Index

Overview

Package

Class

Tree

Deprecated

Index

Help

Weka's home

PREV NEXT

FRAMES NO FRAMES

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

A

abortExperiment() - Method in class weka.experiment.RemoteExperiment: Set the abort flag
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
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
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
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.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
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
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(DataSetEvent) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Subclass must implement
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.ClassAssigner
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.ClassValuePicker
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(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.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.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
actEntropy - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the actual entropy
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.SimpleCLI: 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 classifier using Freund & Schapire's 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(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) - 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) - 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(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
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
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
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.
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
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.Filter: Add a data source listener
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(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Adds an element into the vector
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.
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: Applys a mathematical expression involving attributes and numeric constants to a dataset.
AddExpression() - Constructor for class weka.filters.unsupervised.attribute.AddExpression
addFirst(Object) - Method in class weka.associations.tertius.SimpleLinkedList
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
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: Adds an instance to the Cobweb tree.
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.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,
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.
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
addMouseListenerToHeaderInTable(JTable) - Method in class weka.gui.TableSorter
AddNoise - Class in weka.filters.unsupervised.attribute: Introduces noise data a random subsample of the dataset by changing a given attribute (attribute must be nominal) Valid options are:
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.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.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.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
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.
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.
addStartupListener(StartUpListener) - Static method in class weka.gui.beans.KnowledgeFlowApp: 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.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.arffviewer.ArffTableSorter: 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.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
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(BitSet, double) - Method in class weka.classifiers.rules.DecisionTable.LinkedList: Aadds an element (Link) to the list.
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.ArffTableModel: adds an undo point to the undo history, if the undo support is enabled
addUndoPoint() - Method in class weka.gui.arffviewer.ArffTableSorter: adds an undo point to the undo history
addUndoPoint() - Method in class weka.gui.explorer.PreprocessPanel: Backs up the current state of the dataset, so the changes can be undone.
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 interface weka.estimators.Estimator: Add a new data 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.
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.DataVisualizer: 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.
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
AIC - Static variable in interface weka.classifiers.bayes.net.search.local.Scoreable
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
AlgorithmListPanel.ObjectCellRenderer() - Constructor for class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
AllFilter - Class in weka.filters: A simple instance filter that passes all instances directly through.
AllFilter() - Constructor for class weka.filters.AllFilter
allowed() - Method in class weka.core.xml.PropertyHandler: returns an enumeration of the classnames for which only certain properties (display names) are allowed
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
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
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
applyCostMatrix(Instances, Random) - Method in class weka.classifiers.CostMatrix: Applies the cost matrix to a set of instances.
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.
AprioriItemSet - Class in weka.associations: Class for storing a set of items.
AprioriItemSet(int) - Constructor for class weka.associations.AprioriItemSet: Constructor
ArffLoader - Class in weka.core.converters: Reads a source that is in arff text format.
ArffLoader() - Constructor for class weka.core.converters.ArffLoader
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
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
ArffTableSorter - Class in weka.gui.arffviewer: A sorter for the ARFF-Viewer - necessary because of the custom CellRenderer.
ArffTableSorter(String) - Constructor for class weka.gui.arffviewer.ArffTableSorter: initializes the sorter w/o a model, but loads the given file and creates from that a model
ArffTableSorter(Instances) - Constructor for class weka.gui.arffviewer.ArffTableSorter: initializes the sorter w/o a model, but uses the given data to create a model from that
ArffTableSorter(TableModel) - Constructor for class weka.gui.arffviewer.ArffTableSorter: initializes the sorter with the given model
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(JFrame) - Constructor for class weka.gui.arffviewer.ArffViewerMainPanel: initializes the object
arrayLeftDivide(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Element-by-element left division, C = A.\B
arrayLeftDivide(Matrix) - Method in class weka.core.matrix.Matrix: Element-by-element left division, C = A.\B
arrayLeftDivideEquals(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Element-by-element left division in place, A = 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.classifiers.functions.pace.Matrix: Element-by-element right division, C = 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.classifiers.functions.pace.Matrix: Element-by-element right division in place, A = 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.classifiers.functions.pace.Matrix: Element-by-element multiplication, C = A.*B
arrayTimes(Matrix) - Method in class weka.core.matrix.Matrix: Element-by-element multiplication, C = A.*B
arrayTimesEquals(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Element-by-element multiplication in place, A = 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
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.lmt.LMTNode: Assigns numbers to the logistic regression models at the leaves of the tree
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 - Class in weka.associations: Abstract scheme for learning associations.
Associator() - Constructor for class weka.associations.Associator
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.XMLSerialization: the tag for the class
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.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.XMLSerialization: the version 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.
attIndexSetTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the tip text for this property
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(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.
AttributeEvaluator - Class in weka.attributeSelection: Abstract attribute evaluator.
AttributeEvaluator() - Constructor for class weka.attributeSelection.AttributeEvaluator
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
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.Add: 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.StringToNominal
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
attributeIndexTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: 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.NominalToBinary: 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.Remove: Returns the tip text for this property
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.
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.
attributeNameTipText() - Method in class weka.filters.unsupervised.attribute.Add: 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: 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
AttributeSelectedClassifier - Class in weka.classifiers.meta: Class for running an arbitrary classifier on data that has been reduced through attribute selection.
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: Filter for doing attribute selection.
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 - 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
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.
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.RemoveType: Returns the tip text for this property
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.
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 fo this property
AveragingResultProducer - Class in weka.experiment: AveragingResultProducer 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.
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
balancedTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
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.
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.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.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.AddNoise: 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.Normalize: 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.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.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
BayesNet - Class in weka.classifiers.bayes: Base class for a Bayes Network classifier.
BayesNet() - Constructor for class weka.classifiers.bayes.BayesNet
BayesNet - Static variable in interface weka.core.Drawable
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: BayesNetGenerator offers facilities for generating random Bayes networks and random instances based on a Bayes network.
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 excercise 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: Class for performing a best first search.
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
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
bias() - Method in class weka.classifiers.functions.SMO: Returns the bias of each binary SMO.
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
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.
BIFReader() - Constructor for class weka.classifiers.bayes.net.BIFReader
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
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
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:
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
BIRCHCluster - Class in weka.datagenerators: Cluster data generator designed for the BIRCH System Dataset is generated with instances in K clusters.
BIRCHCluster() - Constructor for class weka.datagenerators.BIRCHCluster
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
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.core.converters.DatabaseLoader
BOOL - Static variable in class weka.experiment.DatabaseUtils: Type mapping for BOOL used for reading experiment results
boost() - Method in class weka.classifiers.trees.ADTree: Performs a single boosting iteration, using two-class optimized method.
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.
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 class weka.associations.Associator: Generates an associator.
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.BayesNet: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: 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.NaiveBayesSimple: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.Classifier: Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.LeastMedSq: Build lms regression
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.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.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.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.Decorate: Build Decorate classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.FilteredClassifier: Build the classifier on the filtered data.
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.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.RegressionByDiscretization: Generates 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.misc.HyperPipes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.misc.VFI: Generates the classifier.
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.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.DecisionStump: Generates the classifier.
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.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.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.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.UserClassifier: Call this function to build a decision tree for the training data provided.
buildClusterer(Instances) - Method in class weka.clusterers.Clusterer: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.Cobweb: Builds the clusterer.
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.MakeDensityBasedClusterer: Builds a clusterer for a set of instances.
buildClusterer(Instances) - Method in class weka.clusterers.SimpleKMeans: Generates a clusterer.
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.
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.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.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
buildLogisticModelsTipText() - Method in class weka.classifiers.functions.SMO: 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.K2: 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.global.SimulatedAnnealing
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.K2: 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.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.SimulatedAnnealing
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, 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) - Method in class weka.classifiers.trees.lmt.LMTNode: Method for building the tree structure.
BVDecompose - Class in weka.classifiers: Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
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:
BVDecomposeSegCVSub() - Constructor for class weka.classifiers.BVDecomposeSegCVSub
BYTE - Static variable in class weka.core.converters.DatabaseLoader
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 C4.5 input files.
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.
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 in the format used by the C4.5 slgorithm.
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.
cacheKeyNameTipText() - Method in class weka.experiment.DatabaseResultListener: Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
cacheSizeTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
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.
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.
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
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.
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.
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.
calculateStdDevsTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
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.
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.ArffTableModel: returns whether an undo is possible, i.e.
canUndo() - Method in class weka.gui.arffviewer.ArffTableSorter: returns whether an undo is possible, i.e.
capacity() - Method in class weka.classifiers.functions.pace.DoubleVector: Gets the capacity of the vector.
capacity() - Method in class weka.classifiers.functions.pace.IntVector: Returns the capacity of the vector
capacity() - Method in class weka.core.FastVector: Returns the capacity of the vector.
cat(DoubleVector) - Method in class weka.classifiers.functions.pace.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.
CfsSubsetEval - Class in weka.attributeSelection: CFS attribute subset evaluator.
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
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
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).
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
check(double) - Method in class weka.classifiers.trees.j48.Distribution: Checks if at least two bags contain a minimum number of instances.
CheckClassifier - Class in weka.classifiers: Class for examining the capabilities and finding problems with classifiers.
CheckClassifier() - Constructor for class weka.classifiers.CheckClassifier
CheckClassifier.PostProcessor - Class in weka.classifiers: a class for postprocessing the test-data
CheckClassifier.PostProcessor() - Constructor for class weka.classifiers.CheckClassifier.PostProcessor
checkErrorRateTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
checkForMissing(Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if instances have a missing value.
checkForMissing(Instance, Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if an instance has a missing value.
checkForNonBinary(Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if any of the nominal attributes is non-binary.
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
checkInstance(Instance) - Method in class weka.core.Instances: Checks if the given instance is compatible with this dataset.
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.
checkModel() - Method in class weka.gui.TableSorter
CheckOptionHandler - Class in weka.core: Simple command line checking of classes that implement OptionHandler.
CheckOptionHandler() - Constructor for class weka.core.CheckOptionHandler
checkOptionHandler(OptionHandler, String[]) - Static method in class weka.core.CheckOptionHandler: Runs some diagnostic tests on an optionhandler object.
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
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.classifiers.functions.pace.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: Class for Evaluating attributes individually by measuring the chi-squared statistic with respect to the class.
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
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
classColumnTipText() - Method in class weka.gui.beans.ClassAssigner: Tool tip text for this property
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.
classificationTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
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: 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.
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.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.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.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.SimpleLinearRegression: Generate a prediction for the supplied instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.SMOreg: Classifies a given instance.
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.RegressionByDiscretization: Returns a predicted class for the test instance.
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.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.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() - 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.Tertius: 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.
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: A filter that sorts the order of classes so that the class values are no longer of in the order of that in the header file after filtered.
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
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.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.
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
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.
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.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.ProtectedProperties: Overrides a method to prevent the properties from being modified.
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.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
clearLayout() - Method in class weka.gui.beans.KnowledgeFlowApp
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.
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.ArffTableModel: removes the undo history
clearUndo() - Method in class weka.gui.arffviewer.ArffTableSorter: removes the undo history
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.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.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.DoubleVector: Clones the DoubleVector object.
clone() - Method in class weka.classifiers.functions.pace.IntVector: Clones the IntVector object.
clone() - Method in class weka.classifiers.functions.pace.Matrix: Clone the Matrix object.
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.Matrix: Deprecated. Creates and returns a clone of this object.
clone() - Method in class weka.core.matrix.Matrix: Clone the Matrix object.
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
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
Clusterer - Class in weka.clusterers: Abstract clusterer.
Clusterer() - Constructor for class weka.clusterers.Clusterer
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.filters.unsupervised.attribute.AddCluster: Returns the tip text for this property
clustererTipText() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns a description of this option suitable for display as a tip text in the gui.
ClusterEvaluation - Class in weka.clusterers: Class for evaluating clustering models.
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
clusteringSeedTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
clusterInstance(Instance) - Method in class 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.FarthestFirst: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.SimpleKMeans: Classifies a given instance.
ClusterMembership - Class in weka.filters.unsupervised.attribute: A filter that uses a clusterer to obtain cluster membership values for each input instance and outputs them as new instances.
ClusterMembership() - Constructor for class weka.filters.unsupervised.attribute.ClusterMembership
clusterPriors() - Method in class 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.
Cobweb - Class in weka.clusterers: Class implementing the Cobweb and Classit clustering algorithms.
Cobweb() - Constructor for class weka.clusterers.Cobweb
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.
coefficients() - Method in class weka.classifiers.functions.LinearRegression: Returns the coefficients for this linear 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.
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.
combinations(int, int) - Static method in class weka.classifiers.functions.LeastMedSq: Produces the combination nCr
combinedDL(double, double) - Method in class weka.classifiers.rules.RuleStats: Compute the combined DL of the ruleset in this class, i.e.
compactify() - Method in class weka.core.Instances: Compactifies the set of instances.
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.
compare(Object, Object) - Method in class weka.core.RTSI.StringCompare: Compares its two arguments for order.
compare(int, int) - Method in class weka.gui.TableSorter
compareOptions(String[], String[]) - Static method in class weka.core.CheckOptionHandler: Compares the two given sets of options.
compareRowsByColumn(int, int, int) - Method in class weka.gui.TableSorter
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.core.Version: checks the version of this class against the given version-string
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.
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
Compute - Interface in weka.experiment: Interface to something that can accept remote connections and execute a task.
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
confidenceForRule(AprioriItemSet, AprioriItemSet) - Static method in class weka.associations.AprioriItemSet: Outputs the confidence for a rule.
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.
ConjunctiveRule() - Constructor for class weka.classifiers.rules.ConjunctiveRule
connect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection: Connects two units together.
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(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.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.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(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
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 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 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.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.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.
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
ConsistencySubsetEval - Class in weka.attributeSelection: Consistency attribute subset evaluator.
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
CONST_AUTOMATIC_SHAPE - Static variable in class weka.gui.visualize.Plot2D
constructWithCopy(double[][]) - Static method in class weka.classifiers.functions.pace.Matrix: Construct a matrix from a copy of a 2-D array.
constructWithCopy(double[][]) - Static method in class weka.core.matrix.Matrix: Construct a matrix from a copy of a 2-D array.
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(Object) - Method in class weka.core.FastVector: added by akibriya
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(String) - Method in class weka.gui.HierarchyPropertyParser: Whether the HierarchyPropertyParser contains the given string
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
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
ConverterUtils - Class in weka.core.converters: Utility routines for the converter package.
ConverterUtils() - Constructor for class weka.core.converters.ConverterUtils
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.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
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.
convictionForRule(AprioriItemSet, AprioriItemSet, int, int) - Method in class weka.associations.AprioriItemSet: Outputs the conviction for a rule.
copy() - Method in class weka.associations.tertius.IndividualInstance
copy(ParentSet) - Method in class weka.classifiers.bayes.net.ParentSet: Copy makes current parents set equal to other parent set
copy() - Method in class weka.classifiers.functions.pace.DoubleVector: Makes a deep copy of the vector
copy() - Method in class weka.classifiers.functions.pace.IntVector: Makes a deep copy of the vector
copy() - Method in class weka.classifiers.functions.pace.Matrix: Make a deep copy of a matrix
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.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.
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
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(CostMatrix) - Constructor for class weka.classifiers.CostMatrix: Creates a cost matrix that is a copy of another.
CostMatrix(int) - Constructor for class weka.classifiers.CostMatrix: Creates a default cost matrix of a particular size.
CostMatrix(Reader) - Constructor for class weka.classifiers.CostMatrix: Creates a cost 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.classifiers.meta.CostSensitiveClassifier
costMatrixSourceTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
costMatrixTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
costMatrixTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
CostSensitiveClassifier - Class in weka.classifiers.meta: This metaclassifier makes its base classifier cost-sensitive.
CostSensitiveClassifier() - Constructor for class weka.classifiers.meta.CostSensitiveClassifier: Default constructor.
CostSensitiveClassifierSplitEvaluator - Class in weka.experiment: A SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
CostSensitiveClassifierSplitEvaluator() - Constructor for class weka.experiment.CostSensitiveClassifierSplitEvaluator
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
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.Rule: Whether the instance covered by this rule
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.
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
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) - 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 text file that is comma or tab delimited..
CSVLoader() - Constructor for class weka.core.converters.CSVLoader
CSVResultListener - Class in weka.experiment: CSVResultListener outputs the received results in csv format to a Writer
CSVResultListener() - Constructor for class weka.experiment.CSVResultListener: Sets temporary file.
CSVSaver - Class in weka.core.converters: Writes to a destination in csv format.
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
cumulate() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns a vector that stores the cumulated values of the original vector
cumulateInPlace() - Method in class weka.classifiers.functions.pace.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.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.
cutoffTipText() - Method in class weka.clusterers.Cobweb: Returns the tip text for this property
CVParameterSelection - Class in weka.classifiers.meta: Class for performing parameter selection by cross-validation for any classifier.
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

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.
DatabaseConverter - Interface in weka.core.converters: Marker interface for a loader/saver that uses a database
DatabaseLoader - Class in weka.core.converters: Reads from a database.
DatabaseLoader() - Constructor for class weka.core.converters.DatabaseLoader: Constructor
DatabaseResultListener - Class in weka.experiment: DatabaseResultListener takes the results from a ResultProducer and submits them to a central database.
DatabaseResultListener() - Constructor for class weka.experiment.DatabaseResultListener: Sets up the database drivers
DatabaseResultProducer - Class in weka.experiment: DatabaseResultProducer 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 - Interface in weka.gui.boundaryvisualizer: Interface to something that can generate new instances based on a set of input instances
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
DATASET_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
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.core.converters.DatabaseLoader
DATE - Static variable in class weka.experiment.DatabaseUtils: Type mapping for DATE used for reading experiment results
dateFormatTipText() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat
DbConnectionDialog(String, String) - Method in class weka.gui.DatabaseConnectionDialog: Display the database connection dialog
dchisq(double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the density of the Chi-squared distribution.
dchisq(double, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the density of the noncentral Chi-squared distribution.
dchisq(double, DoubleVector) - Static method in class weka.classifiers.functions.pace.Maths: Returns the density of the noncentral Chi-squared distribution.
dchisqLog(double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the log-density of the noncentral Chi-square distribution.
dchisqLog(double, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the log-density value of a noncentral Chi-square distribution.
dchisqLog(double, DoubleVector) - Static method in class weka.classifiers.functions.pace.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
debugTipText() - Method in class weka.attributeSelection.RaceSearch: 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.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.classifiers.trees.RandomTree: 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.unsupervised.attribute.AddExpression: Returns the tip text for this property
decayTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
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.
DecisionTable() - Constructor for class weka.classifiers.rules.DecisionTable: Constructor for a DecisionTable
DecisionTable.hashKey - Class in weka.classifiers.rules: Class providing keys to the hash table
DecisionTable.hashKey(Instance, int, boolean) - Constructor for class weka.classifiers.rules.DecisionTable.hashKey: Constructor for a hashKey
DecisionTable.hashKey(double[]) - Constructor for class weka.classifiers.rules.DecisionTable.hashKey: Constructor for a hashKey
DecisionTable.Link - Class in weka.classifiers.rules: Class for a node in a linked list.
DecisionTable.Link(BitSet, double) - Constructor for class weka.classifiers.rules.DecisionTable.Link: The constructor.
DecisionTable.LinkedList - Class in weka.classifiers.rules: Class for handling a linked list.
DecisionTable.LinkedList() - Constructor for class weka.classifiers.rules.DecisionTable.LinkedList
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.
DEFAULT_COLORS - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanel
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_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
defaultWeightTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
defineDataFormat() - Method in class weka.datagenerators.BIRCHCluster: Initializes the format for the dataset produced.
defineDataFormat() - Method in class weka.datagenerators.RDG1: Initializes the format for the dataset produced.
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.
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.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
deleteAttributeAt(int) - Method in class weka.gui.arffviewer.ArffTableSorter: 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.ArffTableModel: deletes the attributes at the given indices
deleteAttributes(int[]) - Method in class weka.gui.arffviewer.ArffTableSorter: deletes the attributes at the given indices
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.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
deleteInstanceAt(int) - Method in class weka.gui.arffviewer.ArffTableSorter: 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.ArffTableModel: deletes the instances at the given positions
deleteInstances(int[]) - Method in class weka.gui.arffviewer.ArffTableSorter: 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)
deleteParent(int, Instances) - Method in class weka.classifiers.bayes.net.ParentSet: delete node from parent set
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.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property
delRange(int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution: Deletes all instances in given range from given bag.
deltaTipText() - Method in class weka.associations.Apriori: 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
DensityBasedClusterer - Class in weka.clusterers: Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
DensityBasedClusterer() - Constructor for class weka.clusterers.DensityBasedClusterer
depth() - Method in class weka.gui.HierarchyPropertyParser: Get the depth of the tree, i.e.
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.
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
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.
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
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.
disconnect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection: Disconnects two units.
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 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 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.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.StripChart: 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.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
displayResultset(int) - Method in class weka.experiment.PairedTTester: 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
dispose() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
disposeSplash() - Static method in class weka.gui.SplashWindow: Closes the splash window.
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
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.BayesNet: 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.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.Classifier: Predicts the class memberships 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.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.Decorate: 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.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.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.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 classifier.
distributionForInstance(Instance) - Method in class weka.classifiers.misc.HyperPipes: Classifies 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.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.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.DecisionStump: Calculates the class membership probabilities for the given test instance.
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.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 decision 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.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.Clusterer: Predicts the cluster memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.clusterers.DensityBasedClusterer: Returns the cluster probability distribution for an instance.
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.classifiers.functions.pace.DoubleVector: Divided by another DoubleVector element by element
dividedByEquals(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Divided by another DoubleVector element by element in place
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
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.classifiers.functions.pace.Maths: Returns the density of the standard normal.
dnorm(double, double, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the density value of a standard normal.
dnorm(double, DoubleVector, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the density values of a set of normal distributions with different means.
dnormLog(double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the log-density of the standard normal.
dnormLog(double, double, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the log-density value of a standard normal.
dnormLog(double, DoubleVector, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the log-density values of a set of normal distributions with different means.
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
doHistory(KeyEvent) - Method in class weka.gui.SimpleCLI: 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.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.
dontNormalizeTipText() - Method in class weka.classifiers.lazy.LWL: 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.classifiers.CheckClassifier: Begin the tests, reporting results to System.out
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.core.converters.DatabaseLoader
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.classifiers.functions.pace
DoubleVector() - Constructor for class weka.classifiers.functions.pace.DoubleVector: Constructs a null vector.
DoubleVector(int) - Constructor for class weka.classifiers.functions.pace.DoubleVector: Constructs an n-vector of zeros.
DoubleVector(int, double) - Constructor for class weka.classifiers.functions.pace.DoubleVector: Constructs a constant n-vector.
DoubleVector(double[]) - Constructor for class weka.classifiers.functions.pace.DoubleVector: Constructs a vector directly from a double array
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
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).
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
editableProperties() - Method in class weka.gui.PropertySheetPanel: Gets the number of editable properties for the current target.
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
elementAt(int) - Method in class weka.core.FastVector: Returns the element at the given position.
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.
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() - 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.
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.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.rules.DecisionTable: 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.J48: 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 interface weka.core.AdditionalMeasureProducer: Returns an enumeration of the 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.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.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.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.Loader: Get a list 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 or a string, null otherwise.
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
epsilonTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
epsilonTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
epsTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
eq(double, double) - Static method in class weka.core.Utils: Tests if a is equal to b.
equalCondset(Object) - Method in class weka.associations.LabeledItemSet: Compares two item sets
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.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.rules.DecisionTable.hashKey: Tests if two instances are equal
equals(Object) - Method in class weka.core.Attribute: Tests if given attribute is equal to this attribute.
equals(Object) - Method in class weka.core.RTSI.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.Version: whether the given version string is equal to this version
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.
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_SHAPE - Static variable in class weka.gui.visualize.Plot2D
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.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.
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 - Interface in weka.estimators: Interface for probability estimators.
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
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: Redefines the eval function of PolyKernel.
EVAL_CROSS_VALIDATION - Static variable in class weka.classifiers.meta.ThresholdSelector
EVAL_TRAINING_SET - Static variable in class weka.classifiers.meta.ThresholdSelector
EVAL_TUNED_SPLIT - Static variable in class weka.classifiers.meta.ThresholdSelector
evaluateAttribute(int) - Method in class weka.attributeSelection.AttributeEvaluator: evaluates an individual attribute
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.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.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(Clusterer, String[]) - Static method in class weka.clusterers.ClusterEvaluation: Evaluates a clusterer with the options given in an array of strings.
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) - 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.
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, 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 class 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.
evaluationModeTipText() - Method in class weka.classifiers.meta.ThresholdSelector
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.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(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.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.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(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.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Perform the sub task
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.
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: Class for performing an exhaustive search.
ExhaustiveSearch() - Constructor for class weka.attributeSelection.ExhaustiveSearch: Constructor
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.
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: Ways of handling missing values.
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
ExponentialFormat - Class in weka.classifiers.functions.pace
ExponentialFormat() - Constructor for class weka.classifiers.functions.pace.ExponentialFormat
ExponentialFormat(int) - Constructor for class weka.classifiers.functions.pace.ExponentialFormat
ExponentialFormat(int, boolean) - Constructor for class weka.classifiers.functions.pace.ExponentialFormat
ExponentialFormat(int, int, boolean, boolean) - Constructor for class weka.classifiers.functions.pace.ExponentialFormat
exponentTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
exponentTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
exponentTipText() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the tip text for this property
expressionTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression: 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.

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
FALLOUT_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
FALSE_NEG_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
FALSE_POS_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
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: Implements the "Farthest First Traversal Algorithm" by Hochbaum and Shmoys 1985: A best possible heuristic for the k-center problem, Mathematics of Operations Research, 10(2):180-184, as cited by Sanjoy Dasgupta "performance guarantees for hierarchical clustering", colt 2002, sydney works as a fast simple approximate clusterer modelled after SimpleKMeans, might be a useful initializer for it Valid options are:
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.
featureSpaceNormalizationTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
featureSpaceNormalizationTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
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
FILE_EXTENSION - Static variable in class weka.core.converters.C45Loader
FILE_EXTENSION - Static variable in class weka.core.converters.CSVLoader
FILE_EXTENSION - Static variable in class weka.core.converters.SerializedInstancesLoader
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.experiment.Experiment: The filename extension that should be used for experiment files
FILE_EXTENSION - Static variable in class weka.gui.beans.KnowledgeFlowApp: the extension for the serialized setups (Java serialization)
FILE_EXTENSION_XML - Static variable in class weka.gui.beans.KnowledgeFlowApp: the extension for the serialized setups (Java serialization)
FileChooser - Class in weka.gui.arffviewer: This class fixes a bug with the Swing JFileChooser: if you entered a new filename in the save dialog and press Enter the getSelectedFile method returns null instead of the filename.
To solve this annoying behavior we call the save dialog once again s.t.
FileChooser() - Constructor for class weka.gui.arffviewer.FileChooser: default constructor, pointing to the user's default directory
FileChooser(String) - Constructor for class weka.gui.arffviewer.FileChooser: sets the default directory to the given one
FileChooser(File) - Constructor for class weka.gui.arffviewer.FileChooser: sets the default directory to the given one
FileEditor - Class in weka.gui: A PropertyEditor for File objects that lets the user select a file.
FileEditor() - Constructor for class weka.gui.FileEditor
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
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 - 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.SMO
FILTER_NONE - Static variable in class weka.classifiers.functions.SMOreg
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.SMO: The filter to apply to the training data
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.SMOreg: The filter to apply to the training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.SMO
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.SMOreg
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
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.
filterFile(Filter, String[]) - Static method in class weka.filters.Filter: Method for testing filters.
filterTipText() - Method in class weka.classifiers.meta.FilteredClassifier: 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.SMO: Returns the tip text for this property
filterTypeTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
finalize() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
find(String) - Static method in class weka.core.RTSI: Returns all the classes inheriting or implementing a given class in the currently loaded packages.
Note: If a package, containing subclasses, has not been loaded by the time of this method call, these classes won't be found! It's better to define the package name explicitly in which to look for subclasses, like in find(String,String).
find(String, String) - Static method in class weka.core.RTSI: Returns all the classes inheriting or implementing a given class in a given package.
find(String, Class) - Static method in class weka.core.RTSI: Return all the classes inheriting or implementing a given class in a given package.
findArgmin(double[], double[][]) - Method in class weka.core.Optimization: Main algorithm.
findBestLeaf(double[], RuleNode[]) - Method in class weka.classifiers.trees.m5.RuleNode: Find the leaf with greatest coverage
findCentralTendencies(double[]) - Method in class weka.classifiers.BVDecomposeSegCVSub: Finds the central tendency, given the classifications for an instance.
findInstance(Point) - Static method in class weka.gui.beans.BeanInstance: Looks for a bean (if any) whose bounds contain the supplied point
findInstances(Rectangle) - Static method in class weka.gui.beans.BeanInstance: Looks for all beans (if any) located within the supplied bounding box
findIntervall(double) - Method in class weka.associations.PriorEstimation: searches the mid point of the interval a given confidence value falls into
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns the tip text for this property
findReadMethod(Object, String) - Static method in class weka.core.xml.XMLSerializationMethodHandler: returns the method with the given name that has the same signature as readFromXML() of the XMLSerialiation class.
findWriteMethod(Object, String) - Static method in class weka.core.xml.XMLSerializationMethodHandler: returns the method with the given name that has the same signature as writeToXML() of the XMLSerialiation class.
finished() - Method in class weka.experiment.OutputZipper: Closes the zip file.
FINISHED - Static variable in class weka.experiment.TaskStatusInfo
finished() - Method in class weka.gui.visualize.PostscriptGraphics: Finalizes output file.
fireLayoutCompleteEvent(LayoutCompleteEvent) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Fires a LayoutCompleteEvent.
fireLayoutCompleteEvent(LayoutCompleteEvent) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This fires a LayoutCompleteEvent once a layout has been completed.
firstElement() - Method in class weka.core.FastVector: Returns the first element of the vector.
firstInstance() - Method in class weka.core.Instances: Returns the first instance in the set.
FirstOrder - Class in weka.filters.unsupervised.attribute: This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
FirstOrder() - Constructor for class weka.filters.unsupervised.attribute.FirstOrder
firstValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
firstValueTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
fit(DoubleVector) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Fits the mixture (or mixing) distribution to the data.
fit(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Fits the mixture (or mixing) distribution to the data.
fitForSingleCluster(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Fits the mixture (or mixing) distribution to the data.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Contructs the set of fitting intervals for mixture estimation.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Contructs the set of fitting intervals for mixture estimation.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Contructs the set of fitting intervals for mixture estimation.
fitToScreen() - Method in class weka.gui.treevisualizer.TreeVisualizer: Fits the tree to the current screen size.
FlexibleDecimalFormat - Class in weka.classifiers.functions.pace
FlexibleDecimalFormat() - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
FlexibleDecimalFormat(int) - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
FlexibleDecimalFormat(int, boolean) - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
FlexibleDecimalFormat(int, boolean, boolean, boolean) - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
FlexibleDecimalFormat(double) - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
FLOAT - Static variable in class weka.core.converters.DatabaseLoader
FLOAT - Static variable in class weka.experiment.DatabaseUtils: Type mapping for FLOAT used for reading experiment results
FloatingPointFormat - Class in weka.classifiers.functions.pace: Class for the format of floating point numbers
FloatingPointFormat() - Constructor for class weka.classifiers.functions.pace.FloatingPointFormat: Default constructor
FloatingPointFormat(int) - Constructor for class weka.classifiers.functions.pace.FloatingPointFormat
FloatingPointFormat(int, int) - Constructor for class weka.classifiers.functions.pace.FloatingPointFormat
FloatingPointFormat(int, int, boolean) - Constructor for class weka.classifiers.functions.pace.FloatingPointFormat
FLOOR - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
FLOOR1 - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
fMeasure(int) - Method in class weka.classifiers.Evaluation: Calculate the F-Measure with respect to a particular class.
FMEASURE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
FOLD_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
foldsTipText() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string for this option suitable for display in the gui as a tip text
foldsTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
foldsTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
foldsTipText() - Method in class weka.gui.beans.CrossValidationFoldMaker: Tip text for this property
foldTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
foldTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.ExponentialFormat
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.FloatingPointFormat
FORMAT_AVAILABLE - Static variable in class weka.gui.beans.InstanceEvent
FORMAT_AVAILABLE - Static variable in class weka.gui.streams.InstanceEvent: Specifies that the instance format is available
formatDate(double) - Method in class weka.core.Attribute
formatString(String) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
forName(String, String[]) - Static method in class weka.associations.Associator: Creates a new instance of a associator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.attributeSelection.ASEvaluation: Creates a new instance of an attribute/subset evaluator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.attributeSelection.ASSearch: Creates a new instance of a search class given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.classifiers.Classifier: Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.clusterers.Clusterer: Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method.
forName(Class, String, String[]) - Static method in class weka.core.Utils: Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forward(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Forward ordering of columns in terms of response explanation.
foundUsefulAttribute() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns true if a usable attribute was found.
FP_RATE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
FProbability(double, int, int) - Static method in class weka.core.Statistics: Computes probability of F-ratio.
freeNotCoveredInstances() - Method in class weka.classifiers.trees.m5.Rule: Free up memory consumed by the set of instances not covered by this rule.
FREQ_ASCEND - Static variable in class weka.filters.supervised.attribute.ClassOrder: The class values are sorted in ascending order based on their frequencies
FREQ_DESCEND - Static variable in class weka.filters.supervised.attribute.ClassOrder: The class values are sorted in descending order based on their frequencies
frequencyThresholdTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
FromFile - Class in weka.classifiers.bayes.net.search.fixed: The FromFile reads the structure of a Bayes net from a file in BIFF format.
FromFile() - Constructor for class weka.classifiers.bayes.net.search.fixed.FromFile
fromXML(Document) - Method in class weka.core.xml.XMLSerialization: returns the given DOM document as an instance of the specified class
fullValue() - Method in class weka.gui.HierarchyPropertyParser: The full value of the current node, i.e.

G

g1(double, double) - Method in class weka.classifiers.functions.pace.PaceMatrix: Constructs the Givens rotation
g2(double[], int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
gainRatio() - Method in class weka.classifiers.trees.j48.BinC45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio() - Method in class weka.classifiers.trees.j48.C45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio(double[][]) - Static method in class weka.core.ContingencyTables: Computes gain ratio for contingency table (split on rows).
GainRatioAttributeEval - Class in weka.attributeSelection: Class for Evaluating attributes individually by measuring gain ratio with respect to the class.
GainRatioAttributeEval() - Constructor for class weka.attributeSelection.GainRatioAttributeEval: Constructor
GainRatioSplitCrit - Class in weka.classifiers.trees.j48: Class for computing the gain ratio for a given distribution.
GainRatioSplitCrit() - Constructor for class weka.classifiers.trees.j48.GainRatioSplitCrit
gamma(double) - Static method in class weka.core.Statistics: Returns the Gamma function of the argument.
gammaTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
gammaTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
GAUSSIAN - Static variable in class weka.filters.unsupervised.attribute.RandomProjection: The types of distributions that can be used for calculating the random matrix
generateDistribution() - Method in class weka.associations.PriorEstimation: Calculates the prior distribution.
generateExample() - Method in class weka.datagenerators.BIRCHCluster: Generate an example of the dataset.
generateExample() - Method in class weka.datagenerators.RDG1: Generate an example of the dataset dataset.
generateExamples() - Method in class weka.datagenerators.BIRCHCluster: Generate all examples of the dataset.
generateExamples(Random, Instances) - Method in class weka.datagenerators.BIRCHCluster: Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.RDG1: Generate all examples of the dataset.
generateExamples(int, Random, Instances) - Method in class weka.datagenerators.RDG1: Generate all examples of the dataset.
generateFinished() - Method in class weka.datagenerators.BIRCHCluster: Compiles documentation about the data generation after the generation process
generateFinished() - Method in class weka.datagenerators.RDG1: Compiles documentation about the data generation.
generateInstances() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
generateInstances(int[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Generate an instance.
generateInstances(int[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Generates a new instance using one kernel estimator.
generateRandomNetwork() - Method in class weka.classifiers.bayes.net.BayesNetGenerator
generateRandomNetworkStructure(int, int) - Method in class weka.classifiers.bayes.net.BayesNetGenerator
generateRankingTipText() - Method in class weka.attributeSelection.GreedyStepwise: Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
generateRuleItem(ItemSet, ItemSet, Instances, int, int, double[], Hashtable) - Method in class weka.associations.RuleItem: Constructs a new RuleItem if the support of the given rule is above the support threshold.
generateRules(double, FastVector, int) - Method in class weka.associations.AprioriItemSet: Generates all rules for an item set.
generateRules(double, boolean) - Method in class weka.associations.LabeledItemSet: Generates rules out of item sets
generateRules(int, double[], Hashtable, double, Instances, TreeSet, int) - Method in class weka.associations.RuleGeneration: Generates all rules for an item set.
generateRulesBruteForce(double, int, FastVector, int, int, double) - Method in class weka.associations.AprioriItemSet: Generates all significant rules for an item set.
generateStart() - Method in class weka.datagenerators.BIRCHCluster: Compiles documentation about the data generation before the generation process
Generator - Class in weka.datagenerators: Abstract class for data generators.
Generator() - Constructor for class weka.datagenerators.Generator
GeneratorPropertyIteratorPanel - Class in weka.gui.experiment: This panel controls setting a list of values for an arbitrary resultgenerator property for an experiment to iterate over.
GeneratorPropertyIteratorPanel() - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel: Creates the property iterator panel initially disabled.
GeneratorPropertyIteratorPanel(Experiment) - Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel: Creates the property iterator panel and sets the experiment.
GenericArrayEditor - Class in weka.gui: A PropertyEditor for arrays of objects that themselves have property editors.
GenericArrayEditor() - Constructor for class weka.gui.GenericArrayEditor: Sets up the array editor.
GenericObjectEditor - Class in weka.gui: A PropertyEditor for objects.
GenericObjectEditor() - Constructor for class weka.gui.GenericObjectEditor: Default constructor.
GenericObjectEditor(boolean) - Constructor for class weka.gui.GenericObjectEditor: Constructor that allows specifying whether it is possible to change the class within the editor dialog.
GenericObjectEditor.GOEPanel - Class in weka.gui: Handles the GUI side of editing values.
GenericObjectEditor.GOEPanel() - Constructor for class weka.gui.GenericObjectEditor.GOEPanel: Creates the GUI editor component
GenericObjectEditor.JTreePopupMenu - Class in weka.gui: Creates a popup menu containing a tree that is aware of the screen dimensions.
GenericObjectEditor.JTreePopupMenu(JTree) - Constructor for class weka.gui.GenericObjectEditor.JTreePopupMenu: Constructs a new popup menu.
GenericPropertiesCreator - Class in weka.gui: This class can generate the properties object that is normally loaded from the GenericObjectEditor.props file (= PROPERTY_FILE).
GenericPropertiesCreator() - Constructor for class weka.gui.GenericPropertiesCreator: initializes the creator, locates the props file with the Utils class.
GenericPropertiesCreator(String) - Constructor for class weka.gui.GenericPropertiesCreator: initializes the creator, the given file overrides the props-file search of the Utils class
GeneticSearch - Class in weka.attributeSelection: Class for performing a genetic based search.
GeneticSearch() - Constructor for class weka.attributeSelection.GeneticSearch: Constructor.
GeneticSearch - Class in weka.classifiers.bayes.net.search.global: GeneticSearch is a crude implementation of genetic search for learning Bayesian network structures.
GeneticSearch() - Constructor for class weka.classifiers.bayes.net.search.global.GeneticSearch
GeneticSearch - Class in weka.classifiers.bayes.net.search.local: GeneticSearch is a crude implementation of genetic search for learning Bayesian network structures.
GeneticSearch() - Constructor for class weka.classifiers.bayes.net.search.local.GeneticSearch
get(int) - Method in class weka.classifiers.functions.pace.DoubleVector: Gets a single element.
get(int) - Method in class weka.classifiers.functions.pace.IntVector: Gets the value of an element.
get(int, int) - Method in class weka.classifiers.functions.pace.Matrix: Get a single element.
get(int, int) - Method in class weka.core.matrix.Matrix: Get a single element.
get(String) - Method in class weka.core.xml.MethodHandler: returns the stored method for the given property
get(Class) - Method in class weka.core.xml.MethodHandler: returns the stored method for the given class
get(String, String) - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the value for the specified property, if non-existent then the default value.
getAboutPanel() - Method in class weka.gui.PropertySheetPanel: Return the panel containing global info and help for the object being edited.
getAcuity() - Method in class weka.clusterers.Cobweb: get the acuity value
getAdjustWeights() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns true if instance weights will be adjusted to maintain total weight per class.
getADTree() - Method in class weka.classifiers.bayes.BayesNet: get ADTree strucrture containing efficient representation of counts.
getAdvanceDataSetFirst() - Method in class weka.experiment.Experiment: Get the value of m_DataSetFirstFirst.
getAlpha() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Get prior used in probability table estimation
getAlpha() - Method in class weka.classifiers.functions.Winnow: Get the value of Alpha.
getAnimatedIcon() - Method in class weka.gui.beans.BeanVisual: Returns the animated icon
getAnimatedIconPath() - Method in class weka.gui.beans.BeanVisual: returns the path for the animated icon
getAppendPredictedProbabilities() - Method in class weka.gui.beans.PredictionAppender: Return true if predicted probabilities are to be appended rather than class value
getArffFile() - Method in class weka.gui.streams.InstanceLoader
getArffFile() - Method in class weka.gui.streams.InstanceSavePanel
getArray() - Method in class weka.classifiers.functions.pace.Matrix: Access the internal two-dimensional array.
getArray() - Method in class weka.core.matrix.Matrix: Access the internal two-dimensional array.
getArrayClass(Class) - Static method in class weka.core.Utils: Returns the basic class of an array class (handles multi-dimensional arrays).
getArrayCopy() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns a copy of the DoubleVector usng a double array.
getArrayCopy() - Method in class weka.classifiers.functions.pace.IntVector: Returns a copy of the internal one-dimensional array.
getArrayCopy() - Method in class weka.classifiers.functions.pace.Matrix: Copy the internal two-dimensional array.
getArrayCopy() - Method in class weka.core.matrix.Matrix: Copy the internal two-dimensional array.
getArrayDimensions(Class) - Static method in class weka.core.Utils: Returns the dimensions of the given array.
getArrayDimensions(Object) - Static method in class weka.core.Utils: Returns the dimensions of the given array.
getArtificialSize() - Method in class weka.classifiers.meta.Decorate: Factor that determines number of artificial examples to generate.
getAssociatedConnections() - Method in class weka.gui.beans.MetaBean
getAsText() - Method in class weka.gui.CostMatrixEditor: Some objects can be represented as text, but a cost matrix cannot.
getAsText() - Method in class weka.gui.GenericArrayEditor: Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.SelectedTagEditor: Gets the current value as text.
getAsText() - Method in class weka.gui.SimpleDateFormatEditor: Returns the date format string.
getAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Attribute Indexes array
getAttList_Irr() - Method in class weka.datagenerators.RDG1: Gets the array that defines which of the attributes are seen to be irrelevant.
getAttribute1() - Method in class weka.gui.visualize.VisualizePanelEvent
getAttribute2() - Method in class weka.gui.visualize.VisualizePanelEvent
getAttributeAt(int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the attribute at the given index, can be NULL if not an attribute column
getAttributeAt(int) - Method in class weka.gui.arffviewer.ArffTableSorter: returns the attribute at the given index, can be NULL if not an attribute column
getAttributeColumn(String) - Method in class weka.gui.arffviewer.ArffTableModel: returns the column of the given attribute name, -1 if not found
getAttributeColumn(String) - Method in class weka.gui.arffviewer.ArffTableSorter: returns the column of the given attribute name, -1 if not found
getAttributeEvaluator() - Method in class weka.attributeSelection.RaceSearch: Get the attribute evaluator used to generate the ranking.
getAttributeEvaluator() - Method in class weka.attributeSelection.RankSearch: Get the attribute evaluator used to generate the ranking.
getAttributeID() - Method in class weka.experiment.ClassifierSplitEvaluator: Get the index of Attibute Identifying the instances
getAttributeIndex() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns the index of the attribute used in the regression.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.Add: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.AddNoise: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Gets the index of the attribute converted.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the index of the attribute used.
getAttributeIndices() - Method in class weka.filters.supervised.attribute.Discretize: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Copy: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Remove: Get the current range selection.
getAttributeMax(int) - Method in class weka.classifiers.lazy.IBk: Get an attributes maximum observed value
getAttributeMin(int) - Method in class weka.classifiers.lazy.IBk: Get an attributes minimum observed value
getAttributeName() - Method in class weka.filters.unsupervised.attribute.Add: Get the name of the attribute to be created
getAttributeNamePrefix() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the attribute name prefix.
getAttributes() - Method in class weka.gui.arffviewer.ArffPanel: returns a list with the attributes
getAttributeSelectionMethod() - Method in class weka.classifiers.functions.LinearRegression: Gets the method used to select attributes for use in the linear regression.
getAttributeType() - Method in class weka.filters.unsupervised.attribute.RemoveType: Gets the attribute type to be deleted by the filter.
getAttsToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval: Get the constant rate of attribute elimination per iteration
getAutoBuild() - Method in class weka.classifiers.functions.MultilayerPerceptron
getAutoKeyGeneration() - Method in class weka.core.converters.DatabaseSaver: Gets whether or not a primary key will be generated automatically
getBackground() - Method in class weka.gui.visualize.JPEGWriter: returns the current background color
getBackground() - Method in class weka.gui.visualize.PostscriptGraphics
getBackup() - Method in class weka.gui.GenericObjectEditor: Returns the backup object (may be null if there is no backup).
getBagSizePercent() - Method in class weka.classifiers.meta.Bagging: Gets the size of each bag, as a percentage of the training set size.
getBagSizePercent() - Method in class weka.classifiers.meta.MetaCost: Gets the size of each bag, as a percentage of the training set size.
getBalanced() - Method in class weka.classifiers.functions.Winnow: Get the value of Balanced.
getBaseExperiment() - Method in class weka.experiment.RemoteExperiment: Get the base experiment used by this remote experiment
getBean() - Method in class weka.gui.beans.BeanInstance: Gets the bean encapsulated in this instance
getBeanContext() - Method in class weka.gui.beans.AbstractDataSource: Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.DataVisualizer: Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.ModelPerformanceChart: Return the bean context (if any) that this bean is embedded in
getBeanContext() - Method in class weka.gui.beans.TextViewer: Return the bean context (if any) that this bean is embedded in
getBeanDescriptor() - Method in class weka.gui.beans.ClassAssignerBeanInfo
getBeanDescriptor() - Method in class weka.gui.beans.ClassifierBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.ClassValuePickerBeanInfo
getBeanDescriptor() - Method in class weka.gui.beans.ClustererBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo: Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.FilterBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.LoaderBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.PredictionAppenderBeanInfo: Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.SaverBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.StripChartBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo: Get the bean descriptor for this bean
getBeanInfoInputs() - Method in class weka.gui.beans.MetaBean
getBeanInfoOutputs() - Method in class weka.gui.beans.MetaBean
getBeanInfoSubFlow() - Method in class weka.gui.beans.MetaBean
getBeanInstances() - Static method in class weka.gui.beans.BeanInstance: Return the list of displayed beans
getBeansInInputs() - Method in class weka.gui.beans.MetaBean: Return all the beans in the inputs
getBeansInOutputs() - Method in class weka.gui.beans.MetaBean: Return all the beans in the outputs
getBeansInSubFlow() - Method in class weka.gui.beans.MetaBean: Return all the beans in the sub flow
getBestCommitteeChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the best committee chunk size
getBestCommitteeErrorEstimate() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the best committee's error on the validation data
getBestCommitteeLLEstimate() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the best committee's log likelihood on the validation data
getBestCommitteeSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the number of members in the best committee
getBeta() - Method in class weka.classifiers.functions.Winnow: Get the value of Beta.
getBias() - Method in class weka.classifiers.BVDecompose: Get the calculated bias squared
getBias() - Method in class weka.classifiers.misc.VFI: Get the value of the bias parameter
getBiasToUniformClass() - Method in class weka.filters.supervised.instance.Resample: Gets the bias towards a uniform class.
getBIFFile() - Method in class weka.classifiers.bayes.BayesNet: Get name of network in BIF file to compare with
getBIFFile() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Get name of network in BIF file to read structure from
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: get whether numeric attributes are just being binarized.
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.InfoGainAttributeEval: get whether numeric attributes are just being binarized.
getBinaryAttributesNominal() - Method in class weka.filters.supervised.attribute.NominalToBinary: Gets if binary attributes are to be treated as nominal ones.
getBinaryAttributesNominal() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets if binary attributes are to be treated as nominal ones.
getBinarySplits() - Method in class weka.classifiers.rules.PART: Get the value of binarySplits.
getBinarySplits() - Method in class weka.classifiers.trees.J48: Get the value of binarySplits.
getBins() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the number of bins numeric attributes will be divided into
getBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Ignored
getBuilder() - Method in class weka.core.xml.XMLDocument: returns the DocumentBuilder
getBuildLogisticModels() - Method in class weka.classifiers.functions.SMO: Get the value of buildLogisticModels.
getBuildRegressionTree() - Method in class weka.classifiers.trees.m5.M5Base: Get the value of regressionTree.
getC() - Method in class weka.classifiers.functions.SMO: Get the value of C.
getC() - Method in class weka.classifiers.functions.SMOreg: Get the value of C.
getCacheKeyName() - Method in class weka.experiment.DatabaseResultListener: Get the value of CacheKeyName.
getCacheSize() - Method in class weka.classifiers.functions.SMO: Get the size of the kernel cache
getCacheSize() - Method in class weka.classifiers.functions.SMOreg: Get the size of the kernel cache
getCacheValues(double) - Method in class weka.classifiers.lazy.kstar.KStarCache: Returns the values in the cache mapped by the specified key
getCalcOutOfBag() - Method in class weka.classifiers.meta.Bagging: Get whether the out of bag error is calculated.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.GreedyStepwise: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.RaceSearch: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.Ranker: Gets the calculated number to select.
getCalculateStdDevs() - Method in class weka.experiment.AveragingResultProducer: Get the value of CalculateStdDevs.
getCardinality(int) - Method in class weka.classifiers.bayes.BayesNet: get number of values a node can take
getCardinalityOfParents() - Method in class weka.classifiers.bayes.net.ParentSet: returns cardinality of parents
getCenter() - Method in class weka.gui.treevisualizer.Node: Get the value of center.
getChangeInWeights() - Method in class weka.classifiers.functions.neural.NeuralNode: call this function to get the chnage in weights array.
getCheckErrorRate() - Method in class weka.classifiers.rules.JRip
getChild(int) - Method in class weka.gui.treevisualizer.Node: Get the Edge for the child number 'i'.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.Splitter: Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the child for a branch of the split.
getChildren() - Method in class weka.classifiers.trees.adtree.PredictionNode: Gets the children of this node.
getChildTags(Node) - Static method in class weka.core.xml.XMLDocument: returns all non tag-children from the given node
getChooseClassPopupMenu() - Method in class weka.gui.GenericObjectEditor: Returns a popup menu that allows the user to change the class of object.
getCindex() - Method in class weka.gui.visualize.PlotData2D: Get the currently set colouring index of the data
getCIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute selected for coloring
getClassColumn() - Method in class weka.gui.beans.ClassAssigner
getClassCounts() - Method in class weka.filters.supervised.attribute.ClassOrder: Get the class distribution of the sorted class values.
getClassesToClusters() - Method in class weka.clusterers.ClusterEvaluation: Return the array (ordered by cluster number) of minimum error class to cluster mappings
getClassFlag() - Method in class weka.datagenerators.ClusterGenerator: Gets the class flag.
getClassForIRStatistics() - Method in class weka.experiment.ClassifierSplitEvaluator: Get the value of ClassForIRStatistics.
getClassification() - Method in class weka.associations.Tertius: Get the value of classification.
getClassifier() - Method in class weka.attributeSelection.ClassifierSubsetEval: Get the classifier used as the base learner.
getClassifier() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the classifier used as the base learner.
getClassifier() - Method in class weka.classifiers.BVDecompose: Gets the name of the classifier being analysed
getClassifier() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets the name of the classifier being analysed
getClassifier() - Method in class weka.classifiers.CheckClassifier: Get the classifier used as the classifier
getClassifier(int) - Method in class weka.classifiers.meta.MultiScheme: Gets a single classifier from the set of available classifiers.
getClassifier(int) - Method in class weka.classifiers.MultipleClassifiersCombiner: Gets a single classifier from the set of available classifiers.
getClassifier() - Method in class weka.classifiers.SingleClassifierEnhancer: Get the classifier used as the base learner.
getClassifier() - Method in class weka.experiment.ClassifierSplitEvaluator: Get the value of Classifier.
getClassifier() - Method in class weka.experiment.RegressionSplitEvaluator: Get the value of Classifier.
getClassifier() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the classifier used by the filter.
getClassifier() - Method in class weka.gui.beans.BatchClassifierEvent: Get the classifier
getClassifier() - Method in class weka.gui.beans.Classifier: Get the classifier currently set for this wrapper
getClassifier() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the classifier
getClassifiers() - Method in class weka.classifiers.meta.MultiScheme: Gets the list of possible classifers to choose from.
getClassifiers() - Method in class weka.classifiers.MultipleClassifiersCombiner: Gets the list of possible classifers to choose from.
getClassifyIterations() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets the number of times an instance is classified
getClassIndex() - Method in class weka.associations.Tertius: Get the value of classIndex.
getClassIndex() - Method in class weka.classifiers.BVDecompose: Get the index (starting from 1) of the attribute used as the class.
getClassIndex() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the index (starting from 1) of the attribute used as the class.
getClassIndex() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the attribute on which misclassifications are based.
getClassName() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the class containing the transformation method.
getClassOrder() - Method in class weka.filters.supervised.attribute.ClassOrder: Get the wanted class order
getClassValueIndex() - Method in class weka.gui.beans.ClassValuePicker: Gets the class value index considered to be the "positive" class value.
getClearEachDataset() - Method in class weka.gui.streams.InstanceViewer
getClip() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
getClipBounds() - Method in class weka.gui.visualize.PostscriptGraphics: This returns the full current drawing area
getClipBounds(Rectangle) - Method in class weka.gui.visualize.PostscriptGraphics: This returns the full current drawing area
getClipRect() - Method in class weka.gui.visualize.PostscriptGraphics: Not implemented
getClosestConnections(Point, int) - Static method in class weka.gui.beans.BeanConnection: Return a list of connections within some delta of a point
getClosestConnectorPoint(Point) - Method in class weka.gui.beans.BeanVisual: Returns the coordinates of the closest "connector" point to the supplied point.
getClusterAssignments() - Method in class weka.clusterers.ClusterEvaluation: Return an array of cluster assignments corresponding to the most recent set of instances clustered.
getClusterCentroids() - Method in class weka.clusterers.SimpleKMeans
getClusterer() - Method in class weka.clusterers.MakeDensityBasedClusterer: Gets the clusterer being wrapped.
getClusterer() - Method in class weka.filters.unsupervised.attribute.AddCluster: Gets the clusterer used by the filter.
getClusterer() - Method in class weka.gui.beans.BatchClustererEvent: Get the clusterer
getClusterer() - Method in class weka.gui.beans.Clusterer: Get the clusterer currently set for this wrapper
getClusteringSeed() - Method in class weka.classifiers.functions.RBFNetwork: Get the random seed used by K-means.
getClusterModelsNumericAtts() - Method in class weka.clusterers.EM: Return the normal distributions for the cluster models
getClusterNominalCounts() - Method in class weka.clusterers.SimpleKMeans
getClusterPriors() - Method in class weka.clusterers.EM: Return the priors for the clusters
getClusterSizes() - Method in class weka.clusterers.SimpleKMeans
getClusterStandardDevs() - Method in class weka.clusterers.SimpleKMeans
getCoefficients() - Method in class weka.core.matrix.LinearRegression: returns the calculated coefficients
getColor() - Method in class weka.gui.treevisualizer.Node: Get the value of color.
getColor() - Method in class weka.gui.visualize.PostscriptGraphics: Get current pen color.
getColorBox() - Method in class weka.gui.AttributeVisualizationPanel: Returns the class selection combo box if the parent component wants to place it in itself or in some component other than this component.
getColoringIndex() - Method in class weka.gui.AttributeVisualizationPanel: Get the coloring (class) index for the plot
getColoringIndex() - Method in class weka.gui.beans.AttributeSummarizer: Return the coloring index for the attribute summary plots
getColors() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the current vector of Color objects used for the classes
getColumn(int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Return a DoubleVector that stores a column of the matrix
getColumn(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Return a DoubleVector that stores some elements of a column of the matrix
getColumn(int) - Method in class weka.core.Matrix: Deprecated. Gets a column of the matrix and returns it as a double array.
getColumn() - Static method in class weka.gui.experiment.ExperimenterDefaults: the comma-separated list of attribute names that identify a column
getColumnClass(int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the most specific superclass for all the cell values in the column (always String)
getColumnClass(int) - Method in class weka.gui.TableMap
getColumnCount() - Method in class weka.gui.arffviewer.ArffTableModel: returns the number of columns in the model
getColumnCount() - Method in class weka.gui.TableMap
getColumnDimension() - Method in class weka.classifiers.functions.pace.Matrix: Get column dimension.
getColumnDimension() - Method in class weka.core.matrix.Matrix: Get column dimension.
getColumnName(int) - Method in class weka.gui.arffviewer.ArffTableModel: returns the name of the column at columnIndex
getColumnName(int) - Method in class weka.gui.TableMap
getColumnPackedCopy() - Method in class weka.classifiers.functions.pace.Matrix: Make a one-dimensional column packed copy of the internal array.
getColumnPackedCopy() - Method in class weka.core.matrix.Matrix: Make a one-dimensional column packed copy of the internal array.
getCommand() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
getComparisonField() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the name of the field used for comparison
getCompatibilityState() - Method in class weka.experiment.AveragingResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.CrossValidationResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.DatabaseResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.LearningRateResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.RandomSplitResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in interface weka.experiment.ResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getComplexityParameter() - Method in class weka.attributeSelection.SVMAttributeEval: Get the value of C used with SMO
getComponent() - Method in class weka.gui.visualize.JComponentWriter: returns the component that is stored in the output format
getComponent() - Method in class weka.gui.visualize.PrintableComponent: returns the GUI component this print dialog is part of
getComposite() - Method in class weka.gui.visualize.PostscriptGraphics
getConfidenceFactor() - Method in class weka.classifiers.rules.PART: Get the value of CF.
getConfidenceFactor() - Method in class weka.classifiers.trees.J48: Get the value of CF.
getConfirmation() - Method in class weka.associations.tertius.Rule: Get the confirmation value of this rule.
getConfirmationThreshold() - Method in class weka.associations.Tertius: Get the value of confirmationThreshold.
getConfirmationValues() - Method in class weka.associations.Tertius: Get the value of confirmationValues.
getConfirmExit() - Method in class weka.gui.arffviewer.ArffViewer: returns the setting of whether to display a confirm messagebox or not on exit
getConfirmExit() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the setting of whether to display a confirm messagebox or not on exit
getConfusionMatrix() - Method in class weka.classifiers.evaluation.TwoClassStats: Generates a ConfusionMatrix representing the current two-class statistics, using class names "negative" and "positive".
getConnectedFormat() - Method in class weka.gui.beans.ClassAssigner: Returns the structure of the incoming instances (if any)
getConnectedFormat() - Method in class weka.gui.beans.ClassValuePicker: Returns the structure of the incoming instances (if any)
getConnections() - Static method in class weka.gui.beans.BeanConnection: Returns the list of connections
getConnectorPoint(int) - Method in class weka.gui.beans.BeanVisual: Returns the coordinates of the connector point given a compass point
getConsequent() - Method in class weka.classifiers.rules.Rule: Get the consequent of this rule, i.e.
getContent(Element) - Method in class weka.classifiers.bayes.net.BIFReader: Returns all TEXT children of the given node in one string.
getContent(Element) - Static method in class weka.core.xml.XMLDocument: returns the text between the opening and closing tag of a node (performs a trim() on the result)
getControlPanel() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: This method returns a handle to the extra controls panel, so that the visualizing class can add it to some of it's own gui panel.
getControlPanel() - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method returns the extra controls panel for the LayoutEngine, if there is any.
getConvertNominal() - Method in class weka.classifiers.trees.LMT: Get the value of convertNominal.
getCostMatrix() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the misclassification cost matrix.
getCostMatrix() - Method in class weka.classifiers.meta.MetaCost: Gets the misclassification cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the source location method of the cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.MetaCost: Gets the source location method of the cost matrix.
getCount(double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Get a counts for a value
getCount(double) - Method in class weka.estimators.DiscreteEstimator: Get the count for a value
getCount(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the number of visible nodes there are (this may accidentally count some of the invis nodes).
getCounterInstancesFrequency() - Method in class weka.associations.tertius.LiteralSet: Get the frequency of counter-instances of this LiteralSet in the data.
getCounterInstancesNumber() - Method in class weka.associations.tertius.LiteralSet: Get the number of counter-instances of this LiteralSet.
getCounts(int[], int[], int[], int, int, boolean) - Method in class weka.classifiers.bayes.net.ADNode: get counts for specific instantiation of a set of nodes
getCounts(int[], int[], int[], int, int, ADNode, boolean) - Method in class weka.classifiers.bayes.net.VaryNode: get counts for specific instantiation of a set of nodes
getCrossoverProb() - Method in class weka.attributeSelection.GeneticSearch: get the probability of crossover
getCrossVal() - Method in class weka.classifiers.rules.DecisionTable: Gets the number of folds for cross validation
getCrossValidate() - Method in class weka.classifiers.lazy.IBk: Gets whether hold-one-out cross-validation will be used to select the best k value
getCurrent() - Method in class weka.core.Memory: returns the current memory consumption
getCurrentDatasetNumber() - Method in class weka.experiment.Experiment: When an experiment is running, this returns the current dataset number.
getCurrentFilename() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the filename of the current tab
getCurrentIndex() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the currently selected tab index
getCurrentInstance() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the current instance
getCurrentPanel() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the currently selected panel
getCurrentPropertyNumber() - Method in class weka.experiment.Experiment: When an experiment is running, this returns the index of the current custom property value.
getCurrentRunNumber() - Method in class weka.experiment.Experiment: When an experiment is running, this returns the current run number.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.CostCurve: Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.CostCurve: Calculates the performance stats for the desired class and return results as a set of Instances.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.MarginCurve: Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the performance stats for the desired class and return results as a set of Instances.
getCustomEditor() - Method in class weka.gui.CostMatrixEditor: Gets a GUI component with which the user can edit the cost matrix.
getCustomEditor() - Method in class weka.gui.FileEditor: Gets the custom editor component.
getCustomEditor() - Method in class weka.gui.GenericArrayEditor: Returns the array editing component.
getCustomEditor() - Method in class weka.gui.GenericObjectEditor: Returns the array editing component.
getCustomEditor() - Method in class weka.gui.SimpleDateFormatEditor: Gets a GUI component with which the user can edit the date format.
getCustomPanel() - Method in interface weka.gui.CustomPanelSupplier: Gets the custom panel for the object.
getCustomPanel() - Method in class weka.gui.GenericObjectEditor: Gets the custom panel used for editing the object.
getCutoff() - Method in class weka.clusterers.Cobweb: get the cutoff
getCutPoints(int) - Method in class weka.filters.supervised.attribute.Discretize: Gets the cut points for an attribute
getCutPoints(int) - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the cut points for an attribute
getCVisible() - Method in class weka.gui.treevisualizer.Node: Get If this node's childs are visible.
getCVParameter(int) - Method in class weka.classifiers.meta.CVParameterSelection: Gets the scheme paramter with the given index.
getCVParameters() - Method in class weka.classifiers.meta.CVParameterSelection: Get method for CVParameters.
getCVPredictions(Classifier, Instances, int) - Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.
getCVType() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: get cross validation strategy to be used in searching for networks.
getD() - Method in class weka.core.matrix.EigenvalueDecomposition: Return the block diagonal eigenvalue matrix
getData() - Method in class weka.attributeSelection.BestFirst.Link2: Get a group
getData() - Method in class weka.classifiers.rules.RuleStats: Get the data of the stats
getDatabaseURL() - Method in class weka.experiment.DatabaseUtils: Get the value of DatabaseURL.
getDataFileName() - Method in class weka.classifiers.BVDecompose: Get the name of the data file used for the decomposition
getDataFileName() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the name of the data file used for the decomposition
getDataPoint() - Method in class weka.gui.beans.ChartEvent: Get the data point
getDataSet() - Method in class weka.core.converters.AbstractLoader
getDataSet() - Method in class weka.core.converters.ArffLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.C45Loader: Return the full data set.
getDataSet() - Method in class weka.core.converters.CSVLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.DatabaseLoader: Return the full data set in batch mode (header and all intances at once).
getDataSet() - Method in interface weka.core.converters.Loader: Return the full data set.
getDataSet() - Method in class weka.core.converters.SerializedInstancesLoader: Return the full data set.
getDataSet() - Method in class weka.gui.beans.DataSetEvent: Return the instances of the data set
getDataSet() - Method in class weka.gui.beans.ThresholdDataEvent: Return the instances of the data set
getDataSet() - Method in class weka.gui.beans.VisualizableErrorEvent: Return the instances of the data set
getDatasetFormat() - Method in class weka.datagenerators.BIRCHCluster: Gets the dataset format.
getDatasetFormat() - Method in class weka.datagenerators.RDG1: Gets the dataset format.
getDatasetKeyColumns() - Method in class weka.experiment.PairedTTester: Get the value of DatasetKeyColumns.
getDatasets() - Method in class weka.experiment.Experiment: Gets the datasets in the experiment.
getDatasetsFirst() - Static method in class weka.gui.experiment.ExperimenterDefaults: whether datasets or algorithms are iterated first
getDataType() - Method in class weka.gui.beans.xml.XMLBeans: returns the type of data that is to be read/written
getDateFormat() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: Get the date format used in output.
getDebug() - Method in class weka.attributeSelection.RaceSearch: Get whether output is to be verbose
getDebug() - Method in class weka.classifiers.BVDecompose: Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.CheckClassifier: Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.Classifier: Get whether debugging is turned on.
getDebug() - Method in class weka.classifiers.functions.LeastMedSq: Returns whether or not debugging output shouild be printed
getDebug() - Method in class weka.classifiers.functions.LinearRegression: Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.functions.Logistic: Gets whether debugging output will be printed.
getDebug() - Method in class weka.classifiers.functions.PaceRegression: Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.meta.MultiScheme: Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.rules.JRip
getDebug() - Method in class weka.classifiers.trees.RandomTree: Get the value of Debug.
getDebug() - Method in class weka.clusterers.EM: Get debug mode
getDebug() - Method in class weka.datagenerators.ClusterGenerator: Gets the debug flag.
getDebug() - Method in class weka.datagenerators.Generator: Gets the debug flag.
getDebug() - Method in class weka.experiment.DatabaseUtils: Gets whether there should be printed some debugging output to stderr or not
getDebug() - Method in class weka.filters.unsupervised.attribute.AddExpression: Gets whether debug is set
getDebug() - Method in class weka.gui.streams.InstanceCounter
getDebug() - Method in class weka.gui.streams.InstanceJoiner
getDebug() - Method in class weka.gui.streams.InstanceLoader
getDebug() - Method in class weka.gui.streams.InstanceSavePanel
getDebug() - Method in class weka.gui.streams.InstanceTable
getDebug() - Method in class weka.gui.streams.InstanceViewer
getDecay() - Method in class weka.classifiers.functions.MultilayerPerceptron
getDefaultWeight() - Method in class weka.classifiers.functions.Winnow: Get the value of defaultWeight.
getDelimiters() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the value of delimiters.
getDelta() - Method in class weka.associations.Apriori: Get the value of delta.
getDelta() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing
getDelta() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing
getDensityBasedClusterer() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Get the clusterer used by this filter
getDescendantPopulationSize() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getDescendantPopulationSize() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getDescription() - Method in class weka.gui.ExtensionFileFilter: Gets the description of accepted files.
getDescription() - Method in class weka.gui.visualize.JComponentWriter: returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.JPEGWriter: returns the name of the writer, to display in the FileChooser.
getDescription() - Method in class weka.gui.visualize.PostscriptWriter: returns the name of the writer, to display in the FileChooser.
getDesignatedClass() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the method to determine which class value to optimize.
getDesiredSize() - Method in class weka.classifiers.meta.Decorate: Gets the desired size of the committee.
getDesiredWeightOfInstancesPerInterval() - Method in class weka.filters.unsupervised.attribute.Discretize: Get the DesiredWeightOfInstancesPerInterval value.
getDestination() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default destination
getDeviceConfiguration() - Method in class weka.gui.visualize.PostscriptGraphics
getDir() - Method in class weka.gui.Loader: returns the dir prefix
getDirection() - Method in class weka.attributeSelection.BestFirst: Get the search direction
getDiscretizer() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Return the discretizer used at this node
getDisplayedResultsets() - Method in class weka.experiment.PairedTTester: Gets the indices of the the datasets that are displayed (if null then all are displayed).
getDisplayRules() - Method in class weka.classifiers.rules.DecisionTable: Gets whether rules are being printed
getDistanceWeighting() - Method in class weka.classifiers.lazy.IBk: Gets the distance weighting method used.
getDistMult() - Method in class weka.datagenerators.BIRCHCluster: Gets the distance multiplier.
getDistribution() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the current distribution that'll be used for calculating the random matrix
getDistributions() - Method in class weka.classifiers.bayes.BayesNet: Get full set of estimators.
getDistributions(int) - Method in class weka.classifiers.rules.RuleStats: Get the class distribution predicted by the rule in given position
getDistributionSpread() - Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the value for the distribution spread
getDocType() - Method in class weka.core.xml.XMLDocument: returns the current DOCTYPE, can be null
getDocument() - Method in class weka.core.xml.XMLDocument: returns the parsed DOM document
getDocument() - Method in class weka.core.xml.XMLOptions: returns the parsed DOM document
getDontNormalize() - Method in class weka.classifiers.lazy.LWL: Gets whether if the numeric attribute values are not to be normalized for calculating the distances.
getDoublePivot() - Method in class weka.core.matrix.LUDecomposition: Return pivot permutation vector as a one-dimensional double array
getEditor() - Method in class weka.gui.PropertyDialog: Gets the current property editor.
getEditorActive() - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Returns true if the editor is currently in an active status---that is the array is active and able to be edited.
getElement(int, int) - Method in class weka.core.Matrix: Deprecated. Returns the value of a cell in the matrix.
getEliminateColinearAttributes() - Method in class weka.classifiers.functions.LinearRegression: Get the value of EliminateColinearAttributes.
getEntropicAutoBlend() - Method in class weka.classifiers.lazy.KStar: Get whether entropic blending being used
getEntry(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Returns the table entry to which the specified key is mapped in this hashtable.
getEps() - Method in class weka.classifiers.functions.SMOreg: Get the value of eps.
getEpsilon() - Method in class weka.classifiers.functions.SMO: Get the value of epsilon.
getEpsilon() - Method in class weka.classifiers.functions.SMOreg: Get the value of epsilon.
getEpsilonParameter() - Method in class weka.attributeSelection.SVMAttributeEval: Get the value of P used with SMO
getError() - Method in class weka.classifiers.BVDecompose: Get the calculated error rate
getError() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated error rate
getErrorOnProbabilities() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of errorOnProbabilities.
getErrorOnProbabilities() - Method in class weka.classifiers.trees.LMT: Get the value of errorOnProbabilities.
getErrors() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Return the errors made by the naive bayes model at this node
getErrors() - Method in class weka.classifiers.trees.j48.NBTreeSplit: Return the errors made by the naive bayes models arising from this split.
getEstimatedErrorsForLeaf() - Method in class weka.classifiers.rules.part.C45PruneableDecList: Computes estimated errors for leaf.
getEstimator() - Method in class weka.classifiers.bayes.BayesNet: Get the BayesNetEstimator used for calculating the CPTs
getEstimator() - Method in class weka.classifiers.functions.PaceRegression: Gets the estimator
getEstimator(double) - Method in interface weka.estimators.ConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DDConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DKConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DNConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.KDConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.KKConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NDConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NNConditionalEstimator: Get a probability estimator for a value
getEvaluationMode() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the evaluation mode used.
getEvaluator() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the attribute evaluator used
getEvaluator() - Method in class weka.filters.supervised.attribute.AttributeSelection: Get the name of the attribute/subset evaluator
getEvalUsingTrainingData() - Method in class weka.attributeSelection.OneRAttributeEval: Returns true if the training data is to be used for evaluation
getEventName() - Method in class weka.gui.beans.BeanConnection: Returns the name of the event for this conncetion
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSinkBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSourceBeanInfo: Get the event set descriptors pertinent to data sources
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTestSetProducerBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainingSetProducerBeanInfo: Returns event set descriptors for this type of bean
getEventSetDescriptors() - Method in class weka.gui.beans.AttributeSummarizerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo: Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.ClassValuePickerBeanInfo: Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ClustererBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.ClustererPerformanceEvaluatorBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.DataVisualizerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.FilterBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.GraphViewerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.ModelPerformanceChartBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.PredictionAppenderBeanInfo: Get the event set descriptors pertinent to data sources
getEventSetDescriptors() - Method in class weka.gui.beans.ScatterPlotMatrixBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.TextViewerBeanInfo: Get the event set descriptors for this bean
getExclusive() - Method in class weka.classifiers.rules.ConjunctiveRule
getExecutionStatus() - Method in class weka.experiment.TaskStatusInfo: Get the execution status of this Task.
getExitOnClose() - Method in class weka.gui.arffviewer.ArffViewer: returns TRUE if a System.exit(0) is done on a close
getExitOnClose() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns TRUE if a System.exit(0) is done on a close
getExpectedFrequency() - Method in class weka.associations.tertius.Rule: Get the expected frequency of counter-instances of this rule.
getExpectedNumber() - Method in class weka.associations.tertius.Rule
getExpectedResultsPerAverage() - Method in class weka.experiment.AveragingResultProducer: Get the value of ExpectedResultsPerAverage.
getExperiment() - Method in class weka.experiment.RemoteExperimentSubTask: Get the experiment for this sub task
getExperiment() - Method in class weka.gui.experiment.SetupModePanel: Gets the currently configured experiment.
getExperiment() - Method in class weka.gui.experiment.SetupPanel: Gets the currently configured experiment.
getExperiment() - Method in class weka.gui.experiment.SimpleSetupPanel: Gets the currently configured experiment.
getExperimentType() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default experiment type
getExplicitPropsFile() - Method in class weka.gui.GenericPropertiesCreator: returns TRUE, if a file is loaded and not the Utils class used for locating the props file.
getExponent() - Method in class weka.classifiers.functions.SMO: Get the value of exponent.
getExponent() - Method in class weka.classifiers.functions.SMOreg: Get the value of exponent.
getExponent() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of exponent.
getExpression() - Method in class weka.filters.unsupervised.attribute.AddExpression: Get the expression
getExtension() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default experiment extension
getExtension() - Method in class weka.gui.visualize.JComponentWriter: returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.JPEGWriter: returns the extension (incl.
getExtension() - Method in class weka.gui.visualize.PostscriptWriter: returns the extension (incl.
getFactory() - Method in class weka.core.xml.XMLDocument: returns the DocumentBuilderFactory
getFallout() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the fallout.
getFalseNegative() - Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of positive instances predicted as negative
getFalsePositive() - Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of negative instances predicted as positive
getFalsePositiveRate() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the false positive rate.
getFastRegression() - Method in class weka.classifiers.trees.LMT: Get the value of fastRegression.
getFeatureSpaceNormalization() - Method in class weka.classifiers.functions.SMO: Check whether feature spaces is being normalized.
getFeatureSpaceNormalization() - Method in class weka.classifiers.functions.SMOreg: Check whether feature spaces is being normalized.
getFile() - Method in class weka.gui.visualize.JComponentWriter: returns the file being used for storing the output
getFileDescription() - Method in class weka.core.converters.AbstractFileSaver: to be pverridden
getFileDescription() - Method in class weka.core.converters.ArffLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.ArffSaver: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.C45Loader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.C45Saver: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.CSVLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.CSVSaver: Returns a description of the file type.
getFileDescription() - Method in interface weka.core.converters.FileSourcedConverter: Get a one line description of the type of file
getFileDescription() - Method in class weka.core.converters.SerializedInstancesLoader: Returns a description of the file type.
getFileDescription() - Method in class weka.core.converters.SerializedInstancesSaver: Returns a description of the file type.
getFileExtension() - Method in class weka.core.converters.AbstractFileSaver: Gets ihe file extension.
getFileExtension() - Method in class weka.core.converters.AbstractSaver: Default implementation throws an IOException.
getFileExtension() - Method in class weka.core.converters.ArffLoader: Get the file extension used for arff files
getFileExtension() - Method in class weka.core.converters.C45Loader: Get the file extension used for arff files
getFileExtension() - Method in class weka.core.converters.CSVLoader: Get the file extension used for arff files
getFileExtension() - Method in interface weka.core.converters.FileSourcedConverter: Get the file extension used for this type of file
getFileExtension() - Method in interface weka.core.converters.Saver: Gets the file extension
getFileExtension() - Method in class weka.core.converters.SerializedInstancesLoader: Get the file extension used for arff files
getFileName() - Method in class weka.classifiers.bayes.net.BIFReader
getFilename() - Method in class weka.gui.arffviewer.ArffPanel: returns the filename
getFilename(int) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the filename of the specified panel
getFillWithMissing() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Gets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
getFilter() - Method in class weka.classifiers.meta.FilteredClassifier: Gets the filter used.
getFilter() - Method in class weka.gui.beans.Filter
getFiltered(int) - Method in class weka.classifiers.rules.RuleStats: Get the data after filtering the given rule
getFilterType() - Method in class weka.attributeSelection.SVMAttributeEval: Get the filtering mode passed to SMO
getFilterType() - Method in class weka.classifiers.functions.SMO: Gets how the training data will be transformed.
getFilterType() - Method in class weka.classifiers.functions.SMOreg: Gets how the training data will be transformed.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.Discretize: Get the value of FindNumBins.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Get the value of FindNumBins.
getFirst() - Method in class weka.associations.tertius.SimpleLinkedList
getFirstToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils: Gets token, skipping empty lines.
getFirstValueIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Get the index of the first value used.
getFirstValueIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the first value used.
getFlag(char, String[]) - Static method in class weka.core.Utils: Checks if the given array contains the flag "-Char".
getFlag(String, String[]) - Static method in class weka.core.Utils: Checks if the given array contains the flag "-String".
getFMeasure() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the F-Measure.
getFold() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the fold which is selected.
getFold() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the fold which is selected.
getFoldColumn() - Method in class weka.experiment.PairedTTester: Get the value of FoldColumn.
getFolds() - Method in class weka.attributeSelection.OneRAttributeEval: Get the number of folds used for cross validation
getFolds() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the number of folds used for accuracy estimation
getFolds() - Method in class weka.classifiers.rules.ConjunctiveRule
getFolds() - Method in class weka.classifiers.rules.JRip
getFolds() - Method in class weka.classifiers.rules.Ridor
getFolds() - Method in class weka.gui.beans.CrossValidationFoldMaker: Get the currently set number of folds
getFolds() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the number of folds used for cross-validation
getFoldsType() - Method in class weka.attributeSelection.RaceSearch: Get the xfold type
getFont() - Method in class weka.gui.visualize.PostscriptGraphics: Get current font.
getFontMetrics(Font) - Method in class weka.gui.visualize.PostscriptGraphics: Get Font metrics
getFontRenderContext() - Method in class weka.gui.visualize.PostscriptGraphics: START overridden Graphics2D methods
getFPRate() - Method in class weka.associations.tertius.Rule: Get the rate of False Positive instances of this rule.
getFrameTitle() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the title (incl.
getFrequencyThreshold() - Method in class weka.associations.Tertius: Get the value of frequencyThreshold.
getFunctionValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Gets a particular function value
getGamma() - Method in class weka.classifiers.functions.SMO: Get the value of gamma.
getGamma() - Method in class weka.classifiers.functions.SMOreg: Get the value of gamma.
getGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the number of visible groups of siblings there are.
getGenerateRanking() - Method in class weka.attributeSelection.GreedyStepwise: Gets whether ranking has been requested.
getGenerateRanking() - Method in class weka.attributeSelection.RaceSearch: Gets whether ranking has been requested.
getGenerateRanking() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets whether the user has opted to see a ranked list of attributes rather than the normal result of the search
getGenerateRanking() - Method in class weka.attributeSelection.Ranker: This is a dummy method.
getGeneratorSamplesBase() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the base used for computing the number of samples to obtain from each generator
getGlobalBlend() - Method in class weka.classifiers.lazy.KStar: Get the value of the global blend parameter
getGlobalInfo(Object) - Static method in class weka.gui.beans.KnowledgeFlowApp: Utility method for grabbing the global info help (if it exists) from an arbitrary object
getGlobalModel() - Method in class weka.classifiers.trees.j48.NBTreeSplit: Return the global naive bayes model for this node
getGraphString() - Method in class weka.gui.beans.GraphEvent: Return the dot string for the graph
getGraphTitle() - Method in class weka.gui.beans.GraphEvent: Return the graph title
getGraphType() - Method in class weka.gui.beans.GraphEvent: Return the graph type
getGridFlag() - Method in class weka.datagenerators.BIRCHCluster: Gets the grid flag (option G).
getGridWidth() - Method in class weka.gui.beans.AttributeSummarizer: Get the width of the grid of plots
getGroup() - Method in class weka.classifiers.rules.DecisionTable.Link: Gets the group.
getGUI() - Method in class weka.classifiers.functions.MultilayerPerceptron
getH() - Method in class weka.core.matrix.QRDecomposition: Return the Householder vectors
getHashtable(FastVector, int) - Static method in class weka.associations.ItemSet: Return a hashtable filled with the given item sets.
getHashtable(FastVector, int) - Static method in class weka.associations.LabeledItemSet: Return a hashtable filled with the given item sets.
getHeight() - Method in class weka.gui.beans.BeanInstance: Gets the height of this bean
getHeight(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the number of visible levels there are.
getHeuristicStop() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of heuristicStop.
getHiddenLayers() - Method in class weka.classifiers.functions.MultilayerPerceptron
getHoldOutFile() - Method in class weka.attributeSelection.ClassifierSubsetEval: Gets the file that holds hold out/test instances.
getHornClauses() - Method in class weka.associations.Tertius: Get the value of hornClauses.
getIconPath() - Method in class weka.gui.beans.BeanVisual: returns the path for the icon
getId() - Method in class weka.classifiers.functions.neural.NeuralConnection
getID() - Method in class weka.core.Tag: Gets the numeric ID of the Tag.
getID() - Method in class weka.gui.streams.InstanceEvent: Get the event type
getID() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
getIDFTransform() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AddCluster: Gets ranges of attributes to be ignored.
getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Gets ranges of attributes to be ignored.
getImage(String, String) - Static method in class weka.gui.ComponentHelper: returns the Image for a given directory and filename, NULL if not successful
getImage(String) - Static method in class weka.gui.ComponentHelper: returns the Image for a given filename, NULL if not successful
getImageIcon(String, String) - Static method in class weka.gui.ComponentHelper: returns the ImageIcon for a given filename and directory, NULL if not successful
getImageIcon(String) - Static method in class weka.gui.ComponentHelper: returns the ImageIcon for a given filename, NULL if not successful
getImagEigenvalues() - Method in class weka.core.matrix.EigenvalueDecomposition: Return the imaginary parts of the eigenvalues
getIncludeClass() - Method in class weka.core.InstanceComparator: returns TRUE if the class is included in the comparison
getIndex() - Method in class weka.associations.tertius.Predicate
getIndex() - Method in class weka.core.SingleIndex: Gets the selected index
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Method declaration
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.global.K2: Method declaration
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Method declaration
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.net.search.local.K2: Method declaration
getInitial() - Method in class weka.core.Memory: returns the initial size of the JVM
getInitialDatasetsDirectory() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the initial directory for the datasets (if empty, it returns the user's home directory)
getInputFilename() - Method in class weka.gui.GenericPropertiesCreator: returns the name of the input file
getInputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the input numbers.
getInputOrder() - Method in class weka.datagenerators.BIRCHCluster: Gets the input order.
getInputProperties() - Method in class weka.gui.GenericPropertiesCreator: returns the input properties object (template containing the packages)
getInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the inputs.
getInputs() - Method in class weka.gui.beans.MetaBean
getInputStream(String, String) - Static method in class weka.gui.Loader: returns an InputStream for the given dir and filename, can be NULL if it fails
getInputStream(String) - Method in class weka.gui.Loader: returns an InputStream for the given filename, can be NULL if it fails
getInstalledLookAndFeels() - Static method in class weka.gui.LookAndFeel: returns an array with the classnames of all the installed LnFs
getInstance() - Method in class weka.gui.beans.InstanceEvent: Get the instance
getInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Instance Index array
getInstanceRange() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Gets the number of instances forward to translate values between.
getInstances() - Method in class weka.core.converters.AbstractSaver: Gets instances that should be stored.
getInstances() - Method in class weka.experiment.PairedTTester: Get the value of Instances.
getInstances() - Method in class weka.gui.arffviewer.ArffPanel: returns the instances of the panel, if none then NULL
getInstances() - Method in class weka.gui.arffviewer.ArffTableModel: returns the data
getInstances() - Method in class weka.gui.arffviewer.ArffTableSorter: returns the data
getInstances() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Get the training instances
getInstances() - Method in class weka.gui.explorer.PreprocessPanel: Gets the working set of instances.
getInstances() - Method in class weka.gui.SetInstancesPanel: Gets the set of instances currently held by the panel
getInstances() - Method in class weka.gui.treevisualizer.Node: This will return the Instances object related to this node.
getInstances() - Method in class weka.gui.ViewerDialog: returns the currently displayed instances
getInstances() - Method in class weka.gui.visualize.VisualizePanel: Get the master plot's instances
getInstances1() - Method in class weka.gui.visualize.VisualizePanelEvent
getInstances2() - Method in class weka.gui.visualize.VisualizePanelEvent
getInstancesIndices() - Method in class weka.filters.unsupervised.instance.RemoveRange: Gets ranges of instances selected.
getInstNums() - Method in class weka.datagenerators.BIRCHCluster: Gets the upper and lower boundary for instances per cluster.
getIntercept() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns the intercept of the function.
getInvert() - Method in class weka.core.Range: Gets whether the range sense is inverted, i.e.
getInvert() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Get whether selection is inverted.
getInvertSelection() - Method in class weka.filters.supervised.attribute.Discretize: Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Copy: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get whether the supplied columns are to be transformed or not
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Remove: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.RemoveType: Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveRange: Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get whether the supplied columns are to be removed or kept
getJavaInitializationString() - Method in class weka.gui.CostMatrixEditor: Returns the Java code that generates an object the same as the one being edited.
getJavaInitializationString() - Method in class weka.gui.FileEditor: Returns a representation of the current property value as java source.
getJavaInitializationString() - Method in class weka.gui.GenericArrayEditor: Supposedly returns an initialization string to create a classifier identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString() - Method in class weka.gui.GenericObjectEditor: Supposedly returns an initialization string to create a Object identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString() - Method in class weka.gui.SelectedTagEditor: Returns a description of the property value as java source.
getJavaInitializationString() - Method in class weka.gui.SimpleDateFormatEditor: Returns the Java code that generates an object the same as the one being edited.
getJTable() - Method in class weka.gui.JTableHelper: returns the JTable
getKernelBandwidth() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Get the kernel bandwidth
getKey() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the key describing the current SplitEvaluator.
getKey() - Method in interface weka.experiment.SplitEvaluator: Gets the key describing the current SplitEvaluator.
getKeyFieldName() - Method in class weka.experiment.AveragingResultProducer: Get the value of KeyFieldName.
getKeyNames() - Method in class weka.experiment.AveragingResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.CrossValidationResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DatabaseResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.LearningRateResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.RandomSplitResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in interface weka.experiment.ResultProducer: Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in interface weka.experiment.SplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeys() - Method in class weka.core.converters.DatabaseLoader: Gets the key columns' name
getKeyTypes() - Method in class weka.experiment.AveragingResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.CrossValidationResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DatabaseResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.LearningRateResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.RandomSplitResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in interface weka.experiment.ResultProducer: Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in interface weka.experiment.SplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKNN() - Method in class weka.classifiers.lazy.IBk: Gets the number of neighbours the learner will use.
getKNN() - Method in class weka.classifiers.lazy.LWL: Gets the number of neighbours used for kernel bandwidth setting.
getKValue() - Method in class weka.classifiers.trees.RandomTree: Get the value of K.
getKWBias() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated bias squared according to the Kohavi and Wolpert definition
getKWSigma() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated sigma according to the Kohavi and Wolpert definition
getKWVariance() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated variance according to the Kohavi and Wolpert definition
getL() - Method in class weka.core.matrix.CholeskyDecomposition: Return triangular factor.
getL() - Method in class weka.core.Matrix: Deprecated. Returns the L part of the matrix.
getL() - Method in class weka.core.matrix.LUDecomposition: Return lower triangular factor
getLabel() - Method in class weka.gui.treevisualizer.Edge: Get the value of label.
getLabel() - Method in class weka.gui.treevisualizer.Node: Get the value of label.
getLast() - Method in class weka.associations.tertius.SimpleLinkedList
getLastLiteral() - Method in class weka.associations.tertius.LiteralSet: Give the last literal added to this set.
getLearningRate() - Method in class weka.classifiers.functions.MultilayerPerceptron
getLegendText() - Method in class weka.gui.beans.ChartEvent: Get the legend text vector
getLevel() - Method in class weka.gui.HierarchyPropertyParser: Get the level of current node.
getLikelihoodThreshold() - Method in class weka.classifiers.meta.LogitBoost: Get the value of Precision.
getLine(int) - Method in class weka.gui.treevisualizer.Edge: Returns line number n
getLine(int) - Method in class weka.gui.treevisualizer.Node: Returns the text String for the specfied line.
getLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2: returns the element (Link) at a specific index from the list.
getLinkAt(int) - Method in class weka.classifiers.rules.DecisionTable.LinkedList: Returns the element (Link) at a specific index from the list.
getList() - Method in class weka.gui.ResultHistoryPanel: Gets the JList used by the results list
getListCellRendererComponent(JList, Object, int, boolean, boolean) - Method in class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
getLiteral(int) - Method in class weka.associations.tertius.Predicate
getLNorm() - Method in class weka.filters.unsupervised.instance.Normalize: Get the L Norm used.
getLoader() - Method in class weka.gui.beans.Loader: Get the loader
getLocallyPredictive() - Method in class weka.attributeSelection.CfsSubsetEval: Return true if including locally predictive attributes
getLogLikelihood() - Method in class weka.clusterers.ClusterEvaluation: Return the log likelihood corresponding to the most recent set of instances clustered.
getLookupCacheSize() - Method in class weka.attributeSelection.BestFirst: Return the maximum size of the evaluated subset cache (expressed as a multiplier for the number of attributes in a data set.
getLower() - Method in class weka.gui.experiment.RunNumberPanel: Gets the current lower run number.
getLowerBoundMinSupport() - Method in class weka.associations.Apriori: Get the value of lowerBoundMinSupport.
getLowerCaseTokens() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the tokens are to be downcased or not.
getLowerNumericBound() - Method in class weka.core.Attribute: Returns the lower bound of a numeric attribute.
getLowerOrderTerms() - Method in class weka.classifiers.functions.SMO: Check whether lower-order terms are being used.
getLowerOrderTerms() - Method in class weka.classifiers.functions.SMOreg: Check whether lower-order terms are being used.
getLowerSize() - Method in class weka.experiment.LearningRateResultProducer: Get the value of LowerSize.
getM5RootNode() - Method in class weka.classifiers.trees.m5.M5Base
getM5RootNode() - Method in class weka.classifiers.trees.m5.Rule
getMainPanel() - Method in class weka.gui.arffviewer.ArffViewer: returns the main panel
getMajorityClass() - Method in class weka.classifiers.rules.Ridor
getMakeBinary() - Method in class weka.filters.supervised.attribute.Discretize: Gets whether binary attributes should be made for discretized ones.
getMakeBinary() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets whether binary attributes should be made for discretized ones.
getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm
getMarkovBlanketClassifier() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
getMasterPlot() - Method in class weka.gui.visualize.Plot2D: Get the master plot
getMatchMissingValues() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Gets whether missing values are counted as a match.
getMatrix(int, int, int, int) - Method in class weka.classifiers.functions.pace.Matrix: Get a submatrix.
getMatrix(int[], int[]) - Method in class weka.classifiers.functions.pace.Matrix: Get a submatrix.
getMatrix(int, int, int[]) - Method in class weka.classifiers.functions.pace.Matrix: Get a submatrix.
getMatrix(int[], int, int) - Method in class weka.classifiers.functions.pace.Matrix: Get a submatrix.
getMatrix(int, int, int, int) - Method in class weka.core.matrix.Matrix: Get a submatrix.
getMatrix(int[], int[]) - Method in class weka.core.matrix.Matrix: Get a submatrix.
getMatrix(int, int, int[]) - Method in class weka.core.matrix.Matrix: Get a submatrix.
getMatrix(int[], int, int) - Method in class weka.core.matrix.Matrix: Get a submatrix.
getMax() - Method in class weka.core.Memory: returns the maximum amount of memory that can be assigned
getMax() - Method in class weka.gui.beans.ChartEvent: Get the max y value
getMaxBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of maxBoostingIterations.
getMaxC() - Method in class weka.gui.visualize.Plot2D: Return the current max value of the colouring attribute
getMaxCardinality() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm
getMaxChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the maximum chunk size
getMaxCost(int) - Method in class weka.classifiers.CostMatrix: Gets the maximum cost for a particular class value.
getMaxCount() - Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the value for the max count
getMaxDepth() - Method in class weka.classifiers.trees.REPTree: Get the value of MaxDepth.
getMaxGenerations() - Method in class weka.attributeSelection.GeneticSearch: get the number of generations
getMaximumAttributeNames() - Method in class weka.attributeSelection.PrincipalComponents: Gets maximum number of attributes to include in transformed attribute names.
getMaximumVariancePercentageAllowed() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Gets the maximum variance attributes are allowed to have before they are deleted by the filter.
getMaxInstNum() - Method in class weka.datagenerators.BIRCHCluster: Gets the upper boundary for instances per cluster.
getMaxIterations() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns the maxIterations parameter.
getMaxIterations() - Method in class weka.clusterers.EM: Get the maximum number of iterations
getMaxIterations() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the maximum number of cleansing iterations performed
getMaxIts() - Method in class weka.classifiers.functions.Logistic: Get the value of MaxIts.
getMaxIts() - Method in class weka.classifiers.functions.RBFNetwork: Get the value of MaxIts.
getMaxK() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of maxK.
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Method declaration
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.global.K2: Method declaration
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Method declaration
getMaxNrOfParents() - Method in class weka.classifiers.bayes.net.search.local.K2: Method declaration
getMaxPlots() - Method in class weka.gui.beans.AttributeSummarizer: Get the number of plots to display
getMaxRadius() - Method in class weka.datagenerators.BIRCHCluster: Gets the upper boundary for the radiuses of the clusters.
getMaxRuleSize() - Method in class weka.datagenerators.RDG1: Gets the maximum number of tests in rules.
getMaxSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent: Get the maximum set number (ie the total number of training and testing sets in the series).
getMaxSetNumber() - Method in class weka.gui.beans.BatchClustererEvent: Get the maximum set number (ie the total number of training and testing sets in the series).
getMaxSetNumber() - Method in class weka.gui.beans.TestSetEvent: Get the maximum set number
getMaxSetNumber() - Method in class weka.gui.beans.TrainingSetEvent: Get the maximum set number
getMaxStale() - Method in class weka.classifiers.rules.DecisionTable: Gets the number of non improving decision tables
getMaxX() - Method in class weka.gui.visualize.Plot2D: Return the current max value of the attribute plotted on the x axis
getMaxY() - Method in class weka.gui.visualize.Plot2D: Return the current max value of the attribute plotted on the y axis
getMeanPrec() - Method in class weka.experiment.PairedTTester: Gets the precision used for printing the mean
getMeanPrec() - Method in class weka.gui.experiment.OutputFormatDialog: Gets the precision used for printing the mean
getMeanPrecision() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the default precision for the mean
getMeanSquared() - Method in class weka.classifiers.lazy.IBk: Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
getMeasure(String) - Method in class weka.classifiers.bayes.BayesNet: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.functions.SimpleLogistic: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.meta.AdditiveRegression: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.meta.Bagging: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.rules.DecisionTable: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.JRip: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.PART: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.Ridor: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.ADTree: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.J48: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.LMT: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.m5.M5Base: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.NBTree: Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.trees.RandomForest: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.REPTree: Returns the value of the named measure.
getMeasure(String) - Method in interface weka.core.AdditionalMeasureProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.AveragingResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.ClassifierSplitEvaluator: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.CrossValidationResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DatabaseResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.LearningRateResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RandomSplitResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RegressionSplitEvaluator: Returns the value of the named measure
getMenu() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the menu bar to be added in a frame
getMerit() - Method in class weka.classifiers.rules.DecisionTable.Link: Gets the merit.
getMetaClassifier() - Method in class weka.classifiers.meta.Stacking: Gets the meta classifier.
getMetadata() - Method in class weka.core.Attribute: Returns the properties supplied for this attribute.
getMetaData() - Method in class weka.core.converters.DatabaseConnection: Gets meta data for the database connection object.
getMethod() - Method in class weka.classifiers.functions.neural.NeuralNode
getMethod() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets the method used.
getMethodName() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the transformation method.
getMetricType() - Method in class weka.associations.Apriori: Get the metric type
getMidPoints() - Method in class weka.associations.PriorEstimation: returns an ordered array of all mid points
getMin() - Method in class weka.gui.beans.ChartEvent: Get the min y value
getMinBucketSize() - Method in class weka.classifiers.rules.OneR: Get the value of minBucketSize.
getMinC() - Method in class weka.gui.visualize.Plot2D: Return the current min value of the colouring attribute
getMinChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the minimum chunk size
getMinFunction() - Method in class weka.core.Optimization: Get the minimal function value
getMinimizeExpectedCost() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the value of MinimizeExpectedCost.
getMinimumBucketSize() - Method in class weka.attributeSelection.OneRAttributeEval: Get the minimum bucket size used by oneR
getMinInstNum() - Method in class weka.datagenerators.BIRCHCluster: Gets the lower boundary for instances per cluster.
getMinMetric() - Method in class weka.associations.Apriori: Get the value of minConfidence.
getMinNo() - Method in class weka.classifiers.rules.ConjunctiveRule
getMinNo() - Method in class weka.classifiers.rules.JRip
getMinNo() - Method in class weka.classifiers.rules.Ridor
getMinNum() - Method in class weka.classifiers.trees.RandomTree: Get the value of MinNum.
getMinNum() - Method in class weka.classifiers.trees.REPTree: Get the value of MinNum.
getMinNumInstances() - Method in class weka.classifiers.trees.LMT: Get the value of minNumInstances.
getMinNumInstances() - Method in class weka.classifiers.trees.m5.M5Base: Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.Rule: Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode: Get the minimum number of instances to allow at a leaf node
getMinNumObj() - Method in class weka.classifiers.rules.PART: Get the value of minNumObj.
getMinNumObj() - Method in class weka.classifiers.trees.J48: Get the value of minNumObj.
getMinRadius() - Method in class weka.datagenerators.BIRCHCluster: Gets the lower boundary for the radiuses of the clusters.
getMinRuleSize() - Method in class weka.datagenerators.RDG1: Gets the minimum number of tests in rules.
getMinStdDev() - Method in class weka.classifiers.functions.RBFNetwork: Get the MinStdDev value.
getMinStdDev() - Method in class weka.clusterers.EM: Get the minimum allowable standard deviation.
getMinStdDev() - Method in class weka.clusterers.MakeDensityBasedClusterer: Get the minimum allowable standard deviation.
getMinVarianceProp() - Method in class weka.classifiers.trees.REPTree: Get the value of MinVarianceProp.
getMinX() - Method in class weka.gui.visualize.Plot2D: Return the current min value of the attribute plotted on the x axis
getMinY() - Method in class weka.gui.visualize.Plot2D: Return the current min value of the attribute plotted on the y axis
getMissingMerge() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.GainRatioAttributeEval: get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.InfoGainAttributeEval: get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: get whether missing values are being distributed or not
getMissingMode() - Method in class weka.classifiers.lazy.KStar: Gets the method to use for handling missing values.
getMissingSeparate() - Method in class weka.attributeSelection.CfsSubsetEval: Return true is missing is treated as a separate value
getMissingValues() - Method in class weka.associations.Tertius: Get the value of missingValues.
getMixingDistribution() - Method in class weka.classifiers.functions.pace.MixtureDistribution: Gets the mixing distribution
getModel() - Method in class weka.classifiers.trees.m5.RuleNode: Get the linear model at this node
getModel() - Method in class weka.gui.TableMap
getModelParameters() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
getModelValueAt(int, int) - Method in class weka.gui.arffviewer.ArffTableSorter: returns the value at the given position
getModelValueAt(int, int) - Method in class weka.gui.TableSorter: access to the unsorted values
getModifyHeader() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Gets whether the header will be modified when selecting on nominal attributes.
getMomentum() - Method in class weka.classifiers.functions.MultilayerPerceptron
getMutationProb() - Method in class weka.attributeSelection.GeneticSearch: get the probability of mutation
getNaiveBayesModel() - Method in class weka.classifiers.trees.j48.NBTreeNoSplit: Get the naive bayes model at this node
getName() - Method in class weka.classifiers.bayes.BayesNet: get name of the Bayes network
getName() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the name of the new attribute
getName() - Method in class weka.gui.visualize.VisualizePanel: Returns the name associated with this plot.
getNameAtIndex(int) - Method in class weka.gui.ResultHistoryPanel: Gets the name of theitem in the list at the specified index
getNamedBuffer(String) - Method in class weka.gui.ResultHistoryPanel: Gets the named buffer
getNamedObject(String) - Method in class weka.gui.ResultHistoryPanel: Get the named object from the list
getNegation() - Method in class weka.associations.Tertius: Get the value of negation.
getNegation() - Method in class weka.associations.tertius.Literal
getNext(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Gets the next element in the set.
getNextInstance(Instances) - Method in class weka.core.converters.AbstractLoader
getNextInstance(Instances) - Method in class weka.core.converters.ArffLoader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.C45Loader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.CSVLoader: CSVLoader is unable to process a data set incrementally.
getNextInstance(Instances) - Method in class weka.core.converters.DatabaseLoader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in interface weka.core.converters.Loader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance(Instances) - Method in class weka.core.converters.SerializedInstancesLoader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNode(String) - Method in class weka.classifiers.bayes.net.BIFReader: getNode finds the index of the node with name sNodeName and throws an exception if no such node can be found.
getNodeName(int) - Method in class weka.classifiers.bayes.BayesNet: get name of a node in the Bayes network
getNodes() - Method in class weka.classifiers.trees.lmt.LMTNode: Return a list of all inner nodes in the tree
getNodes(Vector) - Method in class weka.classifiers.trees.lmt.LMTNode: Fills a list with all inner nodes in the tree
getNodeValue(int, int) - Method in class weka.classifiers.bayes.BayesNet: get name of a particular value of a node
getNoiseRate() - Method in class weka.datagenerators.BIRCHCluster: Gets the percentage of noise set.
getNoiseThreshold() - Method in class weka.associations.Tertius: Get the value of noiseThreshold.
getNominalIndices() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the set of nominal value indices that will be used for selection
getNominalLabels() - Method in class weka.filters.unsupervised.attribute.Add: Get the list of labels for nominal attribute creation
getNominalToBinaryFilter() - Method in class weka.classifiers.functions.MultilayerPerceptron
getNoNormalization() - Method in class weka.classifiers.lazy.IBk: Gets whether normalization is turned off.
getNoPruning() - Method in class weka.classifiers.trees.REPTree: Get the value of NoPruning.
getNorm() - Method in class weka.filters.unsupervised.instance.Normalize: Get the instance's Norm.
getNormalizeAttributes() - Method in class weka.classifiers.functions.MultilayerPerceptron
getNormalizeDocLength() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the word frequencies for a document (instance) should be normalized or not.
getNormalizeNumericClass() - Method in class weka.classifiers.functions.MultilayerPerceptron
getNormalizeWordWeights() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns true if the word weights for each class are to be normalized
getNot() - Method in class weka.datagenerators.Test: Negates the test.
getNotes() - Method in class weka.experiment.Experiment: Get the user notes.
getNPointPrecision(Instances, int) - Static method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the n point precision result, which is the precision averaged over n evenly spaced (w.r.t recall) samples of the curve.
getNrOfNodes() - Method in class weka.classifiers.bayes.BayesNet: get number of nodes in the Bayes network
getNrOfParents(int) - Method in class weka.classifiers.bayes.BayesNet: get number of parents of a node in the network structure
getNrOfParents() - Method in class weka.classifiers.bayes.net.ParentSet: returns number of parents
getNumAntds() - Method in class weka.classifiers.rules.ConjunctiveRule
getNumAttemptsOfGeneOption() - Method in class weka.classifiers.rules.NNge: Gets the number of attempts for generalisation.
getNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes in the dataset
getNumAttributes() - Method in class weka.datagenerators.ClusterGenerator: Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.Generator: Gets the number of attributes that should be produced.
getNumAttributesSet() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes "in use"
getNumberLiterals() - Method in class weka.associations.Tertius: Get the value of numberLiterals.
getNumberOfAttributes() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the current number of attributes (dimensionality) to which the data will be reduced to.
getNumBins() - Method in class weka.classifiers.meta.RegressionByDiscretization: Gets the number of bins numeric attributes will be divided into
getNumBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of numBoostingIterations.
getNumBoostingIterations() - Method in class weka.classifiers.trees.LMT: Get the value of numBoostingIterations.
getNumClasses() - Method in class weka.datagenerators.Generator: Gets the number of classes the dataset should have.
getNumClusters() - Method in class weka.classifiers.functions.RBFNetwork: Return the number of clusters to generate.
getNumClusters() - Method in class weka.clusterers.ClusterEvaluation: Return the number of clusters found for the most recent call to evaluateClusterer
getNumClusters() - Method in class weka.clusterers.EM: Get the number of clusters
getNumClusters() - Method in class weka.clusterers.FarthestFirst: gets the number of clusters to generate
getNumClusters() - Method in class weka.clusterers.SimpleKMeans: gets the number of clusters to generate
getNumClusters() - Method in class weka.datagenerators.ClusterGenerator: Gets the number of clusters the dataset should have.
getNumCycles() - Method in class weka.datagenerators.BIRCHCluster: Gets the number of cycles.
getNumDatasets() - Method in class weka.experiment.PairedTTester: Gets the number of datasets in the resultsets
getNumDisplayedResultsets() - Method in class weka.experiment.PairedTTester: Gets the number of displayed resultsets in the data.
getNumeric() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Check if new attribute is to be numeric.
getNumExamples() - Method in class weka.datagenerators.Generator: Gets the number of examples, given by option.
getNumExamplesAct() - Method in class weka.datagenerators.ClusterGenerator: Gets the number of examples the dataset should have.
getNumExamplesAct() - Method in class weka.datagenerators.Generator: Gets the number of examples the dataset should have.
getNumFeatures() - Method in class weka.classifiers.trees.RandomForest: Get the number of features used in random selection.
getNumFoldersMIOption() - Method in class weka.classifiers.rules.NNge: Gets the number of folder for mutual information.
getNumFolds() - Method in class weka.classifiers.functions.SMO: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.meta.CVParameterSelection: Gets the number of folds for the cross-validation.
getNumFolds() - Method in class weka.classifiers.meta.LogitBoost: Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.meta.MultiScheme: Gets the number of folds for cross-validation.
getNumFolds() - Method in class weka.classifiers.meta.Stacking: Gets the number of folds for the cross-validation.
getNumFolds() - Method in class weka.classifiers.rules.PART: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.J48: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.REPTree: Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.CrossValidationResultProducer: Get the value of NumFolds.
getNumFolds() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the number of folds in which dataset is to be split into.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the number of folds in which dataset is to be split into.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the number of cross-validation folds used by the filter.
getNumGeneratingModels() - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Returns the number of generating models used by this DataGenerator
getNumGeneratingModels() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Return the number of kernels (there is one per training instance)
getNumInnerNodes() - Method in class weka.classifiers.trees.lmt.LMTNode: Method to count the number of inner nodes in the tree
getNumInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
getNumInstances() - Method in class weka.classifiers.CheckClassifier: Gets the current number of instances to use for the datasets.
getNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of instances in the dataset
getNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode: Return the number of instances that reach this node.
getNumInstancesSet() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of instances "in use"
getNumIrrelevant() - Method in class weka.datagenerators.RDG1: Gets the number of irrelevant attributes.
getNumIterations() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of NumIterations.
getNumIterations() - Method in class weka.classifiers.functions.Winnow: Get the value of numIterations.
getNumIterations() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Gets the number of bagging iterations
getNumIterations() - Method in class weka.classifiers.meta.MetaCost: Gets the number of bagging iterations
getNumLeaves() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the number of leaves in the tree.
getNumNeighbours() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the number of nearest neighbours
getNumNumeric() - Method in class weka.datagenerators.RDG1: Gets the number of numerical attributes.
getNumOfBoostingIterations() - Method in class weka.classifiers.trees.ADTree: Gets the number of boosting iterations.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.Splitter: Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the number of branches of the split.
getNumOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
getNumRegressions() - Method in class weka.classifiers.functions.SimpleLogistic: Get the number of LogitBoost iterations performed (= the number of regression functions fit by LogitBoost).
getNumRegressions() - Method in class weka.classifiers.trees.lmt.LogisticBase: The number of LogitBoost iterations performed (= the number of simple regression functions fit).
getNumResultsets() - Method in class weka.experiment.PairedTTester: Gets the number of resultsets in the data.
getNumRules() - Method in class weka.associations.Apriori: Get the value of numRules.
getNumRules() - Method in class weka.associations.PredictiveApriori: Get the value of the number of required rules.
getNumRuns() - Method in class weka.classifiers.meta.LogitBoost: Get the value of NumRuns.
getNumSamplesPerRegion() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the number of points to sample from a region (fixed dimensions).
getNumSubCmtys() - Method in class weka.classifiers.meta.MultiBoostAB: Get the number of sub committees to use
getNumSymbols() - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Gets the number of symbols this estimator operates with
getNumSymbols() - Method in class weka.estimators.DiscreteEstimator: Gets the number of symbols this estimator operates with
getNumToSelect() - Method in class weka.attributeSelection.GreedyStepwise: Gets the number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.RaceSearch: Gets the number of attributes to be retained.
getNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets the user specified number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.Ranker: Gets the number of attributes to be retained.
getNumTraining() - Method in class weka.classifiers.lazy.IBk: Get the number of training instances the classifier is currently using
getNumTrees() - Method in class weka.classifiers.trees.RandomForest: Get the value of numTrees.
getNumXValFolds() - Method in class weka.classifiers.meta.ThresholdSelector: Get the number of folds used for cross-validation.
getObject() - Method in class weka.core.SerializedObject: Returns a serialized object.
getObservedFrequency() - Method in class weka.associations.tertius.Rule: Get the observed frequency of counter-instances of this rule in the dataset.
getObservedNumber() - Method in class weka.associations.tertius.Rule: Get the observed number of counter-instances of this rule in the dataset.
getOnDemandDirectory() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.classifiers.meta.MetaCost: Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns the directory that will be searched for cost files when loading on demand.
getOnlyAlphabeticTokens() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the tokens are to be formed only from contiguous alphabetic sequences.
getOptimistic() - Method in class weka.associations.tertius.Rule: Get the optimistic estimate of the confirmation obtained by refining this rule.
getOptimizations() - Method in class weka.classifiers.rules.JRip
getOption(char, String[]) - Static method in class weka.core.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOption(String, String[]) - Static method in class weka.core.Utils: Gets an option indicated by a flag "-String" from the given array of strings.
getOptions() - Method in class weka.associations.Apriori: Gets the current settings of the Apriori object.
getOptions() - Method in class weka.associations.PredictiveApriori: Gets the current settings of the PredictiveApriori object.
getOptions() - Method in class weka.associations.Tertius: Gets the current settings of the Tertius object.
getOptions() - Method in class weka.attributeSelection.BestFirst: Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.CfsSubsetEval: Gets the current settings of CfsSubsetEval
getOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval: Gets the current settings of ClassifierSubsetEval
getOptions() - Method in class weka.attributeSelection.ExhaustiveSearch: Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.GeneticSearch: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.GreedyStepwise: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.OneRAttributeEval
getOptions() - Method in class weka.attributeSelection.PrincipalComponents: Gets the current settings of PrincipalComponents
getOptions() - Method in class weka.attributeSelection.RaceSearch: Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.RandomSearch: Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.Ranker: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.RankSearch: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.SVMAttributeEval: Gets the current settings of SVMAttributeEval
getOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.WrapperSubsetEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.classifiers.bayes.AODE: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.BayesNet: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.NaiveBayes: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.BayesNetGenerator: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.BayesNetEstimator: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.BMAEstimator: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.estimate.MultiNomialBMAEstimator: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.fixed.FromFile: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.GlobalScoreSearchAlgorithm: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.HillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.K2: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.RepeatedHillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.SimulatedAnnealing: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.TabuSearch: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.global.TAN: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.HillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.K2: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.LocalScoreSearchAlgorithm
getOptions() - Method in class weka.classifiers.bayes.net.search.local.RepeatedHillClimber: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.SimulatedAnnealing: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.TabuSearch: Gets the current settings of the search algorithm.
getOptions() - Method in class weka.classifiers.bayes.net.search.local.TAN: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.bayes.net.search.SearchAlgorithm: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.BVDecompose: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.CheckClassifier: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.Classifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.functions.LeastMedSq: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.functions.LinearRegression: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.Logistic: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.MultilayerPerceptron: Gets the current settings of NeuralNet.
getOptions() - Method in class weka.classifiers.functions.PaceRegression: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.RBFNetwork: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SimpleLogistic: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.functions.SMO: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SMOreg: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.VotedPerceptron: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.Winnow: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.lazy.IBk: Gets the current settings of IBk.
getOptions() - Method in class weka.classifiers.lazy.KStar: Gets the current settings of K*.
getOptions() - Method in class weka.classifiers.lazy.LWL: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.meta.AdaBoostM1: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AdditiveRegression: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Bagging: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.CVParameterSelection: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Decorate: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.FilteredClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.LogitBoost: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MetaCost: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiBoostAB: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiScheme: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RegressionByDiscretization: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Stacking: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.misc.VFI: Gets the current settings of VFI
getOptions() - Method in class weka.classifiers.MultipleClassifiersCombiner: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.RandomizableClassifier: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.ConjunctiveRule: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.DecisionTable: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.JRip: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.NNge: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.rules.OneR: Gets the current settings of the OneR classifier.
getOptions() - Method in class weka.classifiers.rules.PART: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.Ridor: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.SingleClassifierEnhancer: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.ADTree: Gets the current settings of ADTree.
getOptions() - Method in class weka.classifiers.trees.J48: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.LMT: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.m5.M5Base: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.M5P: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.RandomForest: Gets the current settings of the forest.
getOptions() - Method in class weka.classifiers.trees.RandomTree: Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.REPTree: Gets options from this classifier.
getOptions() - Method in class weka.clusterers.Cobweb: Gets the current settings of Cobweb.
getOptions() - Method in class weka.clusterers.EM: Gets the current settings of EM.
getOptions() - Method in class weka.clusterers.FarthestFirst: Gets the current settings of FarthestFirst
getOptions() - Method in class weka.clusterers.MakeDensityBasedClusterer: Gets the current settings of the clusterer.
getOptions() - Method in class weka.clusterers.SimpleKMeans: Gets the current settings of SimpleKMeans
getOptions() - Method in class weka.core.converters.AbstractFileSaver: Gets the current settings of the Saver object.
getOptions() - Method in class weka.core.converters.C45Saver: Gets the current settings of the C45Saver object.
getOptions() - Method in class weka.core.converters.DatabaseLoader: Gets the setting
getOptions() - Method in class weka.core.converters.DatabaseSaver: Gets the setting
getOptions() - Method in interface weka.core.OptionHandler: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.datagenerators.BIRCHCluster: Gets the current settings of the datagenerator BIRCHCluster.
getOptions() - Method in class weka.datagenerators.RDG1: Gets the current settings of the datagenerator RDG1.
getOptions() - Method in class weka.experiment.AveragingResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CrossValidationResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.CSVResultListener: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.DatabaseResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.Experiment: Gets the current settings of the experiment iterator.
getOptions() - Method in class weka.experiment.InstanceQuery: Gets the current settings of InstanceQuery
getOptions() - Method in class weka.experiment.LearningRateResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.PairedTTester: Gets current settings of the PairedTTester.
getOptions() - Method in class weka.experiment.RandomSplitResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection: Gets the current settings for the attribute selection (search, evaluator) etc.
getOptions() - Method in class weka.filters.supervised.attribute.ClassOrder: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.Discretize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.Resample: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Add: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.ChangeDateFormat: {@inheritDoc
getOptions() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Copy: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Remove: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Normalize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Randomize: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Resample: Gets the current settings of the filter.
getOrderedFlag() - Method in class weka.datagenerators.BIRCHCluster: Gets the ordered flag (option O).
getOriginalCoords() - Method in class weka.gui.beans.MetaBean: returns the vector containing the original coordinates (instances of class Point) for the inputs
getOutput() - Method in class weka.datagenerators.ClusterGenerator: Gets the print writer.
getOutput() - Method in class weka.datagenerators.Generator: Gets the print writer.
getOutputFile() - Method in class weka.experiment.CrossValidationResultProducer: Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.CSVResultListener: Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.RandomSplitResultProducer: Get the value of OutputFile.
getOutputFilename() - Method in class weka.gui.GenericPropertiesCreator: returns the name of the output file
getOutputFormat() - Method in class weka.filters.Filter: Gets the format of the output instances.
getOutputFormat() - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Gets the format of the output instances.
getOutputItemSets() - Method in class weka.associations.Apriori: Gets whether itemsets are output as well
getOutputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the output numbers.
getOutputProperties() - Method in class weka.gui.GenericPropertiesCreator: returns the output properties object (structure like the template, but filled with classes instead of packages)
getOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the outputs.
getOutputs() - Method in class weka.gui.beans.MetaBean
getOutputWordCounts() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether output instances contain 0 or 1 indicating word presence, or word counts.
getP() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the proportion of instances that are common between two training sets.
getPaint() - Method in class weka.gui.visualize.PostscriptGraphics
getPanel(int) - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the specified panel, null if index is out of bounds
getPanelCount() - Method in class weka.gui.arffviewer.ArffViewerMainPanel: returns the number of panels currently open
getParent(int, int) - Method in class weka.classifiers.bayes.BayesNet: get node index of a parent of a node in the network structure
getParent(int) - Method in class weka.classifiers.bayes.net.ParentSet: returns index parent of parent specified by index
getParent(int) - Method in class weka.gui.treevisualizer.Node: Get the parent edge.
getParentCardinality(int) - Method in class weka.classifiers.bayes.BayesNet: get number of values the collection of parents of a node can take
getParentSet(int) - Method in class weka.classifiers.bayes.BayesNet: get the parent set of a node
getParentSets() - Method in class weka.classifiers.bayes.BayesNet: Get full set of parent sets.
getParts() - Method in class weka.associations.tertius.IndividualInstance
getPassword() - Method in class weka.experiment.DatabaseUtils: Get the database password
getPassword() - Method in class weka.gui.DatabaseConnectionDialog: Returns password from dialog
getPath() - Method in class weka.gui.PropertySelectorDialog: Gets the path of property nodes to the selected property.
getPattern() - Method in class weka.datagenerators.BIRCHCluster: Gets the pattern type.
getPercent() - Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the size of noise data as a percentage of the original set.
getPercent() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the percent the attributes (dimensions) of the data will be reduced to
getPercentage() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets the percentage of instances to select.
getPercentCompleted() - Method in class weka.gui.boundaryvisualizer.RemoteResult: Return the progress for this row
getPercentThreshold() - Method in class weka.attributeSelection.SVMAttributeEval: Get the threshold below which percentage elimination reverts to constant elimination.
getPercentToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval: Get the percentage rate of attribute elimination per iteration
getPivot() - Method in class weka.core.matrix.LUDecomposition: Return pivot permutation vector
getPlainColumnName(int) - Method in class weka.gui.arffviewer.ArffTable: returns the basically the attribute name of the column and not the HTML column name via getColumnName(int)
getPlotInstances() - Method in class weka.gui.visualize.PlotData2D: Returns the instances for this plot
getPlotName() - Method in class weka.gui.visualize.PlotData2D: Get the name of this plot
getPlots() - Method in class weka.gui.visualize.Plot2D: Return the list of plots
getPlotTrainingData() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Returns true if training data is to be superimposed
getPointValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Gets a particular point value
getPopulationSize() - Method in class weka.attributeSelection.GeneticSearch: get the size of the population
getPopulationSize() - Method in class weka.classifiers.bayes.net.search.global.GeneticSearch
getPopulationSize() - Method in class weka.classifiers.bayes.net.search.local.GeneticSearch
getPostProcessor() - Method in class weka.classifiers.CheckClassifier: returns the current PostProcessor, can be null
getPrecision() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the precision.
getPredicate() - Method in class weka.associations.tertius.Literal
getPrediction(Classifier, Instance) - Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a single prediction for a test instance given the pre-trained classifier.
getPredTargetColumn() - Method in class weka.experiment.ClassifierSplitEvaluator
getProbabilities() - Method in class weka.gui.boundaryvisualizer.RemoteResult: Return the probability distributions for this row in the visualization
getProbability(int, int, int) - Method in class weka.classifiers.bayes.BayesNet: get particular probability of the conditional probability distribtion of a node given its parents.
getProbability(double) - Method in class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes: Get a probability estimate for a value
getProbability(double, double) - Method in interface weka.estimators.ConditionalEstimator: Get a probability for a value conditional on another value
getProbability(double, double) - Method in class weka.estimators.DDConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.DiscreteEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.DKConditionalEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.DNConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in interface weka.estimators.Estimator: Get a probability estimate for a value.
getProbability(double, double) - Method in class weka.estimators.KDConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.KernelEstimator: Get a probability estimate for a value.
getProbability(double, double) - Method in class weka.estimators.KKConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.MahalanobisEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NDConditionalEstimator: Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NNConditionalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.NormalEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.PoissonEstimator: Get a probability estimate for a value
getProduceCSV() - Method in class weka.experiment.PairedTTester: Get whether csv is output
getProduceCSV() - Method in class weka.gui.experiment.OutputFormatDialog: Get whether csv is output
getProduceLatex() - Method in class weka.experiment.PairedTTester: Get whether latex is output
getProduceLatex() - Method in class weka.gui.experiment.OutputFormatDialog: Get whether latex is output
getProgressBar() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Returns a handle to the progressBar of this LayoutEngine.
getProgressBar() - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method returns the progress bar for the LayoutEngine, which shows the progress of the layout process, if it takes a while to layout the graph
getProperties() - Static method in class weka.gui.experiment.ExperimenterDefaults: returns the associated properties file
getPropertyArray() - Method in class weka.experiment.Experiment: Gets the array of values to set the custom property to.
getPropertyArrayLength() - Method in class weka.experiment.Experiment: Gets the number of custom iterator values that have been defined for the experiment.
getPropertyArrayValue(int) - Method in class weka.experiment.Experiment: Gets a specified value from the custom property iterator array.
getPropertyDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo: Returns the property descriptors
getPropertyDescriptors() - Method in class weka.gui.beans.ClassValuePickerBeanInfo: Returns the property descriptors
getPropertyDescriptors() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo: Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.PredictionAppenderBeanInfo: Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo: Get the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.