataSource

Initializes the datasource with the given dataset.

ConverterUtils.DataSource(Loader) - Constructor for class weka.core.converters.ConverterUtils.DataSource

Initializes the datasource with the given Loader.

ConverterUtils.DataSource(InputStream) - Constructor for class weka.core.converters.ConverterUtils.DataSource

Initializes the datasource with the given input stream.

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.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.

CoverTree.CoverTreeNode() - Constructor for class weka.core.neighboursearch.CoverTree.CoverTreeNode

Constructor for the class.

CoverTree.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 - Interface in weka.gui.beans: Indicator interface to something that can store instances to some destination
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
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.Clock() - Constructor for class weka.core.Debug.Clock: automatically starts the clock with FORMAT_SECONDS format and CPU time if available
Debug.Clock(int) - Constructor for class weka.core.Debug.Clock: automatically starts the clock with the given output format and CPU time if available
Debug.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
Debug.Clock(boolean, int) - Constructor for class weka.core.Debug.Clock: starts the clock depending on start immediately, using CPU time if available
Debug.DBO - Class in weka.core: contains debug methods
Debug.DBO() - Constructor for class weka.core.Debug.DBO
Debug.Log - Class in weka.core: A helper class for logging stuff.
Debug.Log() - Constructor for class weka.core.Debug.Log: default constructor, uses only stdout
Debug.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.
Debug.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.
Debug.Random - Class in weka.core: This extended Random class enables one to print the generated random numbers etc., before they are returned.
Debug.Random() - Constructor for class weka.core.Debug.Random: Creates a new random number generator.
Debug.Random(long) - Constructor for class weka.core.Debug.Random: Creates a new random number generator using a single long seed.
Debug.Random(boolean) - Constructor for class weka.core.Debug.Random: Creates a new random number generator.
Debug.Random(long, boolean) - Constructor for class weka.core.Debug.Random: Creates a new random number generator using a single long seed.
Debug.SimpleLog - Class in weka.core: A little, simple helper class for logging stuff.
Debug.SimpleLog() - Constructor for class weka.core.Debug.SimpleLog: default constructor, uses only stdout
Debug.SimpleLog(String) - Constructor for class weka.core.Debug.SimpleLog: Creates a logger that writes into the specified file.
Debug.SimpleLog(String, boolean) - Constructor for class weka.core.Debug.SimpleLog: Creates a logger that writes into the specified file.
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.
Debug.Timestamp() - Constructor for class weka.core.Debug.Timestamp: creates a timestamp with the current date and time and the default format.
Debug.Timestamp(String) - Constructor for class weka.core.Debug.Timestamp: creates a timestamp with the current date and time and the specified format.
Debug.Timestamp(Date) - Constructor for class weka.core.Debug.Timestamp: creates a timestamp with the given date and the default format.
Debug.Timestamp(Date, String) - Constructor for class weka.core.Debug.Timestamp: creates a timestamp with the given date and 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 string 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
DMNBtext.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
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
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
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.CapabilitiesFilterChangeEvent(Object, Capabilities) - Constructor for class weka.gui.explorer.Explorer.CapabilitiesFilterChangeEvent: Constructs a GOECapabilitiesFilterChangeEvent object.
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.
FastVector.FastVectorEnumeration(FastVector) - Constructor for class weka.core.FastVector.FastVectorEnumeration: Constructs an enumeration.
FastVector.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.