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abortExperiment() - Method in class weka.experiment.RemoteExperiment: Set the abort flag
AbstractDataSink - class weka.gui.beans.AbstractDataSink.: Abstract class for objects that store instances to some destination.
AbstractDataSink() - Constructor for class weka.gui.beans.AbstractDataSink
AbstractDataSinkBeanInfo - class weka.gui.beans.AbstractDataSinkBeanInfo.: Bean info class for the AbstractDataSink
AbstractDataSinkBeanInfo() - Constructor for class weka.gui.beans.AbstractDataSinkBeanInfo
AbstractDataSource - class weka.gui.beans.AbstractDataSource.: 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 weka.gui.beans.AbstractDataSourceBeanInfo.: Bean info class for AbstractDataSource.
AbstractDataSourceBeanInfo() - Constructor for class weka.gui.beans.AbstractDataSourceBeanInfo
AbstractEvaluator - class weka.gui.beans.AbstractEvaluator.: Abstract class for objects that can provide some kind of evaluation for classifier, clusterers etc.
AbstractEvaluator() - Constructor for class weka.gui.beans.AbstractEvaluator: Constructor
AbstractLoader - class weka.core.converters.AbstractLoader.: Abstract class gives default implementation of setSource methods.
AbstractLoader() - Constructor for class weka.core.converters.AbstractLoader
AbstractTestSetProducer - class weka.gui.beans.AbstractTestSetProducer.: 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 weka.gui.beans.AbstractTestSetProducerBeanInfo.: BeanInfo class for AbstractTestSetProducer
AbstractTestSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTestSetProducerBeanInfo
AbstractTimeSeries - class weka.filters.unsupervised.attribute.AbstractTimeSeries.: 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 weka.gui.beans.AbstractTrainAndTestSetProducer.: 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 weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo.: Bean info class for AbstractTrainAndTestSetProducers
AbstractTrainAndTestSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
AbstractTrainingSetProducer - class weka.gui.beans.AbstractTrainingSetProducer.: 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 weka.gui.beans.AbstractTrainingSetProducerBeanInfo.: BeanInfo class for AbstractTrainingSetProducer
AbstractTrainingSetProducerBeanInfo() - Constructor for class weka.gui.beans.AbstractTrainingSetProducerBeanInfo
ACCEPT - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent: States that the user has accepted the tree.
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.
acceptClassifier(BatchClassifierEvent) - Method in interface weka.gui.beans.BatchClassifierListener: Accept a BatchClassifierEvent
acceptClassifier(BatchClassifierEvent) - Method in class weka.gui.beans.PredictionAppender: Accept and process a batch classifier event
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 class weka.gui.beans.PredictionAppender: Accept and process an incremental classifier event
acceptClassifier(IncrementalClassifierEvent) - Method in interface weka.gui.beans.IncrementalClassifierListener: Accept the event
acceptDataPoint(ChartEvent) - Method in class weka.gui.beans.StripChart: Accept a data point (encapsulated in a chart event) to plot
acceptDataPoint(ChartEvent) - Method in interface weka.gui.beans.ChartListener
acceptDataPoint(double[]) - Method in class weka.gui.beans.StripChart: Accept a data point to plot
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(DataSetEvent) - Method in class weka.gui.beans.Filter: Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.AbstractDataSink: Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.CSVDataSink
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TextViewer: Accept a data set for displaying as text
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.ClassAssigner
acceptDataSet(DataSetEvent) - Method in interface weka.gui.beans.DataSourceListener
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TestSetMaker: Accepts and processes a data set event
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Subclass must implement
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.DataVisualizer: Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker: Accept a data set
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 interface weka.gui.beans.InstanceListener: Accept and process an instance event
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 class weka.gui.beans.ClassAssigner
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.AveragingResultProducer: Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in interface weka.experiment.ResultListener: 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.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 class weka.experiment.DatabaseResultProducer: Accepts results from a ResultProducer.
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker: Accept a test set
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.Filter: Accept a test set
acceptTestSet(TestSetEvent) - Method in interface weka.gui.beans.TestSetListener: Accept and process a test set event
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.AbstractDataSink: Accept a test set
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.ClassAssigner
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.DataVisualizer: Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker: Accept a test set
acceptText(TextEvent) - Method in class weka.gui.beans.TextViewer: Accept some text
acceptText(TextEvent) - Method in interface weka.gui.beans.TextListener: Accept and process a text event
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
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.Filter: Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.AbstractDataSink: Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.TextViewer: Accept a training set for displaying as text
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.ClassAssigner
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.DataVisualizer: Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker: Accept a training set
actEntropy - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the actual entropy
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.treevisualizer.TreeVisualizer: Performs the action associated with the ActionEvent.
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.DatasetListPanel: Handle actions when buttons get pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.AlgorithmListPanel: 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.RunPanel: Controls starting and stopping the experiment.
actionPerformed(ActionEvent) - Method in class weka.gui.streams.InstanceLoader
actual() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the actual class value.
actual() - Method in interface weka.classifiers.evaluation.Prediction: Gets the actual class value.
actual() - Method in class weka.classifiers.evaluation.NumericPrediction: 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 weka.classifiers.meta.AdaBoostM1.: Class for boosting a classifier using Freund & Schapire's Adaboost M1 method.
AdaBoostM1() - Constructor for class weka.classifiers.meta.AdaBoostM1: Constructor.
Add - class weka.filters.unsupervised.attribute.Add.: An instance filter that adds a new attribute to the dataset.
ADD_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Add() - Constructor for class weka.filters.unsupervised.attribute.Add
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(double, double) - Method in class weka.experiment.PairedStats: Add an observed pair of values.
add(Instance) - Method in class weka.core.Instances: Adds one instance to the end of the set.
add(int, double[]) - Method in class weka.classifiers.trees.j48.Distribution: Adds counts to given bag.
add(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution: Adds given instance to given bag.
add(Matrix) - Method in class weka.core.Matrix: Returns the sum of this matrix with another.
add(Object) - Method in class weka.associations.tertius.SimpleLinkedList
add(String) - Method in class weka.gui.HierarchyPropertyParser: Add the given item of property to the tree
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
addActionListener(ActionListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Register a listener to be notified when plotting completes
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
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
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
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(Edge) - Method in class weka.gui.treevisualizer.Node: Set the value of children.
addChild(Splitter, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode: Adds a child to this node.
AddCluster - class weka.filters.unsupervised.attribute.AddCluster.: 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
addCVParameter(String) - Method in class weka.classifiers.meta.CVParameterSelection: Adds a scheme parameter to the list of parameters to be set by cross-validation
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Loader: Add a listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Filter: Add a data source listener
addDataSourceListener(DataSourceListener) - Method in interface weka.gui.beans.DataSource: Add a data source listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.PredictionAppender: Add a datasource listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassAssigner
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.AbstractDataSource: Add a listener
addDouble(double) - Method in class coreComponents.DoubleVector: Simply adds a double value to a vector
addDoubleToEndOfArray(double[], double) - Method in class probabilityMachine.vpm.VPMBartsRMI2
addElement(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Adds an element into the vector
addElement(int, int, double) - Method in class weka.core.Matrix: Add a value to an element.
addElement(Literal) - Method in class weka.associations.tertius.LiteralSet: Add a Literal to this set.
addElement(Object) - Method in class weka.core.FastVector: Adds an element to this vector.
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 weka.filters.unsupervised.attribute.AddExpression.: 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
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.Loader: Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.Filter: Add an instance listener
addInstanceListener(InstanceListener) - Method in interface weka.gui.beans.DataSource: Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.PredictionAppender: Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.ClassAssigner
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.AbstractDataSource: 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 weka.core.AdditionalMeasureProducer.: Interface to something that can produce measures other than those calculated by evaluation modules.
AdditiveRegression - class weka.classifiers.meta.AdditiveRegression.: 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
AddNoise - class weka.filters.unsupervised.attribute.AddNoise.: 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.ParentSet: Add parent to parent set and update internals (specifically the cardinality of the parent set)
addPatternToCounter(Instance, Instance) - Method in class coreComponents.PatternCounter
addPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel: Set a new plot to the visualize panel
addPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D: Add a plot to the list of plots to display
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(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.GenericArrayEditor: Adds a PropertyChangeListener who will be notified of value changes.
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.explorer.PreprocessPanel: Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.PredictionAppenderCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.BeanVisual: Add a listener for property change events
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.FilterCustomizer: 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.StripChartCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassAssignerCustomizer: Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.LoaderCustomizer: Add a property change listener
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(String, PropertyChangeListener) - Method in class weka.gui.beans.TextViewer: Add a property change listener to this bean
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.AbstractDataSource: Add a property change listener to this bean
addPropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.DataVisualizer: Add a property change listener to this bean
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.gui.boundaryvisualizer.BoundaryPanelDistributed: Add an object to the list of those interested in recieving update information from the RemoteExperiment
addRemoteExperimentListener(RemoteExperimentListener) - Method in class weka.experiment.RemoteExperiment: 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
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.
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.
addResult(String, StringBuffer) - Method in class weka.gui.ResultHistoryPanel: Adds a new result to the result list.
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.
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.
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.Filter: Add a test set listener
addTestSetListener(TestSetListener) - Method in interface weka.gui.beans.TestSetProducer: Add a listener for test set events
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.ClassAssigner
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Add a test set listener
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTestSetProducer: Add a listener for test sets
addTextListener(TextListener) - Method in class weka.gui.beans.Classifier: Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Add a text listener
addToList(BitSet, 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.AbstractTrainingSetProducer: Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.Filter: Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.ClassAssigner
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in interface weka.gui.beans.TrainingSetProducer: Add a training set listener
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.estimators.DiscreteEstimator: 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.
addValue(double, double) - Method in class weka.estimators.MahalanobisEstimator: 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) - Method in class weka.estimators.NormalEstimator: 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) - Method in class weka.classifiers.bayes.DiscreteEstimatorBayes: 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, double) - Method in class weka.estimators.NNConditionalEstimator: 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.KDConditionalEstimator: 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.DDConditionalEstimator: 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.DNConditionalEstimator: Add a new data value to the current estimator.
addVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.TextViewer: Add a vetoable change listener to this bean
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
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 weka.classifiers.bayes.ADNode.: 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.ADNode: Creates new ADNode
ADTree - class weka.classifiers.trees.ADTree.: 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.Scoreable
AlgorithmListPanel - class weka.gui.experiment.AlgorithmListPanel.: This panel controls setting a list of algorithms for an experiment to iterate over.
AlgorithmListPanel.ObjectCellRenderer - class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer.
AlgorithmListPanel.ObjectCellRenderer() - Constructor for class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
AlgorithmListPanel() - Constructor for class weka.gui.experiment.AlgorithmListPanel: Create the algorithm list panel initially disabled.
AlgorithmListPanel(Experiment) - Constructor for class weka.gui.experiment.AlgorithmListPanel: Creates the algorithm list panel with the given experiment.
AllFilter - class weka.filters.AllFilter.: A simple instance filter that passes all instances directly through.
AllFilter() - Constructor for class weka.filters.AllFilter
AlphaProb_SMO - class classifiers.AlphaProb_SMO.: Implements John C.
AlphaProb_SMO.BinarySMO - class classifiers.AlphaProb_SMO.BinarySMO.: Class for building a binary support vector machine.
AlphaProb_SMO.BinarySMO() - Constructor for class classifiers.AlphaProb_SMO.BinarySMO
AlphaProb_SMO() - Constructor for class classifiers.AlphaProb_SMO
alphaTipText() - Method in class weka.classifiers.bayes.BayesNet
alphaTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
AltDist_IBk - class classifiers.AltDist_IBk.: K-nearest neighbour classifier.
AltDist_IBk() - Constructor for class classifiers.AltDist_IBk: IB1 classifer.
AltDist_IBk(int) - Constructor for class classifiers.AltDist_IBk: IBk classifier.
AODE - class weka.classifiers.bayes.AODE.: 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.ListSelectorDialog: Signifies an OK property selection
APPROVE_OPTION - Static variable in class weka.gui.PropertySelectorDialog: Signifies an OK property selection
Apriori - class weka.associations.Apriori.: 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.
ArffCreator - class coreComponents.ArffCreator.: A very messy class used to convert/create arff files
ArffCreator() - Constructor for class coreComponents.ArffCreator
ArffLoader - class weka.core.converters.ArffLoader.: Reads a source that is in arff text format.
ArffLoader() - Constructor for class weka.core.converters.ArffLoader
arrayLeftDivide(Matrix) - Method in class weka.classifiers.functions.pace.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
arrayRightDivide(Matrix) - Method in class weka.classifiers.functions.pace.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
arrayTimes(Matrix) - Method in class weka.classifiers.functions.pace.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
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 weka.attributeSelection.ASEvaluation.: Abstract attribute selection evaluation class
ASEvaluation() - Constructor for class weka.attributeSelection.ASEvaluation
ASSearch - class weka.attributeSelection.ASSearch.: 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
assignVennType(double, int) - Method in class probabilityMachine.vpm.VPMBartsRMI2: This is the function that sets the type depending on the number of deviations from the mean! (This need not be symmetric about the mean!)
AssociationsPanel - class weka.gui.explorer.AssociationsPanel.: 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 weka.associations.Associator.: Abstract scheme for learning associations.
Associator() - Constructor for class weka.associations.Associator
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.BinC45Split: 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 weka.core.Attribute.: Class for handling an attribute.
attribute(int) - Method in class weka.core.Instance: Returns the attribute with the given index.
attribute(int) - Method in class weka.core.Instances: Returns an attribute.
attribute(String) - Method in class weka.core.Instances: Returns an attribute given its name.
Attribute(String) - Constructor for class weka.core.Attribute: Constructor for a numeric attribute.
Attribute(String, FastVector) - Constructor for class weka.core.Attribute: Constructor for nominal attributes and string attributes.
Attribute(String, FastVector, ProtectedProperties) - Constructor for class weka.core.Attribute: Constructor for nominal attributes and string attributes, where metadata is supplied.
Attribute(String, 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.
AttributeEvaluator - class weka.attributeSelection.AttributeEvaluator.: Abstract attribute evaluator.
AttributeEvaluator() - Constructor for class weka.attributeSelection.AttributeEvaluator
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RankSearch: Returns the tip text for this property
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.StringToNominal
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.Add: Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
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.Remove: 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.FirstOrder: 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.Discretize: Returns the tip text for this property
AttributeListPanel - class weka.gui.AttributeListPanel.: 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.
attributeNames() - Method in class classifiers.PC_SMO: Returns the attribute names.
attributeNames() - Method in class classifiers.AlphaProb_SMO: Returns the attribute names.
attributeNameTipText() - Method in class weka.filters.unsupervised.attribute.Add: Returns the tip text for this property
AttributePanel - class weka.gui.visualize.AttributePanel.: 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 weka.gui.visualize.AttributePanelEvent.: 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 weka.gui.visualize.AttributePanelListener.: Interface for classes that want to listen for Attribute selection changes in the attribute panel
AttributeSelectedClassifier - class weka.classifiers.meta.AttributeSelectedClassifier.: Class for running an arbitrary classifier on data that has been reduced through attribute selection.
AttributeSelectedClassifier() - Constructor for class weka.classifiers.meta.AttributeSelectedClassifier
AttributeSelection - class weka.filters.supervised.attribute.AttributeSelection.: Filter for doing attribute selection.
AttributeSelection - class weka.attributeSelection.AttributeSelection.: Attribute selection class.
AttributeSelection() - Constructor for class weka.filters.supervised.attribute.AttributeSelection: Constructor
AttributeSelection() - Constructor for class weka.attributeSelection.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 weka.gui.AttributeSelectionPanel.: 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 - class weka.gui.explorer.AttributeSelectionPanel.: 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.AttributeSelectionPanel: Creates the attribute selection panel with no initial instances.
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 weka.core.AttributeStats.: 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.TwoWayNumericSplit: 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.
AttributeSummarizer - class weka.gui.beans.AttributeSummarizer.: 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 weka.gui.beans.AttributeSummarizerBeanInfo.: Bean info class for the attribute summarizer bean
AttributeSummarizerBeanInfo() - Constructor for class weka.gui.beans.AttributeSummarizerBeanInfo
AttributeSummaryPanel - class weka.gui.AttributeSummaryPanel.: 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 weka.attributeSelection.AttributeTransformer.: Abstract attribute transformer.
attributeTypeTipText() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns the tip text for this property
AttributeValueLiteral - class weka.associations.tertius.AttributeValueLiteral.
AttributeValueLiteral(Predicate, String, int, int, int) - Constructor for class weka.associations.tertius.AttributeValueLiteral
AttributeVisualizationPanel - class weka.gui.AttributeVisualizationPanel.: Creates a panel that shows a visualization of an attribute in a dataset.
AttributeVisualizationPanel() - Constructor for class weka.gui.AttributeVisualizationPanel
AttributeVisualizationPanel(boolean) - Constructor for class weka.gui.AttributeVisualizationPanel
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
AveragingResultProducer - class weka.experiment.AveragingResultProducer.: 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.
avgCost() - Method in class evaluationMethods.EstimatorEvaluation: 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 weka.classifiers.meta.Bagging.: 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
BartsRMI - class classifiers.stbarts.BartsRMI.: Simple classifier only working with ovarian cancer data.
BartsRMI() - Constructor for class classifiers.stbarts.BartsRMI
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 weka.gui.beans.BatchClassifierEvent.: Class encapsulating a built classifier and a batch of instances to test on.
BatchClassifierEvent(Object, Classifier, Instances, int, int) - Constructor for class weka.gui.beans.BatchClassifierEvent: Creates a new BatchClassifierEvent instance.
BatchClassifierListener - interface weka.gui.beans.BatchClassifierListener.: Interface to something that can process a BatchClassifierEvent
batchFilterFile(Filter, String[]) - Static method in class weka.filters.Filter: Method for testing filters ability to process multiple batches.
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.InstanceViewer
batchFinished() - Method in class weka.gui.streams.InstanceTable
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.NominalToBinary: 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.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.RemoveType: 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.RemoveUseless: Signify 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.StringToWordVector: Signify 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.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.AbstractTimeSeries: Signifies 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.ReplaceMissingValues: 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.Discretize: Signifies 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.RemovePercentage: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.Resample: Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.Randomize: 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.RemoveRange: Signify that this batch of input to the filter is finished.
BatchLoader - interface weka.core.converters.BatchLoader.: Marker interface for a loader that can retrieve instances in batch mode
BAYES - Static variable in interface weka.classifiers.bayes.Scoreable: score types
BayesNet - class weka.classifiers.bayes.BayesNet.: Base class for a Bayes Network classifier.
BayesNet - Static variable in interface weka.core.Drawable
BayesNet() - Constructor for class weka.classifiers.bayes.BayesNet
BayesNetB - class weka.classifiers.bayes.BayesNetB.: Class for a Bayes Network classifier based on a hill climbing algorithm for learning structure as described in Buntine, W.
BayesNetB() - Constructor for class weka.classifiers.bayes.BayesNetB
BayesNetB2 - class weka.classifiers.bayes.BayesNetB2.: Class for a Bayes Network classifier based on Buntines hill climbing algorithm for learning structure, but augmented to allow arc reversal as an operation.
BayesNetB2() - Constructor for class weka.classifiers.bayes.BayesNetB2
BayesNetK2 - class weka.classifiers.bayes.BayesNetK2.: Class for a Bayes Network classifier based on K2 for learning structure.
BayesNetK2() - Constructor for class weka.classifiers.bayes.BayesNetK2
BEAN_EXECUTING - Static variable in class weka.gui.beans.BeanInstance
BeanCommon - interface weka.gui.beans.BeanCommon.: 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 weka.gui.beans.BeanConnection.: 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 weka.gui.beans.BeanInstance.: 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 weka.gui.beans.BeanVisual.: BeanVisual encapsulates icons and label for a given bean.
BeanVisual(String, String, String) - Constructor for class weka.gui.beans.BeanVisual: Constructor
BestFirst - class weka.attributeSelection.BestFirst.: Class for performing a best first search.
BestFirst.Link2 - class weka.attributeSelection.BestFirst.Link2.: Class for a node in a linked list.
BestFirst.Link2(BitSet, double) - Constructor for class weka.attributeSelection.BestFirst.Link2
BestFirst.LinkedList2 - class weka.attributeSelection.BestFirst.LinkedList2.: Class for handling a linked list.
BestFirst.LinkedList2(int) - Constructor for class weka.attributeSelection.BestFirst.LinkedList2
BestFirst() - Constructor for class weka.attributeSelection.BestFirst: Constructor
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.
bias() - Method in class classifiers.PC_SMO: Returns the bias of each binary SMO.
bias() - Method in class classifiers.AlphaProb_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
BIFFormatException - exception weka.gui.graphvisualizer.BIFFormatException.: This is the Exception thrown by BIFParser, if there was an error in parsing the XMLBIF string or input stream.
BIFFormatException(String) - Constructor for class weka.gui.graphvisualizer.BIFFormatException
BIFParser - class weka.gui.graphvisualizer.BIFParser.: This class parses an inputstream or a string in XMLBIF ver.
BIFParser(InputStream, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.BIFParser: Constructor (if our input is an InputStream)
BIFParser(String, FastVector, FastVector) - Constructor for class weka.gui.graphvisualizer.BIFParser: Constructor (if our input is a String)
binarizeNumericAttributesTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns the tip text for this property
binarizeNumericAttributesTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: 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 weka.core.BinarySparseInstance.: Class for storing a binary-data-only instance as a sparse vector.
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(Instance) - Constructor for class weka.core.BinarySparseInstance: Constructor that generates a sparse instance from the given instance.
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.
BinarySparseInstance(SparseInstance) - Constructor for class weka.core.BinarySparseInstance: Constructor that copies the info from the given instance.
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
BinC45ModelSelection - class weka.classifiers.trees.j48.BinC45ModelSelection.: 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 weka.classifiers.trees.j48.BinC45Split.: 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.
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.PKIDiscretize: Returns the tip text for this property
binsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
BIRCHCluster - class weka.datagenerators.BIRCHCluster.: 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.
Body - class weka.associations.tertius.Body.: Class representing the body of a rule.
Body() - Constructor for class weka.associations.tertius.Body: Constructor without storing the counter-instances.
Body(Instances) - Constructor for class weka.associations.tertius.Body: Constructor storing the counter-instances.
bodyContains(Literal) - Method in class weka.associations.tertius.Rule: Test if the body of the rule contains a literal.
BOOL - Static variable in class weka.experiment.DatabaseUtils
boost() - Method in class weka.classifiers.trees.ADTree: Performs a single boosting iteration, using two-class optimized method.
BoundaryPanel - class weka.gui.boundaryvisualizer.BoundaryPanel.: BoundaryPanel.
BoundaryPanel(int, int) - Constructor for class weka.gui.boundaryvisualizer.BoundaryPanel: Creates a new BoundaryPanel instance.
BoundaryPanelDistributed - class weka.gui.boundaryvisualizer.BoundaryPanelDistributed.: 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 weka.gui.boundaryvisualizer.BoundaryVisualizer.: BoundaryVisualizer.
BoundaryVisualizer() - Constructor for class weka.gui.boundaryvisualizer.BoundaryVisualizer: Creates a new BoundaryVisualizer instance.
boundsFileTipText() - Method in class weka.classifiers.misc.FLR: Returns the tip text for this property
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.TwoWayNumericSplit: 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.
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.Tertius: Method that launches the search to find the rules with the highest confirmation.
buildAssociations(Instances) - Method in class weka.associations.Associator: Generates an associator.
buildClassifier(Instances) - Method in class weka.classifiers.Classifier: Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Stump method for building the classifiers.
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.lazy.IB1: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.KStar: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.IBk: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Bagging: Bagging method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.CVParameterSelection: Generates the 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.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.ClassificationViaRegression: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.ThresholdSelector: Generates the 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.MultiClassClassifier: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.AdditiveRegression: Build the classifier on the supplied data
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.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.MultiBoostAB: Method for building this classifier.
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.Decorate: Build Decorate classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.AdaBoostM1: Boosting method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RandomCommittee: Builds the committee of randomizable classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.LogitBoost: Builds the boosted classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RegressionByDiscretization: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.OrdinalClassClassifier: Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.misc.HyperPipes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.misc.FLR: Builds the FLR Classifier
buildClassifier(Instances) - Method in class weka.classifiers.misc.VFI: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.BayesNet: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.AODE: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesSimple: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.ZeroR: 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.ConjunctiveRule: Builds a single rule learner with REP dealing with nominal classes or numeric classes.
buildClassifier(Instances) - Method in class weka.classifiers.rules.OneR: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.Prism: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.PART: Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.DecisionTable: 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.NNge: Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.part.MakeDecList: Builds dec list.
buildClassifier(Instances) - Method in class weka.classifiers.trees.J48: 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.RandomForest: 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.UserClassifier: Call this function to build a decision tree for the training data provided.
buildClassifier(Instances) - Method in class weka.classifiers.trees.Id3: Builds Id3 decision tree classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.REPTree: Builds classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.LMT: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomTree: Builds 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.PreConstructedLinearModel: Builds the classifier.
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.m5.M5Base: Generates the classifier.
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.C45Split: Creates a C4.5-type split on the given data.
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.C45PruneableClassifierTree: Method for building a pruneable classifier tree.
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.j48.ClassifierSplitModel: Builds the classifier split model for the given set of instances.
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.lmt.ResidualSplit: Method not in use
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) - Method in class weka.classifiers.functions.LinearRegression: Builds a regression model for the given data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SimpleLogistic: Builds the logistic regression using LogitBoost.
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.LeastMedSq: Build lms regression
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.VotedPerceptron: Builds the ensemble of perceptrons.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SMO: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Winnow: Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.SMOreg: Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Logistic: Builds the classifier
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 confidenceMachine.tcm.TCMBartsRMI: Generates the classifier.
buildClassifier(Instances) - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Generates the classifier.
buildClassifier(Instances) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Generates the classifier.
buildClassifier(Instances) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Generates the classifier.
buildClassifier(Instances) - Method in class probabilityMachine.VPMDistMetaLearner: Generates the classifier.
buildClassifier(Instances) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Generates the classifier.
buildClassifier(Instances) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Generates the classifier.
buildClassifier(Instances) - Method in class probabilityMachine.vpm.VPMBartsRMI: Generates the classifier.
buildClassifier(Instances) - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Generates the classifier.
buildClassifier(Instances) - Method in class classifiers.PC_SMO: Method for building the classifier.
buildClassifier(Instances) - Method in class classifiers.AlphaProb_SMO: Method for building the classifier.
buildClassifier(Instances) - Method in class classifiers.AltDist_IBk: Generates the classifier.
buildClassifier(Instances) - Method in class classifiers.stbarts.BartsRMI: Generates the classifier.
buildClassifier(Instances, double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Builds the split.
buildClusterer(Instances) - Method in class weka.clusterers.Clusterer: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.SimpleKMeans: 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.EM: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.FarthestFirst: Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.Cobweb: Builds the 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
buildEvaluator(Instances) - Method in class weka.attributeSelection.InfoGainAttributeEval: Initializes an information gain attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.CfsSubsetEval: Generates a attribute evaluator.
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.ChiSquaredAttributeEval: Initializes a chi-squared attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.GainRatioAttributeEval: Initializes a gain ratio attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ConsistencySubsetEval: Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.PrincipalComponents: Initializes principal components and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.ASEvaluation: Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.OneRAttributeEval: Initializes a OneRAttribute attribute 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.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval: Generates a attribute evaluator.
buildGenerator(Instances) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Initialize the generator using the supplied instances
buildGenerator(Instances) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Build the data generator
buildLogisticModelsTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
buildLogisticModelsTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
buildLogisticModelsTipText() - Method in class classifiers.AlphaProb_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.BayesNetK2: 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() - Method in class weka.classifiers.bayes.BayesNet: buildStructure determines the network structure/graph of the network.
buildStructure() - Method in class weka.classifiers.bayes.BayesNetB: buildStructure determines the network structure/graph of the network with Buntines greedy hill climbing algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
buildStructure() - Method in class weka.classifiers.bayes.BayesNetB2: buildStructure determines the network structure/graph of the network with Buntines greedy hill climbing algorithm, restricted by its initial structure (which can be an empty graph, or a Naive Bayes graph.
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 weka.classifiers.BVDecompose.: Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
BVDecompose() - Constructor for class weka.classifiers.BVDecompose
BVDecomposeSegCVSub - class weka.classifiers.BVDecomposeSegCVSub.: 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.experiment.DatabaseUtils

C

C45Loader - class weka.core.converters.C45Loader.: Reads C4.5 input files.
C45Loader() - Constructor for class weka.core.converters.C45Loader
C45ModelSelection - class weka.classifiers.trees.j48.C45ModelSelection.: 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 weka.classifiers.trees.j48.C45PruneableClassifierTree.: 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 weka.classifiers.rules.part.C45PruneableDecList.: 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.
C45Split - class weka.classifiers.trees.j48.C45Split.: 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.
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
cacheSizeTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
cacheSizeTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
calcGraph() - Method in class weka.gui.AttributeVisualizationPanel
calcOutOfBagTipText() - Method in class weka.classifiers.meta.Bagging: Returns the tip text for this property
calculateAlphas() - Method in class weka.classifiers.trees.lmt.LMTNode: Updates the alpha field for all nodes.
calculateComplexityAndComplexityStarOf(Instance, Attribute) - Method in class classifiers.usm.distance.USMStringDistance: Calculates the first K(x) and second order K(x*) complexities of an object x
calculateComplexityAndComplexityStarOf(Instance, Attribute) - Method in class classifiers.usm.distance.USMWavDistance: Calculates the first K(x) and second order K(x*) complexities of an object x
calculateComplexityAndComplexityStarOf(Instance, Attribute) - Method in class classifiers.usm.distance.USMDistanceFunction: Calculates the first K(x) and second order K(x*) complexities of an object x
calculateConcatenatedComplexityOf(Instance, Instance, USMComplexityCache) - Method in class classifiers.usm.distance.USMStringDistance: Calculates the first K(xy*) complexity of an object x with a compressed object y*
calculateConcatenatedComplexityOf(Instance, Instance, USMComplexityCache) - Method in class classifiers.usm.distance.USMWavDistance: Calculates the first K(xy*) complexity of an object x with a compressed object y*
calculateConcatenatedComplexityOf(Instance, Instance, USMComplexityCache) - Method in class classifiers.usm.distance.USMDistanceFunction: Calculates the first K(xy*) complexity of an object x with a compressed object y*
calculateConfidenceAndCredibility(double[]) - Static method in class confidenceMachine.ConfidenceClassifier: Calculates the pseudo-probabilistic measures using the p-values calculated for an instance like so: 'Confidence' = 1-2nd largest p-value (percentage) 'Credibility' = = Largest p-value (percentage)
calculateConfidencePerformanceStatistics(double) - Method in class evaluationMethods.OnlineEvaluation: Creates an array of numbers reporting the performance of the p-values of each prediction at a set significance level.
calculateConfirmation() - Method in class weka.associations.tertius.Rule: Calculate the confirmation of this rule.
calculateDerived() - Method in class weka.experiment.Stats: Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateDerived() - Method in class weka.experiment.PairedStatsCorrected: Calculates the derived statistics (significance etc).
calculateDerived() - Method in class weka.experiment.PairedStats: Calculates the derived statistics (significance etc).
CalculateLoss - class evaluationMethods.CalculateLoss.: Create Zadrozny's reliability curves from a matlab file containing probabilities.
CalculateLoss() - Constructor for class evaluationMethods.CalculateLoss
calculateLossFile(String) - Static method in class evaluationMethods.CalculateLoss: Method for calculating Square Loss (decomposed) and log loss
calculateOptimistic() - Method in class weka.associations.tertius.Rule: Calculate the optimistic estimate of this rule.
calculatePValue(double[], double) - Static method in class confidenceMachine.ConfidenceClassifier: Calculate the p-value using the formula described by Vovk et al.
calculatePValue(double[], double, double) - Static method in class confidenceMachine.ConfidenceClassifier: Calculate the randomised p-value using the formula manipulating the strangeness values for training and test example described by Vovk et al.
calculateRMI(Instance) - Method in class confidenceMachine.tcm.TCMBartsRMI: Calculates the Risk of Malignancy Index find ref ? (Jacobs et al)
calculateRMI(Instance) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Calculates the Risk of Malignancy Index find ref ? (Jacobs et al)
calculateRMI(Instance) - Method in class probabilityMachine.vpm.VPMBartsRMI: Calculates the Risk of Malignancy Index find ref ? (Jacobs et al)
calculateRMI(Instance) - Method in class classifiers.stbarts.BartsRMI: Calculates the Risk of Malignancy Index find ref ? (Jacobs et al)
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedTTester: Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedCorrectedTTester: 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
calculateTentativeDistance(Instance, Instance) - Method in interface coreComponents.NonExchangeableDistance: Calculate the tentative instance of the classifier.
calculateTentativeDistance(Instance, Instance) - Method in class classifiers.vdm.ValueDifferenceMetric: Calculate the tentative instance of the classifier.
calculateTypesForExamples(Instances, double[][]) - Method in class probabilityMachine.VPMDistMetaLearner: This function will calculate the types for each example using the Gaussians calculated above.
calculateTypesForExamples(Instances, double[][]) - Method in class probabilityMachine.vpm.VPMNaiveBayes: This function will calculate the types for each example using the Gaussians calculated above.
calculateTypesForExamples(Instances, Instances) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: This function will calculate the types for each example using the Gaussians calculated above.
calculateTypesForExamples(Instances, Instances[], int[][]) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: This function will calculate the types for each example using the Gaussians calculated above.
calculateUSM(double, double, double, double, double, double) - Method in class classifiers.usm.distance.USMDistanceFunction: Function to compute the USM distance from the respective complexity estmiates from the instances x and y.
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
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.
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.
capacity() - Method in class weka.core.FastVector: Returns the capacity of the vector.
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
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 weka.attributeSelection.CfsSubsetEval.: CFS attribute subset evaluator.
CfsSubsetEval() - Constructor for class weka.attributeSelection.CfsSubsetEval: Constructor
ChartEvent - class weka.gui.beans.ChartEvent.: Event encapsulating info for plotting a data point on the StripChart
ChartEvent(Object) - Constructor for class weka.gui.beans.ChartEvent: Creates a new ChartEvent instance.
ChartEvent(Object, Vector, double, double, double[], boolean) - Constructor for class weka.gui.beans.ChartEvent: Creates a new ChartEvent instance.
ChartListener - interface weka.gui.beans.ChartListener.: 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.
checkBounds() - Method in class weka.classifiers.misc.FLR: Checks the metric space
CheckClassifier - class weka.classifiers.CheckClassifier.: Class for examining the capabilities and finding problems with classifiers.
CheckClassifier() - Constructor for class weka.classifiers.CheckClassifier
checkErrorRateTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
checkForMissing(Instance, Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if an instance has a missing value.
checkForMissing(Instances) - Method in class weka.classifiers.functions.PaceRegression: Checks if instances have 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.
checkInstance(Instance) - Method in class classifiers.usm.distance.USMStringDistance: Function to check that an individual instance is of the correct format.
checkInstance(Instance) - Method in class classifiers.usm.distance.USMWavDistance: Function to check that an individual instance is of the correct format.
checkInstance(Instance) - Method in class classifiers.usm.distance.USMDistanceFunction: Function to check that an individual instance is of the correct format.
checkInstances() - Method in class coreComponents.EuclideanDistanceMetric: Checks the instances.
checkInstances(Instances) - Method in class coreComponents.EuclideanDistanceMetric: Check if the instances are valid for the distance metric.
checkInstances(Instances) - Method in class coreComponents.DistanceMetric: Check if the instances are valid for the distance metric.
checkInstances(Instances) - Method in class classifiers.vdm.ValueDifferenceMetric
checkInstances(Instances) - Method in class classifiers.usm.distance.USMStringDistance: Function to check that the data instances are in the correct format.
checkInstances(Instances) - Method in class classifiers.usm.distance.USMWavDistance: Function to check that the data instances are in the correct format.
checkInstances(Instances) - Method in class classifiers.usm.distance.USMDistanceFunction: Function to check that the data instances are in the correct format.
checkModel() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Checks if generated model is valid.
checkModel(int) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Checks if there are at least 2 subsets that contain >= minNumInstances.
CheckOptionHandler - class weka.core.CheckOptionHandler.: 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 class weka.experiment.RemoteEngine: Returns status information on a particular task
checkStatus(Object) - Method in interface weka.experiment.Compute: Check on the status of a 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 weka.classifiers.functions.pace.ChisqMixture.: 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 weka.attributeSelection.ChiSquaredAttributeEval.: 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.
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.
ClassAssigner - class weka.gui.beans.ClassAssigner.: Describe class ClassAssigner here.
ClassAssigner() - Constructor for class weka.gui.beans.ClassAssigner
ClassAssignerBeanInfo - class weka.gui.beans.ClassAssignerBeanInfo.: BeanInfo class for the class assigner bean
ClassAssignerBeanInfo() - Constructor for class weka.gui.beans.ClassAssignerBeanInfo
ClassAssignerCustomizer - class weka.gui.beans.ClassAssignerCustomizer.: GUI customizer for the class assigner bean
ClassAssignerCustomizer() - Constructor for class weka.gui.beans.ClassAssignerCustomizer
classAttribute() - Method in class weka.core.Instance: Returns class attribute.
classAttribute() - Method in class weka.core.Instances: Returns the class attribute.
classAttributeNames() - Method in class weka.classifiers.functions.SMO
classAttributeNames() - Method in class classifiers.PC_SMO
classAttributeNames() - Method in class classifiers.AlphaProb_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 weka.classifiers.meta.ClassificationViaRegression.: Class for doing classification using regression methods.
ClassificationViaRegression() - Constructor for class weka.classifiers.meta.ClassificationViaRegression: Default constructor.
Classifier - class weka.gui.beans.Classifier.: Bean that wraps around weka.classifiers
Classifier - class weka.classifiers.Classifier.: Abstract classifier.
Classifier() - Constructor for class weka.gui.beans.Classifier: Creates a new Classifier instance.
Classifier() - Constructor for class weka.classifiers.Classifier
ClassifierBeanInfo - class weka.gui.beans.ClassifierBeanInfo.: BeanInfo class for the Classifier wrapper bean
ClassifierBeanInfo() - Constructor for class weka.gui.beans.ClassifierBeanInfo
ClassifierCustomizer - class weka.gui.beans.ClassifierCustomizer.: GUI customizer for the classifier wrapper bean
ClassifierCustomizer() - Constructor for class weka.gui.beans.ClassifierCustomizer
ClassifierDecList - class weka.classifiers.rules.part.ClassifierDecList.: 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 weka.gui.explorer.ClassifierPanel.: 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 weka.gui.beans.ClassifierPerformanceEvaluator.: A bean that evaluates the performance of batch trained classifiers
ClassifierPerformanceEvaluator() - Constructor for class weka.gui.beans.ClassifierPerformanceEvaluator
ClassifierPerformanceEvaluatorBeanInfo - class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo.: Bean info class for the classifier performance evaluator
ClassifierPerformanceEvaluatorBeanInfo() - Constructor for class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
classifiers - package classifiers
classifiers.stbarts - package classifiers.stbarts
classifiers.usm.distance - package classifiers.usm.distance
classifiers.vdm - package classifiers.vdm
classifiers() - Method in class weka.classifiers.meta.LogitBoost: Returns the array of classifiers that have been built.
ClassifierSplitEvaluator - class weka.experiment.ClassifierSplitEvaluator.: 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 weka.classifiers.trees.j48.ClassifierSplitModel.: 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.MultipleClassifiersCombiner: Returns the tip text for this property
classifiersTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
ClassifierSubsetEval - class weka.attributeSelection.ClassifierSubsetEval.: Classifier subset evaluator.
ClassifierSubsetEval() - Constructor for class weka.attributeSelection.ClassifierSubsetEval
classifierTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: 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.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
classifierTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval: 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.classifiers.meta.RacedIncrementalLogitBoost
classifierTipText() - Method in class weka.classifiers.meta.ThresholdSelector
classifierTipText() - Method in class weka.classifiers.meta.FilteredClassifier: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
classifierTipText() - Method in class weka.classifiers.meta.AdditiveRegression: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.Decorate: Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
classifierTipText() - Method in class weka.classifiers.meta.OrdinalClassClassifier
ClassifierTree - class weka.classifiers.trees.j48.ClassifierTree.: 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.Classifier: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.lazy.IB1: Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.MetaCost: Classifies a 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.misc.FLR: Classifies a given instance using the FLR Classifier model
classifyInstance(Instance) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.ZeroR: 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.Prism: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.PART: Classifies an 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.NNge: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.MakeDecList: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.J48: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.Id3: Classifies a given test instance using the decision tree.
classifyInstance(Instance) - Method in class weka.classifiers.trees.LMT: Classifies an instance.
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.PreConstructedLinearModel: Predicts the class of the supplied instance using the linear model.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.RuleNode: Classify an instance using this node.
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.j48.ClassifierTree: Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Classifies a given instance.
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.SimpleLinearRegression: Generate a prediction for the supplied instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LeastMedSq: Classify a given instance using the best generated LinearRegression Classifier.
classifyInstance(Instance) - Method in class weka.classifiers.functions.Winnow: Outputs the prediction for the given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.SMOreg: Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.PaceRegression: Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class confidenceMachine.ConfidenceClassifier: Classifies the given test instance.
classifyInstance(Instance) - Method in class probabilityMachine.VennProbabilityClassifier: Classifies the given test 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.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
classIndexTipText() - Method in class weka.associations.Tertius: 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 weka.filters.supervised.attribute.ClassOrder.: 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 weka.gui.visualize.ClassPanel.: 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.C45Split: Gets class probability for instance.
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.ClassifierSplitModel: Gets class probability for instance.
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.
clean() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Frees the cache used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.Kernel: Frees the memory used by the kernel.
clean() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Frees the cache used by the kernel.
cleanup() - Method in class weka.classifiers.trees.j48.C45ModelSelection: Sets reference to training data to null.
cleanup() - Method in class weka.classifiers.trees.j48.BinC45ModelSelection: 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
cleanup(Instances) - Method in class weka.classifiers.rules.part.ClassifierDecList: Cleanup in order to save memory.
cleanup(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree: Cleanup in order to save memory.
clear() - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
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.
clone() - Method in class weka.core.Matrix: Creates and returns a clone of this object.
clone() - Method in class weka.associations.tertius.Rule: Returns a shallow copy of this rule.
clone() - Method in class weka.associations.tertius.LiteralSet: Returns a shallow copy of this set.
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.j48.Distribution: Clones distribution (Deep copy of distribution).
clone() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Allows to clone a model (shallow copy).
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.TwoWayNumericSplit: Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Clones this node.
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.PaceMatrix: Clone the PaceMatrix 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.
Clusterer - class weka.clusterers.Clusterer.: Abstract clusterer.
Clusterer() - Constructor for class weka.clusterers.Clusterer
ClustererPanel - class weka.gui.explorer.ClustererPanel.: 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
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 weka.clusterers.ClusterEvaluation.: Class for evaluating clustering models.
ClusterEvaluation() - Constructor for class weka.clusterers.ClusterEvaluation: Constructor.
ClusterGenerator - class weka.datagenerators.ClusterGenerator.: 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.SimpleKMeans: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.FarthestFirst: Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.Cobweb: Classifies a given instance.
ClusterMembership - class weka.filters.unsupervised.attribute.ClusterMembership.: A filter that uses a clusterer to obtain cluster membership probabilites 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.MakeDensityBasedClusterer: Returns the cluster priors.
clusterPriors() - Method in class weka.clusterers.EM: Returns the cluster priors.
clusterResultsToString() - Method in class weka.clusterers.ClusterEvaluation: return the results of clustering.
Cobweb - class weka.clusterers.Cobweb.: 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.trees.m5.PreConstructedLinearModel: Return the array of coefficients
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.
collapse() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree: Collapses a tree to a node if training error doesn't increase.
Colors - class weka.gui.treevisualizer.Colors.: 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.
commentOutText(String) - Static method in class coreComponents.MatlabUtils: This is a stupid function that puts in Matlab comments into a region of text.
compactify() - Method in class weka.core.Instances: Compactifies the set of instances.
compare(Object, Object) - Method in class coreComponents.DoubleWithIndexComparator: How to compare the double values
compareOptions(String[], String[]) - Static method in class weka.core.CheckOptionHandler: Compares the two given sets of options.
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.TwoWayNumericSplit: 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.
ComplementNaiveBayes - class weka.classifiers.bayes.ComplementNaiveBayes.: 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
compressBytes(byte[]) - Method in class classifiers.usm.distance.USMStringDistance: Simple function for compressing an array of bytes into another array of bytes!
Compute - interface weka.experiment.Compute.: Interface to something that can accept remote connections and execute a task.
computeRowOfVPMMatrix(String[], Matrix, Instances, Instance) - Static method in class probabilityMachine.VennProbabilityClassifier: Counts the number of examples with the same type as the new example to create each row of the VPM matrix.
computeValueDifference(int, int, int) - Method in class classifiers.vdm.ValueDifferenceMetric
ConditionalEstimator - interface weka.estimators.ConditionalEstimator.: Interface for conditional probability estimators.
ConfidenceClassifier - class confidenceMachine.ConfidenceClassifier.: Abstract classification model that produces (for each test instance) a valid confidence estimation of the membership in each class (ie.
ConfidenceClassifier() - Constructor for class confidenceMachine.ConfidenceClassifier
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(ItemSet, ItemSet) - Static method in class weka.associations.ItemSet: Outputs the confidence for a rule.
confidenceMachine - package confidenceMachine
confidenceMachine.tcm - package confidenceMachine.tcm
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 weka.classifiers.evaluation.ConfusionMatrix.: 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() - Method in class evaluationMethods.EstimatorEvaluation: 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 weka.classifiers.rules.ConjunctiveRule.: 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(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(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(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(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(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.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.PredictionAppender: 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.ClassAssigner: 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(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(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
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.StripChart: Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.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.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 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.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.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.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.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.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.AbstractTestSetProducer: Notify this object that it has been registered as a listener with a source with respect to the supplied event name
CONNECTIONS - Static variable in class weka.gui.beans.BeanConnection: The list of connections
connectToDatabase() - Method in class weka.experiment.DatabaseUtils: Opens a connection to the database
ConsistencySubsetEval - class weka.attributeSelection.ConsistencySubsetEval.: Consistency attribute subset evaluator.
ConsistencySubsetEval.hashKey - class weka.attributeSelection.ConsistencySubsetEval.hashKey.: Class providing keys to the hash table.
ConsistencySubsetEval.hashKey(double[]) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey: Constructor for a hashKey
ConsistencySubsetEval.hashKey(Instance, int) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey: Constructor for a hashKey
ConsistencySubsetEval() - Constructor for class weka.attributeSelection.ConsistencySubsetEval: Constructor.
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.
containedBy(Instance) - Method in class weka.associations.ItemSet: Checks if an instance contains an item set.
contains(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Checks whether an element is in the set.
contains(Literal) - Method in class weka.associations.tertius.LiteralSet: Test if this LiteralSet contains a given Literal.
contains(Object) - Method in class weka.core.FastVector: added by akibriya
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: Checks if the specified key maps with an entry in the cache table
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Tests if the specified double is a key in this hashtable.
context() - Method in class weka.gui.HierarchyPropertyParser: The context of the current node, i.e.
ContingencyTables - class weka.core.ContingencyTables.: Class implementing some statistical routines for contingency tables.
ContingencyTables() - Constructor for class weka.core.ContingencyTables
ConverterUtils - class weka.core.converters.ConverterUtils.: Utility routines for the converter package.
ConverterUtils() - Constructor for class weka.core.converters.ConverterUtils
convertFileToArffString(String) - Static method in class coreComponents.SVMToArff: Method for converting the old SVM data format into the WEKA arff format.
convertFileToArffString(String) - Static method in class coreComponents.DataToArff: Method for converting the old data format into the WEKA arff format.
convertFileToRelCurveString(String) - Static method in class evaluationMethods.CreateROCCurve: Method for converting the old data format into the WEKA arff format.
convertFileToRelCurveString(String) - Static method in class evaluationMethods.CreateReliabilityCurve: Method for converting the old data format into the WEKA arff format.
convertFileToString(File) - Static method in class coreComponents.ArffCreator: Method for converting a text file into a big long string for processing!
convertInstance(Instance) - Method in class weka.attributeSelection.PrincipalComponents: Transform an instance in original (unormalized) format.
convertInstance(Instance) - Method in interface weka.attributeSelection.AttributeTransformer: Transforms an instance in the format of the original data to the transformed space
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(ItemSet, ItemSet, int, int) - Method in class weka.associations.ItemSet: Outputs the conviction for a rule.
Copy - class weka.filters.unsupervised.attribute.Copy.: An instance filter that copies a range of attributes in the dataset.
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.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.Attribute: Produces a shallow copy of this attribute.
copy() - Method in class weka.core.SparseInstance: Produces a shallow copy of this instance.
copy() - Method in class weka.associations.tertius.IndividualInstance
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.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() - Constructor for class weka.filters.unsupervised.attribute.Copy
Copyable - interface weka.core.Copyable.: Interface implemented by classes that can produce "shallow" copies of their objects.
copyElements() - Method in class weka.core.FastVector: Clones the vector and shallow copies all its elements.
coreComponents - package coreComponents
correct() - Method in class weka.classifiers.Evaluation: Gets the number of instances correctly classified (that is, for which a correct prediction was made).
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 evaluationMethods.EstimatorEvaluation: Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correlation - Variable in class weka.experiment.PairedStats: The correlation coefficient
correlation(double[], double[], int) - Static method in class weka.core.Utils: Returns the correlation coefficient of two double vectors.
correlationCoefficient() - Method in class weka.classifiers.Evaluation: Returns the correlation coefficient if the class is numeric.
correlationCoefficient() - Method in class evaluationMethods.EstimatorEvaluation: Returns the correlation coefficient if the class is numeric.
CorrelationSplitInfo - class weka.classifiers.trees.m5.CorrelationSplitInfo.: 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 weka.classifiers.evaluation.CostCurve.: 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 weka.classifiers.CostMatrix.: 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 weka.gui.CostMatrixEditor.: Class for editing CostMatrix objects.
CostMatrixEditor() - Constructor for class weka.gui.CostMatrixEditor: Constructs a new CostMatrixEditor.
costMatrixSourceTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
costMatrixSourceTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
costMatrixTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
costMatrixTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
CostSensitiveClassifier - class weka.classifiers.meta.CostSensitiveClassifier.: This metaclassifier makes its base classifier cost-sensitive.
CostSensitiveClassifier() - Constructor for class weka.classifiers.meta.CostSensitiveClassifier
CostSensitiveClassifierSplitEvaluator - class weka.experiment.CostSensitiveClassifierSplitEvaluator.: 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 - Variable in class weka.experiment.Stats: The number of values seen
count - Variable in class weka.experiment.PairedStats: The number of data points 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.
counterInstance(Instance) - Method in class weka.associations.tertius.Rule: Test if an instance is a counter-instance of this rule.
counterInstance(Instance) - Method in class weka.associations.tertius.LiteralSet: Test if an instance is a counter-instance of this LiteralSet.
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.
countNumberInSameTypeAsNewTest(int[]) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Simple function to count the number of training examples of the same Venn type as the new test example.
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.
createArffDataFromTextFilesInDirectories(FastVector, String, String, String, FastVector[]) - Static method in class coreComponents.ArffCreator: Create a pattern recognition data set from text files stored in directories.
createConfidenceCalibrationHistogram() - Method in class evaluationMethods.OnlineEvaluation: Creates a histogram tracking the performance of the confidence predictions at various significance levels.
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.
createMatlabMatrixFile(String, Instances, String) - Static method in class coreComponents.MatlabUtils: Output instances as simple matlab matrix file
CreateReliabilityCurve - class evaluationMethods.CreateReliabilityCurve.: Create Zadrozny's reliability curves from a matlab file containing probabilities.
CreateReliabilityCurve() - Constructor for class evaluationMethods.CreateReliabilityCurve
createResultsTable(ResultProducer, String) - Method in class weka.experiment.DatabaseUtils: Creates a results table for the supplied result producer.
CreateROCCurve - class evaluationMethods.CreateROCCurve.: Create Zadrozny's reliability curves from a matlab file containing probabilities.
CreateROCCurve() - Constructor for class evaluationMethods.CreateROCCurve
createSymmetricVennTranslationVector() - Method in class probabilityMachine.vpm.VPMBartsRMI2: Creates a vector to work out how to translate identifier -> index
createTypeDetailString(int[], int[], int) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Output type membership details
createTypeDetailString(int[], int[], int) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Output type membership details
createTypeDetailString(int[], int[], int) - Method in class probabilityMachine.VPMDistMetaLearner: Output type membership details
createTypeDetailString(int[], int[], int) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Output type membership details
createTypeDetailString(int[], int[], int) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Output type membership details
crossoverProbTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
CrossValidateAttributes() - Method in class weka.attributeSelection.AttributeSelection: Perform a cross validation for attribute selection.
crossValidateModel(Classifier, Instances, int) - Method in class evaluationMethods.EstimatorEvaluation: Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
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[]) - Method in class evaluationMethods.EstimatorEvaluation: Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(String, Instances, int, String[], Random) - Static method in class weka.clusterers.ClusterEvaluation: Performs a cross-validation for a distribution clusterer 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.
crossValidateTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property
CrossValidationFoldMaker - class weka.gui.beans.CrossValidationFoldMaker.: Bean for splitting instances into training ant test sets according to a cross validation
CrossValidationFoldMaker() - Constructor for class weka.gui.beans.CrossValidationFoldMaker
CrossValidationFoldMakerBeanInfo - class weka.gui.beans.CrossValidationFoldMakerBeanInfo.: BeanInfo class for the cross validation fold maker bean
CrossValidationFoldMakerBeanInfo() - Constructor for class weka.gui.beans.CrossValidationFoldMakerBeanInfo
CrossValidationFoldMakerCustomizer - class weka.gui.beans.CrossValidationFoldMakerCustomizer.: GUI Customizer for the cross validation fold maker bean
CrossValidationFoldMakerCustomizer() - Constructor for class weka.gui.beans.CrossValidationFoldMakerCustomizer
CrossValidationResultProducer - class weka.experiment.CrossValidationResultProducer.: 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
CrossValidEvaluation - class evaluationMethods.CrossValidEvaluation.
CrossValidEvaluation() - Constructor for class evaluationMethods.CrossValidEvaluation
crossValTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
CRUDE_NEAREST_NEIGHBOUR - Static variable in class probabilityMachine.vpm.VPMKNearestNeighbours
CSVDataSink - class weka.gui.beans.CSVDataSink.: Data sink that stores instances to a comma separated values (CSV) text file
CSVDataSink() - Constructor for class weka.gui.beans.CSVDataSink
CSVDataSinkBeanInfo - class weka.gui.beans.CSVDataSinkBeanInfo.: Bean info class for the CSVDataSink bean
CSVDataSinkBeanInfo() - Constructor for class weka.gui.beans.CSVDataSinkBeanInfo
CSVLoader - class weka.core.converters.CSVLoader.: Reads a text file that is comma or tab delimited..
CSVLoader() - Constructor for class weka.core.converters.CSVLoader
CSVResultListener - class weka.experiment.CSVResultListener.: CSVResultListener outputs the received results in csv format to a Writer
CSVResultListener() - Constructor for class weka.experiment.CSVResultListener
cTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
cTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
cTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
cTipText() - Method in class classifiers.AlphaProb_SMO: 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
CustomPanelSupplier - interface weka.gui.CustomPanelSupplier.: 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 weka.classifiers.meta.CVParameterSelection.: 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.

D

DatabaseConnectionDialog - class weka.gui.DatabaseConnectionDialog.: 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.
DatabaseResultListener - class weka.experiment.DatabaseResultListener.: 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 weka.experiment.DatabaseResultProducer.: 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.
databaseURLTipText() - Method in class weka.experiment.DatabaseUtils: Returns the tip text for this property
DatabaseUtils - class weka.experiment.DatabaseUtils.: DatabaseUtils provides utility functions for accessing the experiment database.
DatabaseUtils() - Constructor for class weka.experiment.DatabaseUtils: 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.
DataGenerator - interface weka.gui.boundaryvisualizer.DataGenerator.: Interface to something that can generate new instances based on a set of input instances
DATASET_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
DATASET_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
dataset() - Method in class weka.core.Instance: Returns the dataset this instance has access to.
DataSetEvent - class weka.gui.beans.DataSetEvent.: Event encapsulating a data set
DataSetEvent(Object, Instances) - Constructor for class weka.gui.beans.DataSetEvent
DatasetListPanel - class weka.gui.experiment.DatasetListPanel.: This panel controls setting a list of datasets for an experiment to iterate over.
DatasetListPanel() - Constructor for class weka.gui.experiment.DatasetListPanel: Create the dataset list panel initially disabled.
DatasetListPanel(Experiment) - Constructor for class weka.gui.experiment.DatasetListPanel: Creates the dataset list panel with the given experiment.
DataSink - interface weka.gui.beans.DataSink.: Indicator interface to something that can store instances to some destination
DataSource - interface weka.gui.beans.DataSource.: Interface to something that is capable of being a source for data - either batch or incremental data
DataSourceListener - interface weka.gui.beans.DataSourceListener.: Interface to something that can accept DataSetEvents
DataToArff - class coreComponents.DataToArff.: Here is a boring and pointless application for converting the primitive data to the nice WEKA arff format.
DataToArff() - Constructor for class coreComponents.DataToArff
DataVisualizer - class weka.gui.beans.DataVisualizer.: Bean that encapsulates weka.gui.visualize.VisualizePanel
DataVisualizer() - Constructor for class weka.gui.beans.DataVisualizer
DataVisualizerBeanInfo - class weka.gui.beans.DataVisualizerBeanInfo.: Bean info class for the data visualizer
DataVisualizerBeanInfo() - Constructor for class weka.gui.beans.DataVisualizerBeanInfo
DATE - Static variable in class weka.core.Attribute: Constant set for attributes with date values.
DATE - Static variable in class weka.experiment.DatabaseUtils
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 weka.estimators.DDConditionalEstimator.: 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.filters.unsupervised.attribute.AddExpression: Returns the tip text for this property
debugTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.Classifier: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
debugTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
debugTipText() - Method in class weka.classifiers.meta.AdditiveRegression: 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.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
decayTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
DecisionStump - class weka.classifiers.trees.DecisionStump.: Class for building and using a decision stump.
DecisionStump() - Constructor for class weka.classifiers.trees.DecisionStump
DecisionTable - class weka.classifiers.rules.DecisionTable.: Class for building and using a simple decision table majority classifier.
DecisionTable.hashKey - class weka.classifiers.rules.DecisionTable.hashKey.: Class providing keys to the hash table
DecisionTable.hashKey(double[]) - Constructor for class weka.classifiers.rules.DecisionTable.hashKey: Constructor for a hashKey
DecisionTable.hashKey(Instance, int) - Constructor for class weka.classifiers.rules.DecisionTable.hashKey: Constructor for a hashKey
DecisionTable.Link - class weka.classifiers.rules.DecisionTable.Link.: 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 weka.classifiers.rules.DecisionTable.LinkedList.: Class for handling a linked list.
DecisionTable.LinkedList() - Constructor for class weka.classifiers.rules.DecisionTable.LinkedList
DecisionTable() - Constructor for class weka.classifiers.rules.DecisionTable: Constructor for a DecisionTable
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 weka.classifiers.meta.Decorate.: 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
DEFAULT_COLORS - Static variable in class weka.gui.boundaryvisualizer.BoundaryPanel
DEFAULT_SHAPE_SIZE - Static variable in class weka.gui.visualize.Plot2D
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.
definePrefix(String) - Method in class classifiers.usm.distance.USMDistanceFunction: This function defines the prefix used for this type.
del(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution: Deletes given instance from given bag.
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.
delete(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Deletes an element from the set.
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).
deleteItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet: Deletes all item sets that don't have minimum support.
DeleteLastParent(Instances) - Method in class weka.classifiers.bayes.ParentSet: Delete last added parent from parent set and update internals (specifically the cardinality of the parent set)
deleteStringAttributes() - Method in class weka.core.Instances: Deletes all string attributes in the dataset.
deleteWithMissing(Attribute) - Method in class weka.core.Instances: Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(int) - 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
DensityBasedClusterer - class weka.clusterers.DensityBasedClusterer.: 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.
description() - Method in class weka.core.Option: Returns the option's description.
description() - Method in class weka.associations.tertius.Predicate
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
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.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.AveragingResultProducer: 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.
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.LearningRateResultProducer: Determines if there are any constraints (imposed by the destination) on the 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.AbstractTrainingSetProducer: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.StripChart: Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.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.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 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.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.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.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.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.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.AbstractTestSetProducer: Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
DiscreteEstimator - class weka.estimators.DiscreteEstimator.: 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 weka.classifiers.bayes.DiscreteEstimatorBayes.: Symbolic probability estimator based on symbol counts and a prior.
DiscreteEstimatorBayes(int, double) - Constructor for class weka.classifiers.bayes.DiscreteEstimatorBayes: Constructor
DiscreteFunction - class weka.classifiers.functions.pace.DiscreteFunction.: 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 weka.filters.supervised.attribute.Discretize.: An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
Discretize - class weka.filters.unsupervised.attribute.Discretize.: 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() - Constructor for class weka.filters.unsupervised.attribute.Discretize: Constructor - initialises the filter
Discretize(String) - Constructor for class weka.filters.unsupervised.attribute.Discretize: Another constructor
displayRulesTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
distance(Instance, Instance) - Method in class coreComponents.EuclideanDistanceMetric: Calculates the distance (or similarity) between two instances.
distance(Instance, Instance) - Method in class coreComponents.DistanceMetric: Calculates the distance between two instances.
distance(Instance, Instance) - Method in class classifiers.vdm.ValueDifferenceMetric
distance(Instance, Instance) - Method in class classifiers.usm.distance.USMDistanceFunction: Calculates the distance (or similarity) between two instances.
DistanceMetric - class coreComponents.DistanceMetric.: Abstract class for implementing specialised distance measures.
DistanceMetric() - Constructor for class coreComponents.DistanceMetric
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 weka.gui.experiment.DistributeExperimentPanel.: 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 - class weka.classifiers.trees.j48.Distribution.: Class for handling a distribution of class values.
distribution() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns the distribution of class values induced by the model.
distribution() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the predicted probabilities
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.
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(int, int) - Constructor for class weka.classifiers.trees.j48.Distribution: Creates and initializes a new distribution.
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.
distributionForInstance(Instance) - Method in class weka.classifiers.Classifier: Predicts the class memberships for a given 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.lazy.KStar: Calculates the class membership probabilities for the given test instance.
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.meta.RacedIncrementalLogitBoost: Computes class distribution of an instance using the best committee.
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.CVParameterSelection: Predicts the class distribution for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiScheme: Returns class probabilities.
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.ClassificationViaRegression: Returns the distribution for an instance.
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.FilteredClassifier: 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.Vote: Classifies a given instance using the selected classifier.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Stacking: Returns class probabilities.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Classifies a given instance after attribute selection
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.AdaBoostM1: Calculates the class membership probabilities for the given test instance.
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.StackingC: Classifies 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.CostSensitiveClassifier: 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.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.bayes.BayesNet: Calculates the class membership probabilities for the given test instance.
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.NaiveBayesSimple: 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.NaiveBayes: Calculates the class membership probabilities for the given test 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.rules.JRip: Classify the test instance with the rule learner and provide the class distributions
distributionForInstance(Instance) - Method in class weka.classifiers.rules.ConjunctiveRule: Computes class distribution for the given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.PART: Returns class probabilities for an 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.part.MakeDecList: Returns the class distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList: 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.ADTree: Returns the class probability distribution 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.DecisionStump: Calculates the class membership probabilities for the given test instance.
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.classifiers.trees.Id3: Computes class distribution for instance using 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.LMT: Returns class probabilities 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.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.functions.SimpleLogistic: Returns class probabilities for an 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.VotedPerceptron: Outputs the distribution for the given output.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.SMO: Estimates class probabilities for 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.RBFNetwork: Computes the distribution for a given instance
distributionForInstance(Instance) - Method in class probabilityMachine.VennProbabilityClassifier: Returns a single distribution for an instance, by crudely averaging the Venn probabilities.
distributionForInstance(Instance) - Method in class classifiers.PC_SMO: Estimates class probabilities for given instance.
distributionForInstance(Instance) - Method in class classifiers.AlphaProb_SMO: Estimates class probabilities for given instance.
distributionForInstance(Instance) - Method in class classifiers.AltDist_IBk: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class classifiers.stbarts.BartsRMI: Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns class probabilities for a weighted 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
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 weka.estimators.DKConditionalEstimator.: Conditional probability estimator for a discrete domain conditional upon a numeric domain.
DKConditionalEstimator(int, double) - Constructor for class weka.estimators.DKConditionalEstimator: Constructor
DNConditionalEstimator - class weka.estimators.DNConditionalEstimator.: 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.
doHistory(KeyEvent) - Method in class weka.gui.SimpleCLI: Changes the currently displayed command line when certain keys are pressed.
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.
doRun(int) - Method in interface weka.experiment.ResultProducer: Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.AveragingResultProducer: 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.CrossValidationResultProducer: 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 class weka.experiment.DatabaseResultProducer: Gets the results for a specified run number.
doRunKeys(int) - Method in interface weka.experiment.ResultProducer: Gets the keys 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.LearningRateResultProducer: 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.RandomSplitResultProducer: Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.DatabaseResultProducer: 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 weka.gui.graphvisualizer.DotParser.: 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 interface weka.gui.graphvisualizer.GraphConstants: Types of Edges
DOUBLE - Static variable in class weka.experiment.DatabaseUtils
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.
doubleValue() - Method in class coreComponents.DoubleWithIndex: returns the double value
DoubleVector - class weka.classifiers.functions.pace.DoubleVector.
DoubleVector - class coreComponents.DoubleVector.: Implements a vector class that can be used to store double values, like having a dynamic array of doubles (saves all the conversions to and from Double)
DoubleVector() - Constructor for class weka.classifiers.functions.pace.DoubleVector: Constructs a null vector.
DoubleVector() - Constructor for class coreComponents.DoubleVector
DoubleVector(double[]) - Constructor for class weka.classifiers.functions.pace.DoubleVector: Constructs a vector directly from a double array
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.
DoubleWithIndex - class coreComponents.DoubleWithIndex.: Class for storing a double value with a respective index.
DoubleWithIndex(double, int) - Constructor for class coreComponents.DoubleWithIndex: Simple constructor for initialising the variables.
DoubleWithIndexComparator - class coreComponents.DoubleWithIndexComparator.: Class used for sorting an indexed list of double values
DoubleWithIndexComparator() - Constructor for class coreComponents.DoubleWithIndexComparator: Constructor for the comparator class
Drawable - interface weka.core.Drawable.: Interface to something that can be drawn as a graph.
drawHighlight(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to draw the node highlighted.
drawInputLines(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to draw the nodes input connections.
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.
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 weka.gui.treevisualizer.Edge.: 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.
editableProperties() - Method in class weka.gui.PropertySheetPanel: Gets the number of editable properties for the current target.
eigenvalueDecomposition(double[][], double[]) - Method in class weka.core.Matrix: Performs Eigenvalue Decomposition using Householder QR Factorization This function is adapted from the CERN Jet Java libraries, for it the following copyright applies (see also, text on top of file) Copyright (C) 1999 CERN - European Organization for Nuclear Research.
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 weka.clusterers.EM.: 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.Scoreable
entropy(double[]) - Static method in class weka.core.ContingencyTables: Computes the entropy of the given array.
EntropyBasedSplitCrit - class weka.classifiers.trees.j48.EntropyBasedSplitCrit.: "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 weka.classifiers.trees.j48.EntropySplitCrit.: 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 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.LearningRateResultProducer: Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.RegressionSplitEvaluator: 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.RandomSplitResultProducer: 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.classifiers.meta.Bagging: 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.misc.FLR: 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.DecisionTable: 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.J48: 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.RandomForest: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.REPTree: Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.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.functions.SimpleLogistic: Returns an enumeration of the additional measure names
enumerateRequests() - Method in class weka.gui.beans.TrainTestSplitMaker: Get list of user requests
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.StripChart: Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.Loader: Get a list of user requests
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.Filter: Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.TextViewer: Get a list of user requests
enumerateRequests() - Method in interface weka.gui.beans.UserRequestAcceptor: Get a list of performable requests
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.GraphViewer: 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.CrossValidationFoldMaker: Return an enumeration of user 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
EPSILON - Static variable in class weka.classifiers.misc.FLR
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
epsilonTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
epsilonTipText() - Method in class classifiers.AlphaProb_SMO: 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.
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.gui.graphvisualizer.GraphNode: Returns true if passed in argument is an instance of GraphNode and is equal to this node.
equals(Object) - Method in class weka.gui.graphvisualizer.GraphEdge
equals(Object) - Method in class weka.core.Attribute: Tests if given attribute is equal to this attribute.
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.attributeSelection.ConsistencySubsetEval.hashKey: Tests if two instances are equal
equals(Object) - Method in class weka.associations.ItemSet: Tests if two item sets 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 evaluationMethods.EstimatorEvaluation: Tests whether the current evaluation object is equal to another evaluation object
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.TwoWayNumericSplit: Tests whether two splitters are equivalent.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: 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_SHAPE - Static variable in class weka.gui.visualize.Plot2D
error() - Method in class weka.classifiers.evaluation.NumericPrediction: Calculates the prediction error.
ErrorBasedMeritEvaluator - interface weka.attributeSelection.ErrorBasedMeritEvaluator.: Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme
errorOnProbabilitiesTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
errorOnProbabilitiesTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
errorRate() - Method in class weka.classifiers.Evaluation: Returns the estimated error rate or the root mean squared error (if the class is numeric).
errorRate() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Returns the estimated error rate.
errorRate() - Method in class evaluationMethods.EstimatorEvaluation: Returns the estimated error rate or the root mean squared error (if the class is numeric).
errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to get the error value of this unit.
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.
errorValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod: This function calculates what the error value should be.
errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit: 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.
Estimator - interface weka.estimators.Estimator.: Interface for probability estimators.
EstimatorEvaluation - class evaluationMethods.EstimatorEvaluation.: Class for evaluating machine learning models.
EstimatorEvaluation(Instances) - Constructor for class evaluationMethods.EstimatorEvaluation: Initializes all the counters for the evaluation.
EstimatorEvaluation(Instances, CostMatrix) - Constructor for class evaluationMethods.EstimatorEvaluation: Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
estimatorTipText() - Method in class weka.classifiers.functions.PaceRegression: Returns the tip text for this property
EuclideanDistanceMetric - class coreComponents.EuclideanDistanceMetric.: Implementing Euclidean distance (or similarity) function.
EuclideanDistanceMetric() - Constructor for class coreComponents.EuclideanDistanceMetric: Constructs an Euclidean Distance object.
EuclideanDistanceMetric(Instances) - Constructor for class coreComponents.EuclideanDistanceMetric: Constructs an Euclidean Distance object.
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
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.NormalizedPolyKernel: Redefines the eval function of PolyKernel.
eval(int, int, Instance) - Method in class weka.classifiers.functions.supportVector.RBFKernel: Implements the abstract function of Kernel.
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.PolyKernel: Implements the abstract function of Kernel.
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.AttributeEvaluator: evaluates an individual 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.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.PrincipalComponents: Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.OneRAttributeEval: evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.
evaluateClusterer(Clusterer, String[]) - Static method in class weka.clusterers.ClusterEvaluation: Evaluates a clusterer with the options given in an array of strings.
evaluateClusterer(Instances) - Method in class weka.clusterers.ClusterEvaluation: Evaluate the clusterer on a set of instances.
evaluateModel(Classifier, Instances) - Method in class weka.classifiers.Evaluation: Evaluates the classifier on a given set of instances.
evaluateModel(Classifier, Instances) - Method in class evaluationMethods.EstimatorEvaluation: Evaluates the classifier on a given set of instances.
evaluateModel(Classifier, 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 evaluationMethods.EstimatorEvaluation: Evaluates a classifier 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(String, String[]) - Static method in class evaluationMethods.EstimatorEvaluation: Evaluates a classifier with the options given in an array of strings.
evaluateModelOnce(Classifier, Instance) - Method in class weka.classifiers.Evaluation: Evaluates the classifier on a single instance.
evaluateModelOnce(Classifier, Instance) - Method in class evaluationMethods.EstimatorEvaluation: 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 evaluationMethods.EstimatorEvaluation: 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.
evaluateModelOnce(double, Instance) - Method in class evaluationMethods.EstimatorEvaluation: Evaluates the supplied prediction on a single instance.
evaluateModelOnline(String, String[]) - Method in class evaluationMethods.OnlineEvaluation: Evaluate the classifier model online.
evaluateProbabilityCalibration() - Method in class evaluationMethods.OnlineEvaluation: Creates a string reporting the calibration performance of the probabilities for each prediction
evaluateSubset(BitSet) - Method in class weka.attributeSelection.CfsSubsetEval: evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.ConsistencySubsetEval: Evaluates a subset of attributes
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
evaluateSubset(BitSet) - Method in class weka.attributeSelection.ClassifierSubsetEval: Evaluates a subset of attributes
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator: Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval: Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator: Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval: Evaluates a subset of attributes with respect to a set of instances.
Evaluation - class weka.classifiers.Evaluation.: 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.
evaluationMethods - package evaluationMethods
evaluationModeTipText() - Method in class weka.classifiers.meta.ThresholdSelector
EvaluationUtils - class weka.classifiers.evaluation.EvaluationUtils.: Contains utility functions for generating lists of predictions in various manners.
EvaluationUtils() - Constructor for class weka.classifiers.evaluation.EvaluationUtils
evaluatorTipText() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns the tip text for this property
evaluatorTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the tip text for this property
evalulatePValuesAndProbs(double) - Method in class evaluationMethods.OnlineEvaluation: Deprecated. now use the incremental stats methods instead of this clumsy batch function Creates a string reporting the performance of the p-values of each prediction at a set significance level
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 weka.gui.beans.EventConstraints.: Interface for objects that want to be able to specify at any given time whether their current configuration allows a particular event to be generated.
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.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.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.
eventGeneratable(String) - Method in class weka.gui.beans.Loader: Returns true if the named event can be generated at this time
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.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 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.PredictionAppender: Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClassAssigner: Returns true, if at the current time, the named event could be generated.
eventGeneratable(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.TestSetMaker: 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.
exclusiveTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
execute() - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Perform the sub task
execute() - Method in interface weka.experiment.Task: Execute this task.
execute() - Method in class weka.experiment.RemoteExperimentSubTask: Run the experiment
execute(String) - Method in class weka.experiment.DatabaseUtils: Executes a SQL query.
executeTask(Task) - Method in class weka.experiment.RemoteEngine: Takes a task object and queues it for execution
executeTask(Task) - Method in interface weka.experiment.Compute: Execute a task
ExhaustiveSearch - class weka.attributeSelection.ExhaustiveSearch.: 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)
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 weka.experiment.Experiment.: Holds all the necessary configuration information for a standard type experiment.
Experiment() - Constructor for class weka.experiment.Experiment
Experimenter - class weka.gui.experiment.Experimenter.: 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
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 weka.gui.explorer.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 weka.classifiers.functions.pace.ExponentialFormat.
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.VotedPerceptron: Returns the tip text for this property
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 classifiers.PC_SMO: Returns the tip text for this property
exponentTipText() - Method in class classifiers.AlphaProb_SMO: 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 weka.gui.ExtensionFileFilter.: 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

F

f(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of f(x) given the mixture.
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.ChisqMixture: Computes the value of f(x) given the mixture, where x is a vector.
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.
falseNegativeRate(int) - Method in class evaluationMethods.EstimatorEvaluation: 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.
falsePositiveRate(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate the false positive rate with respect to a particular class.
FarthestFirst - class weka.clusterers.FarthestFirst.: 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 weka.core.FastVector.: Implements a fast vector class without synchronized methods.
FastVector.FastVectorEnumeration - class weka.core.FastVector.FastVectorEnumeration.: 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.
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(int, int, double) - Constructor for class weka.core.FastVector: Constructs a vector with the given capacity, capacity increment and capacity mulitplier.
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
featureSpaceNormalizationTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
featureSpaceNormalizationTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
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.experiment.Experiment: The filename extension that should be used for experiment files
FILE_EXTENSION - Static variable in class weka.classifiers.CostMatrix: The deafult file extension for cost matrix files
FileEditor - class weka.gui.FileEditor.: A PropertyEditor for File objects that lets the user select a file.
FileEditor() - Constructor for class weka.gui.FileEditor
fillWithMissingTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns the tip text for this property
Filter - class weka.gui.beans.Filter.: A wrapper bean for Weka filters
Filter - class weka.filters.Filter.: 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_NONE - Static variable in class weka.classifiers.functions.SMO
FILTER_NONE - Static variable in class weka.classifiers.functions.SMOreg
FILTER_NONE - Static variable in class classifiers.PC_SMO
FILTER_NONE - Static variable in class classifiers.AlphaProb_SMO
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_NORMALIZE - Static variable in class classifiers.PC_SMO: The filter to apply to the training data
FILTER_NORMALIZE - Static variable in class classifiers.AlphaProb_SMO: 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
FILTER_STANDARDIZE - Static variable in class classifiers.PC_SMO
FILTER_STANDARDIZE - Static variable in class classifiers.AlphaProb_SMO
Filter() - Constructor for class weka.gui.beans.Filter
Filter() - Constructor for class weka.filters.Filter
FilterBeanInfo - class weka.gui.beans.FilterBeanInfo.: Bean info class for the Filter bean
FilterBeanInfo() - Constructor for class weka.gui.beans.FilterBeanInfo
FilterCustomizer - class weka.gui.beans.FilterCustomizer.: GUI customizer for the filter bean
FilterCustomizer() - Constructor for class weka.gui.beans.FilterCustomizer
FilteredClassifier - class weka.classifiers.meta.FilteredClassifier.: 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 specifying ZeroR as the classifier and AllFilter as the filter.
FilteredClassifier(Classifier, Filter) - Constructor for class weka.classifiers.meta.FilteredClassifier: Constructor that specifies the subclassifier and filter to use.
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
filterTypeTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
filterTypeTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
findArgmin(double[], double[][]) - Method in class weka.core.Optimization: Main algorithm.
findAttributesWithPrefix() - Method in class classifiers.usm.distance.USMDistanceFunction: The function
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
findInstanceInCache(Instance, Attribute) - Method in class classifiers.usm.distance.USMDistanceFunction: Finds if complexities have been cached for an instance and if so returns the index in the cache vector.
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns the tip text for this property
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
findVennTypes(double, double, double, double, double[], double) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Computes the Venn types for training and new test example
FINISHED - Static variable in class weka.experiment.TaskStatusInfo
finished() - Method in class weka.experiment.OutputZipper: Closes the zip 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 weka.filters.unsupervised.attribute.FirstOrder.: 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.MixtureDistribution: Contructs the set of fitting intervals for mixture estimation.
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.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 weka.classifiers.functions.pace.FlexibleDecimalFormat.
FlexibleDecimalFormat() - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
FlexibleDecimalFormat(double) - 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
FLOAT - Static variable in class weka.experiment.DatabaseUtils
FloatingPointFormat - class weka.classifiers.functions.pace.FloatingPointFormat.: 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
FLR - class weka.classifiers.misc.FLR.: Fuzzy Lattice Reasoning Classifier
FLR() - Constructor for class weka.classifiers.misc.FLR
FMEASURE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
fMeasure(int) - Method in class weka.classifiers.Evaluation: Calculate the F-Measure with respect to a particular class.
fMeasure(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate the F-Measure with respect to a particular class.
FOLD_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
foldsTipText() - Method in class weka.gui.beans.CrossValidationFoldMaker: Tip text for this property
foldsTipText() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string for this option suitable for display in the gui as a tip text
foldsTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
foldsTipText() - Method in class weka.attributeSelection.RaceSearch: 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.ConjunctiveRule: Returns the tip text for this property
foldsTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
foldTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
foldTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
FORMAT_AVAILABLE - Static variable in class weka.gui.beans.InstanceEvent
FORMAT_AVAILABLE - Static variable in class weka.gui.streams.InstanceEvent: Specifies that the instance format is available
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.FloatingPointFormat
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.ExponentialFormat
formatDate(double) - Method in class weka.core.Attribute
formatString(String) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
forName(Class, String, String[]) - Static method in class weka.core.Utils: Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.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(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.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.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.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 coreComponents.DistanceMetric: Creates a new instance of a distance metric 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.
ForwardSelection - class weka.attributeSelection.ForwardSelection.: Class for performing a forward selection hill climbing search.
ForwardSelection() - Constructor for class weka.attributeSelection.ForwardSelection
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.
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
frequencyLimitForParentAttributesTipText() - Method in class weka.classifiers.bayes.AODE: Returns the tip text for this property
frequencyThresholdTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
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.C45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio() - Method in class weka.classifiers.trees.j48.BinC45Split: 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 weka.attributeSelection.GainRatioAttributeEval.: Class for Evaluating attributes individually by measuring gain ratio with respect to the class.
GainRatioAttributeEval() - Constructor for class weka.attributeSelection.GainRatioAttributeEval: Constructor
GainRatioSplitCrit - class weka.classifiers.trees.j48.GainRatioSplitCrit.: Class for computing the gain ratio for a given distribution.
GainRatioSplitCrit() - Constructor for class weka.classifiers.trees.j48.GainRatioSplitCrit
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
gammaTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
gammaTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
GeneralUtils - class coreComponents.GeneralUtils.: Class for keeping lots of useful functions that I like a lot.
GeneralUtils() - Constructor for class coreComponents.GeneralUtils
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() - 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.
generateExamples(Random, Instances) - Method in class weka.datagenerators.BIRCHCluster: 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(int[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Generates a new instance using one kernel estimator.
generateInstances(int[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Generate an instance.
generateRankingTipText() - Method in class weka.attributeSelection.ForwardSelection: Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
generateRules(double, FastVector, int) - Method in class weka.associations.ItemSet: Generates all rules for an item set.
generateRulesBruteForce(double, int, FastVector, int, int, double) - Method in class weka.associations.ItemSet: 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 weka.datagenerators.Generator.: Abstract class for data generators.
Generator() - Constructor for class weka.datagenerators.Generator
GeneratorPropertyIteratorPanel - class weka.gui.experiment.GeneratorPropertyIteratorPanel.: 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 weka.gui.GenericArrayEditor.: 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 weka.gui.GenericObjectEditor.: A PropertyEditor for objects that themselves have been defined as editable in the GenericObjectEditor configuration file, which lists possible values that can be selected from, and themselves configured.
GenericObjectEditor.GOEPanel - class weka.gui.GenericObjectEditor.GOEPanel.: Handles the GUI side of editing values.
GenericObjectEditor.GOEPanel() - Constructor for class weka.gui.GenericObjectEditor.GOEPanel: Creates the GUI editor component
GenericObjectEditor.JTreePopupMenu - class weka.gui.GenericObjectEditor.JTreePopupMenu.: 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.
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.
GeneticSearch - class weka.attributeSelection.GeneticSearch.: Class for performing a genetic based search.
GeneticSearch() - Constructor for class weka.attributeSelection.GeneticSearch: Constructor.
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.
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.
getAdvanceDataSetFirst() - Method in class weka.experiment.Experiment: Get the value of m_DataSetFirstFirst.
getAlpha() - Method in class weka.classifiers.bayes.BayesNet: Method declaration
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
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.
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.
getArtificialSize() - Method in class weka.classifiers.meta.Decorate: Factor that determines number of artificial examples to generate.
getAsText() - Method in class weka.gui.SelectedTagEditor: Gets the current value 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.GenericArrayEditor: Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.CostMatrixEditor: Some objects can be represented as text, but a cost matrix cannot.
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.
getAttribute() - Method in class classifiers.usm.distance.USMComplexityCache: Simple function to return the attribute for which has been compressed of the instance.
getAttribute1() - Method in class weka.gui.visualize.VisualizePanelEvent
getAttribute2() - Method in class weka.gui.visualize.VisualizePanelEvent
getAttributeEvaluator() - Method in class weka.attributeSelection.RankSearch: Get the attribute evaluator used to generate the ranking.
getAttributeEvaluator() - Method in class weka.attributeSelection.RaceSearch: Get the attribute evaluator used to generate the ranking.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.StringToNominal: 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.MergeTwoValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.Add: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the index of the attribute used.
getAttributeIndex() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns the index of the attribute used in the regression.
getAttributeIndices() - Method in class weka.filters.supervised.attribute.Discretize: Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Remove: Get the current range selection.
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.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.FirstOrder: Get 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.Discretize: Gets the current range selection
getAttributeMax(int) - Method in class weka.classifiers.lazy.IBk: Get an attributes maximum observed value
getAttributeMax(int) - Method in class classifiers.AltDist_IBk: Get an attributes maximum observed value
getAttributeMin(int) - Method in class weka.classifiers.lazy.IBk: Get an attributes minimum observed value
getAttributeMin(int) - Method in class classifiers.AltDist_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.
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
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.TextViewer: Return the bean context (if any) that this bean is embedded in
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
getBeanDescriptor() - Method in class weka.gui.beans.FilterBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.ClassAssignerBeanInfo
getBeanDescriptor() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo: Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo: 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.StripChartBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.ClassifierBeanInfo: Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.LoaderBeanInfo: Get the bean descriptor for this bean
getBeanInstances() - Static method in class weka.gui.beans.BeanInstance: Return the list of displayed beans
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.
getBetterMDL() - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Returns if better MDL is used.
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.
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.InfoGainAttributeEval: get whether numeric attributes are just being binarized.
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: 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.PKIDiscretize: Ignored
getBins() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets the number of bins numeric attributes will be divided into
getBoundsFile() - Method in class weka.classifiers.misc.FLR: Get boundaries File
getBuildLogisticModels() - Method in class weka.classifiers.functions.SMO: Get the value of buildLogisticModels.
getBuildLogisticModels() - Method in class classifiers.PC_SMO: Get the value of buildLogisticModels.
getBuildLogisticModels() - Method in class classifiers.AlphaProb_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.
getC() - Method in class classifiers.PC_SMO: Get the value of C.
getC() - Method in class classifiers.AlphaProb_SMO: 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
getCacheSize() - Method in class classifiers.PC_SMO: Get the size of the kernel cache
getCacheSize() - Method in class classifiers.AlphaProb_SMO: 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 interface weka.attributeSelection.RankedOutputSearch: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.ForwardSelection: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.Ranker: Gets the calculated number to select.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.RaceSearch: Gets the calculated number of attributes to retain.
getCalculateStdDevs() - Method in class weka.experiment.AveragingResultProducer: Get the value of CalculateStdDevs.
GetCardinalityOfParents() - Method in class weka.classifiers.bayes.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.TwoWayNumericSplit: 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.
getChildren() - Method in class weka.classifiers.trees.adtree.PredictionNode: Gets the children of this 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.gui.beans.Classifier: Get the classifier currently set for this wrapper
getClassifier() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the classifier
getClassifier() - Method in class weka.gui.beans.BatchClassifierEvent: Get the classifier
getClassifier() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the classifier used by the filter.
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.attributeSelection.WrapperSubsetEval: Get the classifier used as the base learner.
getClassifier() - Method in class weka.attributeSelection.ClassifierSubsetEval: Get the classifier used as the base learner.
getClassifier() - Method in class weka.classifiers.CheckClassifier: Get the classifier used as the classifier
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.SingleClassifierEnhancer: Get the classifier used as the base learner.
getClassifier() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.ThresholdSelector: Get the Classifier used as the classifier.
getClassifier() - Method in class weka.classifiers.meta.FilteredClassifier: Gets the classifier used.
getClassifier() - Method in class weka.classifiers.meta.MultiClassClassifier: Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.AdditiveRegression: Gets the classifier used.
getClassifier() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the classifier used.
getClassifier() - Method in class weka.classifiers.meta.Decorate: Get the classifier used as the base classifier
getClassifier() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the classifier used.
getClassifier() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Get the classifier used as the classifier
getClassifier(int) - Method in class weka.classifiers.MultipleClassifiersCombiner: Gets a single classifier from the set of available classifiers.
getClassifier(int) - Method in class weka.classifiers.meta.MultiScheme: Gets a single classifier from the set of available classifiers.
getClassifiers() - Method in class weka.classifiers.MultipleClassifiersCombiner: Gets the list of possible classifers to choose from.
getClassifiers() - Method in class weka.classifiers.meta.MultiScheme: 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.filters.unsupervised.instance.RemoveMisclassified: Gets the attribute on which misclassifications are based.
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.
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
getClearEachDataset() - Method in class weka.gui.streams.InstanceViewer
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.
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.
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
getClusterPriors() - Method in class weka.clusterers.EM: Return the priors for the clusters
getColor() - Method in class weka.gui.treevisualizer.Node: Get the value of color.
getColorBox() - Method in class weka.gui.AttributeVisualizationPanel
getColoringIndex() - Method in class weka.gui.AttributeVisualizationPanel: Get the coloring 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.core.Matrix: Gets a column of the matrix and returns it as a double array.
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
getColumnDimension() - Method in class weka.classifiers.functions.pace.Matrix: Get column dimension.
getColumnPackedCopy() - Method in class weka.classifiers.functions.pace.Matrix: Make a one-dimensional column packed copy of the internal array.
getCommand() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
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).
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.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.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.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 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).
getComplexity() - Method in class classifiers.usm.distance.USMComplexityCache: Simple function to return the complexity of an instance of interest
getComplexityParameter() - Method in class weka.attributeSelection.SVMAttributeEval: Get the value of C used with SMO
getComplexityStar() - Method in class classifiers.usm.distance.USMComplexityCache: Simple function to return the complexity of the compressed instance of interest
getCompressedVersion() - Method in class classifiers.usm.distance.USMComplexityCache: Simple function to return the compressed version of the instance.
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.
getConfusionMatrix() - Method in class weka.classifiers.evaluation.TwoClassStats: Generates a ConfusionMatrix representing the current two-class statistics, using class names "negative" and "positive".
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.
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.MetaCost: Gets the misclassification cost matrix.
getCostMatrix() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the misclassification cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.MetaCost: Gets the source location method of the cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the source location method of the cost matrix.
getCount(double) - Method in class weka.classifiers.bayes.DiscreteEstimatorBayes: Get a counts 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, ADNode, boolean) - Method in class weka.classifiers.bayes.VaryNode: get counts for specific instantiation of a set of nodes
getCounts(int[], int[], int[], int, int, boolean) - Method in class weka.classifiers.bayes.ADNode: 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
getCrossValidate() - Method in class classifiers.AltDist_IBk: Gets whether hold-one-out cross-validation will be used to select the best k value
getCurrentDatasetNumber() - Method in class weka.experiment.Experiment: When an experiment is running, this returns the current dataset number.
getCurrentInstance() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the current instance
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.ThresholdCurve: Calculates the performance stats for the default 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.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.ThresholdCurve: Calculates the performance stats for the desired 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.
getCustomEditor() - Method in class weka.gui.FileEditor: Gets the custom editor component.
getCustomEditor() - Method in class weka.gui.GenericObjectEditor: Returns the array editing component.
getCustomEditor() - Method in class weka.gui.GenericArrayEditor: Returns the array editing component.
getCustomEditor() - Method in class weka.gui.CostMatrixEditor: Gets a GUI component with which the user can edit the cost matrix.
getCustomPanel() - Method in class weka.gui.GenericObjectEditor: Gets the custom panel used for editing the object.
getCustomPanel() - Method in interface weka.gui.CustomPanelSupplier: Gets the custom panel for 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.
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.gui.beans.DataSetEvent: Return the instances of the data set
getDataSet() - Method in class weka.core.converters.CSVLoader: Return the full data set.
getDataSet() - Method in interface weka.core.converters.Loader: Return the full data set.
getDataSet() - Method in class weka.core.converters.C45Loader: Return the full data set.
getDataSet() - Method in class weka.core.converters.SerializedInstancesLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.ArffLoader: Return the full data set.
getDataSet() - Method in class weka.core.converters.AbstractLoader
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.
getDebug() - Method in class weka.gui.streams.InstanceJoiner
getDebug() - Method in class weka.gui.streams.InstanceLoader
getDebug() - Method in class weka.gui.streams.InstanceCounter
getDebug() - Method in class weka.gui.streams.InstanceSavePanel
getDebug() - Method in class weka.gui.streams.InstanceViewer
getDebug() - Method in class weka.gui.streams.InstanceTable
getDebug() - Method in class weka.clusterers.EM: Get debug mode
getDebug() - Method in class weka.datagenerators.Generator: Gets the debug flag.
getDebug() - Method in class weka.datagenerators.ClusterGenerator: Gets the debug flag.
getDebug() - Method in class weka.filters.unsupervised.attribute.AddExpression: Gets whether debug is set
getDebug() - Method in class weka.attributeSelection.RaceSearch: Get whether output is to be verbose
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.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.meta.RacedIncrementalLogitBoost: Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.meta.MultiScheme: Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.meta.AdditiveRegression: Gets whether debugging has been turned on
getDebug() - Method in class weka.classifiers.meta.Decorate: 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.classifiers.functions.LinearRegression: Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.functions.LeastMedSq: Returns whether or not debugging output shouild 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 classifiers.AltDist_IBk: Get the value of Debug.
getDebugEvery() - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Reports whether the TCM is currently in debug mode (if not -1), and specifies how frequently to output message about TCM's progress on data.
getDebugEvery() - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Reports whether the VPM is currently in debug mode (if not -1), and specifies how frequently to output message about VPM's progress on data.
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.
getDensityBasedClusterer() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Get the clusterer used by this filter
getDescription() - Method in class weka.gui.ExtensionFileFilter: Gets the description of accepted files.
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.
getDirection() - Method in class weka.attributeSelection.BestFirst: Get the search direction
getDisplayRules() - Method in class weka.classifiers.rules.DecisionTable: Gets whether rules are being printed
getDistanceMetric() - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: The gives details about the distance metric that has been chosen
getDistanceMetric() - Method in class classifiers.AltDist_IBk: The gives details about the distance metric that has been chosen
getDistanceWeighting() - Method in class weka.classifiers.lazy.IBk: Gets the distance weighting method used.
getDistanceWeighting() - Method in class classifiers.AltDist_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(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
getDouble(int) - Method in class coreComponents.DoubleVector: Simply returns a double value to a vector at a particular index
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: 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.
getEpsilon() - Method in class classifiers.PC_SMO: Get the value of epsilon.
getEpsilon() - Method in class classifiers.AlphaProb_SMO: 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.trees.LMT: Get the value of errorOnProbabilities.
getErrorOnProbabilities() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of errorOnProbabilities.
getEstimatedErrorsForLeaf() - Method in class weka.classifiers.rules.part.C45PruneableDecList: Computes estimated errors for leaf.
getEstimator() - Method in class weka.classifiers.functions.PaceRegression: Gets the estimator
getEstimator(double) - Method in class weka.estimators.KKConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NNConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in interface weka.estimators.ConditionalEstimator: 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.DKConditionalEstimator: 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.NDConditionalEstimator: Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DNConditionalEstimator: 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.filters.supervised.attribute.AttributeSelection: Get the name of the attribute/subset evaluator
getEvaluator() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the attribute evaluator used
getEvalUsingTrainingData() - Method in class weka.attributeSelection.OneRAttributeEval: Returns true if the training data is to be used for evaluation
getEventSetDescriptors() - Method in class weka.gui.beans.FilterBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.DataVisualizerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo: Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.AttributeSummarizerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.ScatterPlotMatrixBeanInfo: 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.AbstractDataSinkBeanInfo: 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.ClassifierBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.TextViewerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTestSetProducerBeanInfo
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSourceBeanInfo: Get the event set descriptors pertinent to data sources
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainingSetProducerBeanInfo: Returns event set descriptors for this type of bean
getEventSetDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.GraphViewerBeanInfo: 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.
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.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.
getExperiment() - Method in class weka.experiment.RemoteExperimentSubTask: Get the experiment for this sub task
getExponent() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of exponent.
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 classifiers.PC_SMO: Get the value of exponent.
getExponent() - Method in class classifiers.AlphaProb_SMO: Get the value of exponent.
getExpression() - Method in class weka.filters.unsupervised.attribute.AddExpression: Get the expression
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.
getFeatureSpaceNormalization() - Method in class classifiers.PC_SMO: Check whether feature spaces is being normalized.
getFeatureSpaceNormalization() - Method in class classifiers.AlphaProb_SMO: Check whether feature spaces is being normalized.
getFile() - Method in class weka.core.converters.ArffLoader: get the File specified as the source
getFileStem() - Method in class weka.gui.beans.CSVDataSink: Gets the destination file stem
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.gui.beans.Filter
getFilter() - Method in class weka.classifiers.meta.FilteredClassifier: Gets the filter used.
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.
getFilterType() - Method in class classifiers.PC_SMO: Gets how the training data will be transformed.
getFilterType() - Method in class classifiers.AlphaProb_SMO: Gets how the training data will be transformed.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Get the value of FindNumBins.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.Discretize: 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".
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.gui.beans.CrossValidationFoldMaker: Get the currently set number of folds
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.JRip
getFolds() - Method in class weka.classifiers.rules.ConjunctiveRule
getFolds() - Method in class weka.classifiers.rules.Ridor
getFoldsType() - Method in class weka.attributeSelection.RaceSearch: Get the xfold type
getFPRate() - Method in class weka.associations.tertius.Rule: Get the rate of False Positive instances of this rule.
getFrequencyLimitForParentAttributes() - Method in class weka.classifiers.bayes.AODE: Return the frequency limit for parent attributes
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.
getGamma() - Method in class classifiers.PC_SMO: Get the value of gamma.
getGamma() - Method in class classifiers.AlphaProb_SMO: Get the value of gamma.
getGaussianStdTypes(int[], double, double, double, double, double[], double) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Finds the number of std deviations away from the mean of each Gaussian -ve represent in Benign Gaussian +ve represent in Malignant Gaussian.
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 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.ForwardSelection: Gets whether ranking has been requested.
getGenerateRanking() - Method in class weka.attributeSelection.Ranker: This is a dummy method.
getGenerateRanking() - Method in class weka.attributeSelection.RaceSearch: Gets whether ranking has been requested.
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
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
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.attributeSelection.BestFirst.Link2: Get a group
getGroup() - Method in class weka.classifiers.rules.DecisionTable.Link: Gets the group.
getGUI() - Method in class weka.classifiers.functions.MultilayerPerceptron
getHashtable(FastVector, int) - Static method in class weka.associations.ItemSet: 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.
getId() - Method in class weka.classifiers.functions.neural.NeuralConnection
getID() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
getID() - Method in class weka.gui.streams.InstanceEvent: Get the event type
getID() - Method in class weka.core.Tag: Gets the numeric ID of the Tag.
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.
getIndex() - Method in class weka.core.SingleIndex: Gets the selected index
getIndex() - Method in class weka.associations.tertius.Predicate
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.BayesNet: Method declaration
getInputFileName() - Method in class coreComponents.SVMToArff
getInputFileName() - Method in class coreComponents.DataToArff
getInputFileName() - Method in class evaluationMethods.CreateROCCurve
getInputFileName() - Method in class evaluationMethods.CalculateLoss
getInputFileName() - Method in class evaluationMethods.CreateReliabilityCurve
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.
getInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the inputs.
getInstance() - Method in class weka.gui.beans.InstanceEvent: Get the instance
getInstance() - Method in class classifiers.usm.distance.USMComplexityCache: Simple function to return the instance of interest
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.gui.SetInstancesPanel: Gets the set of instances currently held by the panel
getInstances() - Method in class weka.gui.explorer.PreprocessPanel: Gets the working set of instances.
getInstances() - Method in class weka.gui.treevisualizer.Node: This will return the Instances object related to this node.
getInstances() - Method in class weka.gui.visualize.VisualizePanel: Get the master plot's instances
getInstances() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Get the training instances
getInstances() - Method in class weka.experiment.PairedTTester: Get the value of 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.RemoveType: Get whether the supplied columns are to be removed or kept
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.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.NumericTransform: Get whether the supplied columns are to be transformed or not
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.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.FileEditor: Returns a representation of the current property value as java source.
getJavaInitializationString() - Method in class weka.gui.SelectedTagEditor: Returns a description of the property value as java source.
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.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.CostMatrixEditor: Returns the Java code that generates an object the same as the one being edited.
getKernelBandwidth() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Get the kernel bandwidth
getKey() - Method in interface weka.experiment.SplitEvaluator: Gets the key describing the current SplitEvaluator.
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.
getKeyFieldName() - Method in class weka.experiment.AveragingResultProducer: Get the value of KeyFieldName.
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.
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.LearningRateResultProducer: 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 class weka.experiment.CrossValidationResultProducer: 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.DatabaseResultProducer: Gets the names of each of the 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.
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.LearningRateResultProducer: 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 class weka.experiment.CrossValidationResultProducer: 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.DatabaseResultProducer: Gets the data types of each of the columns produced for a single run.
getKNN() - Method in class weka.classifiers.lazy.LWL: Gets the number of neighbours used for kernel bandwidth setting.
getKNN() - Method in class weka.classifiers.lazy.IBk: Gets the number of neighbours the learner will use.
getKNN() - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Gets the number of neighbours the learner will use.
getKNN() - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Gets the number of neighbours the VPM learner will use.
getKNN() - Method in class classifiers.AltDist_IBk: Gets the number of neighbours the learner will use.
getKononekoMDL() - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Returns if Kononeko MDL is used.
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: Returns the L part of the matrix.
getLabel() - Method in class weka.gui.treevisualizer.Node: Get the value of label.
getLabel() - Method in class weka.gui.treevisualizer.Edge: 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.Node: Returns the text String for the specfied line.
getLine(int) - Method in class weka.gui.treevisualizer.Edge: Returns line number n
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
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
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.
getLowerOrderTerms() - Method in class classifiers.PC_SMO: Check whether lower-order terms are being used.
getLowerOrderTerms() - Method in class classifiers.AlphaProb_SMO: 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.Rule
getM5RootNode() - Method in class weka.classifiers.trees.m5.M5Base
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.
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[]) - 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.classifiers.functions.pace.Matrix: Get a submatrix.
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
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
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.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
getMaxIterations() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns the maxIterations parameter.
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.
getMaxModels() - Method in class weka.classifiers.meta.AdditiveRegression: Get the max number of models to generate
getMaxNrOfParents() - Method in class weka.classifiers.bayes.BayesNet: 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.TrainingSetEvent: Get the maximum set number
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.TestSetEvent: 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
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.
getMeanSquared() - Method in class classifiers.AltDist_IBk: Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
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.LearningRateResultProducer: Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RegressionSplitEvaluator: 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.RandomSplitResultProducer: 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.classifiers.meta.Bagging: 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.misc.FLR: 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.DecisionTable: 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.J48: 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.RandomForest: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.REPTree: Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.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.functions.SimpleLogistic: Returns the value of the named measure
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.
getMethod() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets the method used.
getMethod() - Method in class weka.classifiers.functions.neural.NeuralNode
getMethodName() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the transformation method.
getMetricType() - Method in class weka.associations.Apriori: Get the metric type
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.JRip
getMinNo() - Method in class weka.classifiers.rules.ConjunctiveRule
getMinNo() - Method in class weka.classifiers.rules.Ridor
getMinNum() - Method in class weka.classifiers.trees.REPTree: Get the value of MinNum.
getMinNum() - Method in class weka.classifiers.trees.RandomTree: 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.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
getMinNumInstances() - Method in class weka.classifiers.trees.m5.M5Base: 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.clusterers.MakeDensityBasedClusterer: Get the minimum allowable standard deviation.
getMinStdDev() - Method in class weka.clusterers.EM: 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.InfoGainAttributeEval: get whether missing values are being distributed or not
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.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.
getMissingSeperate() - Method in class weka.attributeSelection.CfsSubsetEval: Return true is missing is treated as a seperate 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
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.
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
getName() - Method in class weka.gui.visualize.VisualizePanel: Returns the name associated with this plot.
getName() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the name of the new attribute
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() - Method in class weka.core.converters.CSVLoader: CSVLoader is unable to process a data set incrementally.
getNextInstance() - 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() - 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() - Method in class weka.core.converters.SerializedInstancesLoader: Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.
getNextInstance() - 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() - Method in class weka.core.converters.AbstractLoader
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
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.
getNoNormalization() - Method in class classifiers.AltDist_IBk: Gets whether normalization is turned off.
getNoPruning() - Method in class weka.classifiers.trees.REPTree: Get the value of NoPruning.
getNormalize() - Method in class weka.attributeSelection.PrincipalComponents: Gets whether or not input data is to be normalized
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.
GetNrOfParents() - Method in class weka.classifiers.bayes.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.datagenerators.Generator: Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.ClusterGenerator: Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes in the dataset
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.
getNumberOfBins() - Method in class evaluationMethods.OnlineEvaluation: Gets the number of bins used in the probability calibration histograms
getNumberVennTypes() - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Gets the number of Venn probability types used!
getNumberVennTypes() - Method in class probabilityMachine.VPMDistMetaLearner: Gets the number of Venn probability types used!
getNumberVennTypes() - Method in class probabilityMachine.vpm.VPMNaiveBayes: Gets the number of Venn probability types used!
getNumberVennTypes() - Method in class probabilityMachine.vpm.VPMBartsRMI2: Gets the number of Venn probability types used!
getNumberVennTypes() - Method in class probabilityMachine.vpm.VPMBartsRMI: Gets the number of Venn probability types used!
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.trees.LMT: Get the value of numBoostingIterations.
getNumBoostingIterations() - Method in class weka.classifiers.functions.SimpleLogistic: 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.clusterers.SimpleKMeans: gets the number of clusters to generate
getNumClusters() - Method in class weka.clusterers.EM: Get the number of clusters
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.FarthestFirst: gets the number of clusters to generate
getNumClusters() - Method in class weka.datagenerators.ClusterGenerator: Gets the number of clusters the dataset should have.
getNumClusters() - Method in class weka.classifiers.functions.RBFNetwork: Return the number of clusters to generate.
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
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.Generator: Gets the number of examples the dataset should have.
getNumExamplesAct() - Method in class weka.datagenerators.ClusterGenerator: 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.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.
getNumFolds() - Method in class weka.experiment.CrossValidationResultProducer: 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.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.meta.LogitBoost: Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.rules.PART: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.J48: Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.REPTree: Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.functions.SMO: Get the value of numFolds.
getNumFolds() - Method in class classifiers.PC_SMO: Get the value of numFolds.
getNumFolds() - Method in class classifiers.AlphaProb_SMO: Get the value of numFolds.
getNumGeneratingModels() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Return the number of kernels (there is one per training instance)
getNumGeneratingModels() - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Returns the number of generating models used by this DataGenerator
getNumInnerNodes() - Method in class weka.classifiers.trees.lmt.LMTNode: Method to count the number of inner nodes in the tree
getNumInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
getNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of instances in the dataset
getNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode: Return the number of instances that reach this node.
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.IteratedSingleClassifierEnhancer: Gets the number of bagging iterations
getNumIterations() - Method in class weka.classifiers.meta.MetaCost: Gets the number of bagging iterations
getNumIterations() - Method in class weka.classifiers.meta.Decorate: Gets the max number of Decorate iterations to run.
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.
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.TwoWayNumericSplit: 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.
getNumOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
getNumRegressions() - Method in class weka.classifiers.trees.lmt.LogisticBase: The number of LogitBoost iterations performed (= the number of simple regression functions fit).
getNumRegressions() - Method in class weka.classifiers.functions.SimpleLogistic: Get the number of LogitBoost iterations performed (= the number of regression functions fit by LogitBoost).
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.
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.estimators.DiscreteEstimator: Gets the number of symbols this estimator operates with
getNumSymbols() - Method in class weka.classifiers.bayes.DiscreteEstimatorBayes: Gets the number of symbols this estimator operates with
getNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets the user specified number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.ForwardSelection: Gets the number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.Ranker: Gets the number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.RaceSearch: 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
getNumTraining() - Method in class classifiers.AltDist_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.experiment.CostSensitiveClassifierSplitEvaluator: 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.classifiers.meta.CostSensitiveClassifier: Returns the directory that will be searched for cost files when loading on demand.
getOnlineMode() - Method in class evaluationMethods.OnlineEvaluation: Gets the chosen online learning mode.
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
getOptimizeBins() - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Returns whether bin optimisation is switched on.
getOption(char, String[]) - Static method in class weka.core.Utils: Gets an option indicated by a flag "-Char" from the given array of strings.
getOptions() - Method in interface weka.core.OptionHandler: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.clusterers.SimpleKMeans: Gets the current settings of SimpleKMeans
getOptions() - Method in class weka.clusterers.MakeDensityBasedClusterer: Gets the current settings of the clusterer.
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.Cobweb: Gets the current settings of Cobweb.
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.filters.supervised.attribute.AttributeSelection: Gets the current settings for the attribute selection (search, evaluator) etc.
getOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary: Gets the current settings of the filter.
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.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.RemoveType: 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.PKIDiscretize: 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.AddCluster: 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.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.ClusterMembership: 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.SwapValues: 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.AbstractTimeSeries: 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.FirstOrder: 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.Add: 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.Discretize: 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.instance.RemoveFolds: 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.Resample: 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.RemoveMisclassified: 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.experiment.CostSensitiveClassifierSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.AveragingResultProducer: 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.InstanceQuery: Gets the current settings of InstanceQuery
getOptions() - Method in class weka.experiment.Experiment: Gets the current settings of the experiment iterator.
getOptions() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CSVResultListener: Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.LearningRateResultProducer: 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.experiment.CrossValidationResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.RandomSplitResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.DatabaseResultProducer: Gets the current settings of the result producer.
getOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.ExhaustiveSearch: Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.CfsSubsetEval: Gets the current settings of CfsSubsetEval
getOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval: Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.ForwardSelection: 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.RankSearch: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.PrincipalComponents: Gets the current settings of PrincipalComponents
getOptions() - Method in class weka.attributeSelection.BestFirst: Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.OneRAttributeEval
getOptions() - Method in class weka.attributeSelection.GeneticSearch: Gets the current settings of ReliefFAttributeEval.
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.attributeSelection.Ranker: Gets the current settings of ReliefFAttributeEval.
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.ClassifierSubsetEval: Gets the current settings of ClassifierSubsetEval
getOptions() - Method in class weka.associations.Apriori: Gets the current settings of the Apriori object.
getOptions() - Method in class weka.associations.Tertius: Gets the current settings of the Tertius object.
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.IteratedSingleClassifierEnhancer: 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.RandomizableSingleClassifierEnhancer: 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.BVDecomposeSegCVSub: Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: 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.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.lazy.LWL: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.lazy.KStar: Gets the current settings of K*.
getOptions() - Method in class weka.classifiers.lazy.IBk: Gets the current settings of IBk.
getOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: 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.CVParameterSelection: 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.MultiScheme: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.FilteredClassifier: 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.AdditiveRegression: 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.MultiBoostAB: 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.Decorate: 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.LogitBoost: 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.RegressionByDiscretization: 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.misc.FLR: 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.bayes.BayesNetK2: 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.AODE: 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.ComplementNaiveBayes: 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.ConjunctiveRule: Gets the current settings of the Classifier.
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.DecisionTable: 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.rules.NNge: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.trees.J48: 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.RandomForest: Gets the current settings of the forest.
getOptions() - Method in class weka.classifiers.trees.REPTree: Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.M5P: 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.RandomTree: Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.m5.M5Base: Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.LinearRegression: 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.LeastMedSq: Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.functions.MultilayerPerceptron: Gets the current settings of NeuralNet.
getOptions() - Method in class weka.classifiers.functions.VotedPerceptron: 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.Winnow: 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.Logistic: Gets the current settings of the classifier.
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 confidenceMachine.tcm.TCMBartsRMI: Gets the current settings of TCM.
getOptions() - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Gets the current settings of TCM.
getOptions() - Method in class coreComponents.SVMToArff: Gets the current settings for data to arff conversion
getOptions() - Method in class coreComponents.DataToArff: Gets the current settings for data to arff conversion
getOptions() - Method in class evaluationMethods.CreateROCCurve: Gets the current settings for data to arff conversion
getOptions() - Method in class evaluationMethods.CalculateLoss: Gets the current settings for data to arff conversion
getOptions() - Method in class evaluationMethods.CreateReliabilityCurve: Gets the current settings for data to arff conversion
getOptions() - Method in class evaluationMethods.OnlineEvaluation: Gets the current settings of the online experiment.
getOptions() - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Gets the current settings of VPM.
getOptions() - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Gets the current settings of VPM.
getOptions() - Method in class probabilityMachine.VPMDistMetaLearner: Gets the current settings of VPM.
getOptions() - Method in class probabilityMachine.vpm.VPMNaiveBayes: Gets the current settings of VPM.
getOptions() - Method in class probabilityMachine.vpm.VPMBartsRMI2: Gets the current settings of VPM.
getOptions() - Method in class probabilityMachine.vpm.VPMBartsRMI: Gets the current settings of VPM.
getOptions() - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Gets the current settings of VPM.
getOptions() - Method in class classifiers.PC_SMO: Gets the current settings of the classifier.
getOptions() - Method in class classifiers.AlphaProb_SMO: Gets the current settings of the classifier.
getOptions() - Method in class classifiers.AltDist_IBk: Gets the current settings of IBk.
getOptions() - Method in class classifiers.usm.distance.USMWavDistance: Gets the current settings of the USM Wav Distance metric
getOptions() - Method in class classifiers.stbarts.BartsRMI: Gets the current settings of the BartsRMI classifier.
getOrderedFlag() - Method in class weka.datagenerators.BIRCHCluster: Gets the ordered flag (option O).
getOutput() - Method in class weka.datagenerators.Generator: Gets the print writer.
getOutput() - Method in class weka.datagenerators.ClusterGenerator: Gets the print writer.
getOutputFile() - Method in class weka.experiment.CSVResultListener: Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.CrossValidationResultProducer: Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.RandomSplitResultProducer: Get the value of OutputFile.
getOutputFileName() - Method in class coreComponents.SVMToArff
getOutputFileName() - Method in class coreComponents.DataToArff
getOutputFileName() - Method in class evaluationMethods.CreateROCCurve
getOutputFileName() - Method in class evaluationMethods.CalculateLoss
getOutputFileName() - Method in class evaluationMethods.CreateReliabilityCurve
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.
getOutputNums() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the output numbers.
getOutputPValuesAndProbs() - Method in class evaluationMethods.OnlineEvaluation: Reports whether p-values and probabilities are output for each example in the online experiment
getOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the outputs.
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.
getParent(int) - Method in class weka.gui.treevisualizer.Node: Get the parent edge.
GetParent(int) - Method in class weka.classifiers.bayes.ParentSet: returns index parent of parent specified by index
getParts() - Method in class weka.associations.tertius.IndividualInstance
getPassword() - Method in class weka.gui.DatabaseConnectionDialog: Returns password from dialog
getPassword() - Method in class weka.experiment.DatabaseUtils: Get the database password
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
getPerformanceStat(String, int) - Method in class evaluationMethods.OnlineEvaluation: Gets the value for a particular numeric statistic at a particular trial number.
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
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.
getPredictionOfLastExample() - Method in class evaluationMethods.OnlineEvaluation: This will return the last prediction made in the online process.
getProbabilities() - Method in class weka.gui.boundaryvisualizer.RemoteResult: Return the probability distributions for this row in the visualization
getProbability(double) - Method in class weka.estimators.DiscreteEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.PoissonEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.MahalanobisEstimator: Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.KernelEstimator: 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 interface weka.estimators.Estimator: Get a probability estimate for a value.
getProbability(double) - Method in class weka.classifiers.bayes.DiscreteEstimatorBayes: 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, double) - Method in class weka.estimators.NNConditionalEstimator: 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.KDConditionalEstimator: 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.DDConditionalEstimator: 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.DNConditionalEstimator: Get a probability estimate for a value
getProduceLatex() - Method in class weka.experiment.PairedTTester: 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
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.CrossValidationFoldMakerBeanInfo: Return the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo: Get 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
getPropertyPath() - Method in class weka.experiment.Experiment: Gets the path of properties taken to get to the custom property to iterate over.
getPruningType() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the pruning type
getQuery() - Method in class weka.experiment.InstanceQuery: Get the query to execute against the database
getRaceType() - Method in class weka.attributeSelection.RaceSearch: Get the race type
getRadiuses() - Method in class weka.datagenerators.BIRCHCluster: Gets the upper and lower boundary for the radius of the clusters.
getRandom() - Method in class weka.datagenerators.BIRCHCluster: Gets the random generator.
getRandom() - Method in class weka.datagenerators.RDG1: Gets the random generator.
getRandomizeData() - Method in class weka.experiment.RandomSplitResultProducer: Get if dataset is to be randomized
getRandomNumberGenerator(long) - Method in class weka.core.Instances: Returns a random number generator.
getRandomOrder() - Method in class weka.classifiers.bayes.BayesNetK2: Get random order flag
getRandomSeed() - Method in class weka.filters.supervised.instance.Resample: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the random seed of the random number generator
getRandomSeed() - Method in class weka.filters.unsupervised.instance.Resample: Gets the random number seed.
getRandomSeed() - Method in class weka.filters.unsupervised.instance.Randomize: Get the random number generator seed value.
getRandomSeed() - Method in class weka.classifiers.trees.ADTree: Gets random seed for a random walk.
getRandomSeed() - Method in class weka.classifiers.functions.LeastMedSq: get the seed for the random number generator
getRandomSeed() - Method in class weka.classifiers.functions.MultilayerPerceptron
getRandomSeed() - Method in class weka.classifiers.functions.SMO: Get the value of randomSeed.
getRandomSeed() - Method in class classifiers.PC_SMO: Get the value of randomSeed.
getRandomSeed() - Method in class classifiers.AlphaProb_SMO: Get the value of randomSeed.
getRandomWidthFactor() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets the multiplier when generating random codes.
getRangeCorrection() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the confidence range correction mode used.
getRanges() - Method in class weka.core.Range: Gets the string representing the selected range of values
getRankOfAnIndexedDoubles(int, double[]) - Static method in class coreComponents.GeneralUtils: Gets the ranking of a particular index in an indexed list of doubles sorts the values from lowest to highest
getRawOutput() - Method in class weka.experiment.CrossValidationResultProducer: Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.RandomSplitResultProducer: Get if raw split evaluator output is to be saved
getRawResultOutput() - Method in interface weka.experiment.SplitEvaluator: Returns the raw output for the most recent call to getResult.
getRawResultOutput() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the raw output from the classifier
getRawResultOutput() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the raw output from the classifier
getReadable() - Method in class weka.core.Tag: Gets the string description of the Tag.
getRecall() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the recall.
getReducedErrorPruning() - Method in class weka.classifiers.rules.PART: Get the value of reducedErrorPruning.
getReducedErrorPruning() - Method in class weka.classifiers.trees.J48: Get the value of reducedErrorPruning.
getRefer() - Method in class weka.gui.treevisualizer.Node: Get the value of refer.
getRefreshFreq() - Method in class weka.gui.beans.StripChart: Get the refresh frequency
getRegressionTree() - Method in class weka.classifiers.trees.m5.Rule: Get the value of regressionTree.
getRegressionTree() - Method in class weka.classifiers.trees.m5.RuleNode: Get the value of regressionTree.
getRelationName() - Method in class weka.datagenerators.Generator: Gets the relation name the dataset should have.
getRelationName() - Method in class weka.datagenerators.ClusterGenerator: Gets the relation name the dataset should have.
getRemoteHosts() - Method in class weka.experiment.RemoteExperiment: Get the list of remote host names
getRemoveAllMissingCols() - Method in class weka.associations.Apriori: Returns whether columns containing all missing values are to be removed
getRepeatLiterals() - Method in class weka.associations.Tertius: Get the value of repeatLiterals.
getReplaceMissingValues() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Gets the current setting for using ReplaceMissingValues filter
getReportFrequency() - Method in class weka.attributeSelection.GeneticSearch: get how often repports are generated
getReset() - Method in class weka.gui.beans.ChartEvent: get the value of the reset flag
getReset() - Method in class weka.classifiers.functions.MultilayerPerceptron
getResult(Instances, Instances) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in interface weka.experiment.SplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.RegressionSplitEvaluator: Gets the results for the supplied train and test datasets.
getResultFromTable(String, ResultProducer, Object[]) - Method in class weka.experiment.DatabaseUtils: Executes a database query to extract a result for the supplied key from the database.
getResultListener() - Method in class weka.experiment.Experiment: Gets the result listener where results will be sent.
getResultNames() - Method in interface weka.experiment.ResultProducer: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in interface weka.experiment.SplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.AveragingResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.LearningRateResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.CrossValidationResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.RandomSplitResultProducer: Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.DatabaseResultProducer: Gets the names of each of the columns produced for a single run.
getResultProducer() - Method in class weka.experiment.AveragingResultProducer: Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.Experiment: Get the result producer used for the current experiment.
getResultProducer() - Method in class weka.experiment.LearningRateResultProducer: Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.DatabaseResultProducer: Get the ResultProducer.
getResultSet() - Method in class weka.experiment.DatabaseUtils: Gets the results generated by a previous query.
getResultsetKeyColumns() - Method in class weka.experiment.PairedTTester: Get the value of ResultsetKeyColumns.
getResultsetName(int) - Method in class weka.experiment.PairedTTester: Gets a string descriptive of the specified resultset.
getResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseUtils: Gets the name of the experiment table that stores results from a particular ResultProducer.
getResultTypes() - Method in interface weka.experiment.ResultProducer: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in interface weka.experiment.SplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.AveragingResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.ClassifierSplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.LearningRateResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.RegressionSplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.CrossValidationResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.RandomSplitResultProducer: Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DatabaseResultProducer: Gets the data types of each of the columns produced for a single run.
getReturnValue() - Method in class weka.gui.DatabaseConnectionDialog: Returns which of OK or cancel was clicked from dialog
getRhoa() - Method in class weka.classifiers.misc.FLR: Get rhoa
getRidge() - Method in class weka.classifiers.functions.LinearRegression: Get the value of Ridge.
getRidge() - Method in class weka.classifiers.functions.Logistic: Gets the ridge in the log-likelihood.
getRidge() - Method in class weka.classifiers.functions.RBFNetwork: Gets the ridge value.
getRMIThreshold() - Method in class confidenceMachine.tcm.TCMBartsRMI: Gets the currently set RMI threshold
getRMIThreshold() - Method in class probabilityMachine.vpm.VPMBartsRMI2: Gets the currently set RMI threshold
getRMIThreshold() - Method in class probabilityMachine.vpm.VPMBartsRMI: Gets the currently set RMI threshold
getRocAnalysis() - Method in class weka.associations.Tertius: Get the value of rocAnalysis.
getROCArea(Instances) - Static method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the area under the ROC curve.
getROCString() - Method in class weka.gui.visualize.ThresholdVisualizePanel: This extracts the ROC area string
getRoot() - Method in class weka.gui.treevisualizer.Node: Get the value of root.
getRow(int) - Method in class weka.core.Matrix: Gets a row of the matrix and returns it as double array.
getRowDimension() - Method in class weka.classifiers.functions.pace.Matrix: Get row dimension.
getRowPackedCopy() - Method in class weka.classifiers.functions.pace.Matrix: Make a one-dimensional row packed copy of the internal array.
getRsource() - Method in class weka.gui.treevisualizer.Edge: Get the value of rsource.
getRtarget() - Method in class weka.gui.treevisualizer.Edge: Get the value of rtarget.
getRuleset() - Method in class weka.classifiers.rules.JRip: Get the ruleset generated by Ripper
getRuleset() - Method in class weka.classifiers.rules.RuleStats: Get the ruleset of the stats
getRulesetSize() - Method in class weka.classifiers.rules.RuleStats: Get the size of the ruleset in the stats
getRuleStats(int) - Method in class weka.classifiers.rules.JRip: Get the statistics of the ruleset in the given position
getRunColumn() - Method in class weka.experiment.PairedTTester: Get the value of RunColumn.
getRunLower() - Method in class weka.experiment.Experiment: Get the lower run number for the experiment.
getRunUpper() - Method in class weka.experiment.Experiment: Get the upper run number for the experiment.
getSampleSize() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the number of instances used for estimating attributes
getSampleSize() - Method in class weka.classifiers.functions.LeastMedSq: gets number of samples
getSampleSizePercent() - Method in class weka.filters.supervised.instance.Resample: Gets the subsample size as a percentage of the original set.
getSampleSizePercent() - Method in class weka.filters.unsupervised.instance.Resample: Gets the subsample size as a percentage of the original set.
getSaveInstanceData() - Method in class weka.clusterers.Cobweb: Get the value of saveInstances.
getSaveInstanceData() - Method in class weka.classifiers.trees.J48: Check whether instance data is to be saved.
getSaveInstanceData() - Method in class weka.classifiers.trees.ADTree: Gets whether the tree is to save instance data.
getSaveInstances() - Method in class weka.classifiers.trees.M5P: Get whether instance data is being save.
getScoreType() - Method in class weka.classifiers.bayes.BayesNet: Method declaration
getSearch() - Method in class weka.filters.supervised.attribute.AttributeSelection: Get the name of the search method
getSearch() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the search method used
getSearchPath() - Method in class weka.classifiers.trees.ADTree: Gets the method of searching the tree for a new insertion.
getSearchPercent() - Method in class weka.attributeSelection.RandomSearch: get the percentage of the search space to consider
getSearchTermination() - Method in class weka.attributeSelection.BestFirst: Get the termination criterion (number of non-improving nodes).
getSecondValueIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Get the index of the second value used.
getSecondValueIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the second value used.
getSeed() - Method in class weka.gui.beans.TrainTestSplitMaker: Get the value of the random seed
getSeed() - Method in class weka.gui.beans.CrossValidationFoldMaker: Get the currently set seed
getSeed() - Method in interface weka.core.Randomizable: Gets the seed for the random number generations
getSeed() - Method in class weka.clusterers.SimpleKMeans: Get the random number seed
getSeed() - Method in class weka.clusterers.EM: Get the random number seed
getSeed() - Method in class weka.clusterers.FarthestFirst: Get the random number seed
getSeed() - Method in class weka.datagenerators.BIRCHCluster: Gets the random number seed.
getSeed() - Method in class weka.datagenerators.RDG1: Gets the random number seed.
getSeed() - Method in class weka.filters.supervised.attribute.ClassOrder: Get the current randomization seed
getSeed() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the random number seed used for shuffling the dataset.
getSeed() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the random number seed used for shuffling the dataset.
getSeed() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the seed used for randomly sampling instances.
getSeed() - Method in class weka.attributeSelection.OneRAttributeEval: Get the random number seed
getSeed() - Method in class weka.attributeSelection.GeneticSearch: get the value of the random number generator's seed
getSeed() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the random number seed used for cross validation
getSeed() - Method in class weka.classifiers.BVDecompose: Gets the random number seed
getSeed() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.BVDecomposeSegCVSub: Gets the random number seed
getSeed() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.RandomizableClassifier: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get seed for resampling.
getSeed() - Method in class weka.classifiers.meta.MultiScheme: Gets the random number seed.
getSeed() - Method in class weka.classifiers.meta.ThresholdSelector: Gets the random number seed.
getSeed() - Method in class weka.classifiers.meta.MultiClassClassifier: Gets the random number seed.
getSeed() - Method in class weka.classifiers.meta.Decorate: Gets the seed for the random number generator.
getSeed() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Get seed for resampling.
getSeed() - Method in class weka.classifiers.rules.JRip
getSeed() - Method in class weka.classifiers.rules.ConjunctiveRule
getSeed() - Method in class weka.classifiers.rules.PART: Get the value of Seed.
getSeed() - Method in class weka.classifiers.rules.Ridor
getSeed() - Method in class weka.classifiers.trees.J48: Get the value of Seed.
getSeed() - Method in class weka.classifiers.trees.RandomForest: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.trees.REPTree: Get the value of Seed.
getSeed() - Method in class weka.classifiers.trees.RandomTree: Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.evaluation.EvaluationUtils: Gets the seed for randomization during cross-validation
getSeed() - Method in class weka.classifiers.functions.VotedPerceptron: Get the value of Seed.
getSeed() - Method in class weka.classifiers.functions.Winnow: Get the value of Seed.
getSelectedAttributes() - Method in class weka.gui.AttributeSelectionPanel: Gets an array containing the indices of all selected attributes.
getSelectedBuffer() - Method in class weka.gui.ResultHistoryPanel: Gets the buffer associated with the currently selected item in the list.
getSelectedName() - Method in class weka.gui.ResultHistoryPanel: Get the name of the currently selected item in the list
getSelectedObject() - Method in class weka.gui.ResultHistoryPanel: Gets the object associated with the currently selected item in the list.
getSelectedRange() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the value of m_SelectedRange.
getSelectedTag() - Method in class weka.core.SelectedTag: Gets the selected Tag.
getSelection() - Method in class weka.core.Range: Gets an array containing all the selected values, in the order that they were selected (or ascending order if range inversion is on)
getSelectionModel() - Method in class weka.gui.AttributeListPanel: Gets the selection model used by the table.
getSelectionModel() - Method in class weka.gui.ResultHistoryPanel: Gets the selection model used by the results list.
getSelectionModel() - Method in class weka.gui.AttributeSelectionPanel: Gets the selection model used by the table.
getSelectionThreshold() - Method in class weka.attributeSelection.RaceSearch: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getSeparatingThreshold() - Method in class weka.classifiers.functions.pace.ChisqMixture: Gets the separating threshold value.
getSeparatingThreshold() - Method in class weka.classifiers.functions.pace.NormalMixture: Gets the separating threshold value.
getSeperator() - Method in class weka.gui.HierarchyPropertyParser: Get the seperator between levels.
getSequentialAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Sequential Attribute Indexes array
getSequentialInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Sequential Instance Indexes array
getSequentialNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes in the Sequential array
getSequentialNumInstances() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of instances in the Sequential array
getSetNumber() - Method in class weka.gui.beans.TrainingSetEvent: Get the set number (eg.
getSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent: Get the set number (ie which fold this is)
getSetNumber() - Method in class weka.gui.beans.TestSetEvent: Get the test set number (eg.
getShape() - Method in class weka.gui.treevisualizer.Node: Get the value of shape.
getShowRules() - Method in class weka.classifiers.misc.FLR: Get ShowRules parameter
getShowStdDevs() - Method in class weka.experiment.PairedTTester: Returns true if standard deviations have been requested.
getShrinkage() - Method in class weka.classifiers.meta.AdditiveRegression: Get the shrinkage rate.
getShrinkage() - Method in class weka.classifiers.meta.LogitBoost: Get the value of Shrinkage.
getShuffle() - Method in class weka.classifiers.rules.Ridor
getSigma() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get the value of sigma.
getSigma() - Method in class weka.classifiers.BVDecompose: Get the calculated sigma squared
getSignificanceLevel() - Method in class weka.experiment.PairedTTester: Get the value of SignificanceLevel.
getSignificanceLevel() - Method in class weka.attributeSelection.RaceSearch: Get the significance level
getSignificanceLevel() - Method in class weka.associations.Apriori: Get the value of significanceLevel.
getSignificanceLevel() - Method in class evaluationMethods.OnlineEvaluation: Sets the significance level for a confidence classifier in an online experiment.
getSimpleStats(int) - Method in class weka.classifiers.rules.RuleStats: Get the simple stats of one rule, including 6 parameters: 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives; 4: false positives; 5: false negatives
getSIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the shape selected for creating splits.
getSineFlag() - Method in class weka.datagenerators.BIRCHCluster: Gets the sine flag (option S).
getSingleIndex() - Method in class weka.core.SingleIndex: Gets the string representing the selected range of values
getSingleModeFlag() - Method in class weka.datagenerators.BIRCHCluster: Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.RDG1: Gets the single mode flag.
getSlope() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns the slope of the function.
getSmoothing() - Method in class weka.classifiers.trees.m5.Rule: Get whether or not smoothing has been turned on
getSmoothingParameter() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Gets the smoothing value to be used to avoid zero WordGivenClass probabilities.
getSource() - Method in class weka.gui.treevisualizer.Edge: Get the value of source.
getSparseData() - Method in class weka.experiment.InstanceQuery: Gets whether data is to be returned as a set of sparse instances
getSplitByDataSet() - Method in class weka.experiment.RemoteExperiment: Returns true if sub experiments are to be created on the basis of data set..
getSplitEvaluator() - Method in class weka.experiment.CrossValidationResultProducer: Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.RandomSplitResultProducer: Get the SplitEvaluator.
getSplitOnResiduals() - Method in class weka.classifiers.trees.LMT: Get the value of splitOnResiduals.
getSplitPoint() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the split point used for numeric selection
getStartSet() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.ForwardSelection: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in interface weka.attributeSelection.StartSetHandler: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.BestFirst: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.GeneticSearch: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.Ranker: Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.RandomSearch: Returns a list of attributes (and or attribute ranges) as a String
getStaticIcon() - Method in class weka.gui.beans.BeanVisual: Returns the static icon
getStatus() - Method in class weka.gui.beans.IncrementalClassifierEvent: Get the status
getStatus() - Method in class weka.gui.beans.InstanceEvent: Get the status
getStatusMessage() - Method in class weka.experiment.TaskStatusInfo: Get the status message.
getStepSize() - Method in class weka.experiment.LearningRateResultProducer: Get the value of StepSize.
getStructure() - Method in class weka.core.converters.CSVLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in interface weka.core.converters.Loader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.C45Loader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.SerializedInstancesLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.ArffLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.AbstractLoader
getSubtreeRaising() - Method in class weka.classifiers.trees.J48: Get the value of subtreeRaising.
getSummary() - Method in class weka.gui.SetInstancesPanel: Gets the instances summary panel associated with this panel
getTags() - Method in class weka.gui.SelectedTagEditor: Gets the list of tags that can be selected from.
getTags() - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting values as tags.
getTags() - Method in class weka.gui.GenericArrayEditor: Returns null as we don't support getting values as tags.
getTags() - Method in class weka.gui.CostMatrixEditor: Some objects can return tags, but a cost matrix cannot.
getTags() - Method in class weka.core.SelectedTag: Gets the set of all valid Tags.
getTarget() - Method in class weka.gui.treevisualizer.Edge: Get the value of target.
getTaskResult() - Method in class weka.experiment.TaskStatusInfo: Get the returnable result of this task.
getTaskStatus() - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Return status information for this sub task
getTaskStatus() - Method in interface weka.experiment.Task: Clients should be able to call this method at any time to obtain information on a current task.
getTaskStatus() - Method in class weka.experiment.RemoteExperimentSubTask
getTestPredictions(Classifier, Instances) - Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.
getTestSet() - Method in class weka.gui.beans.BatchClassifierEvent: Get the test set
getTestSet() - Method in class weka.gui.beans.TestSetEvent: Get the test set instances
getText() - Method in class weka.gui.beans.TextEvent: Describe getText method here.
getText() - Method in class weka.gui.beans.BeanVisual: Get the visual's label
getTextTitle() - Method in class weka.gui.beans.TextEvent: Describe getTextTitle method here.
getTFTransform() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.
getThreshold() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the threshold for the max error when predicting a numeric class.
getThreshold() - Method in interface weka.attributeSelection.RankedOutputSearch: Gets the threshold by which attributes can be discarded.
getThreshold() - Method in class weka.attributeSelection.ForwardSelection: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold() - Method in class weka.attributeSelection.WrapperSubsetEval: Get the value of the threshold
getThreshold() - Method in class weka.attributeSelection.Ranker: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold() - Method in class weka.attributeSelection.RaceSearch: Get the threshold
getThreshold() - Method in class weka.classifiers.functions.Winnow: Get the value of Threshold.
getThreshold() - Method in class weka.classifiers.functions.PaceRegression: Gets the threshold for olsc estimator
getThresholdInstance(Instances, double) - Static method in class weka.classifiers.evaluation.ThresholdCurve: Gets the index of the instance with the closest threshold value to the desired target
getTimestamp() - Static method in class weka.experiment.CrossValidationResultProducer: Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.RandomSplitResultProducer: Gets a Double representing the current date and time.
getToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils: Gets token.
getToleranceParameter() - Method in class weka.attributeSelection.SVMAttributeEval: Get the value of T used with SMO
getToleranceParameter() - Method in class weka.classifiers.functions.SMO: Get the value of tolerance parameter.
getToleranceParameter() - Method in class weka.classifiers.functions.SMOreg: Get the value of tolerance parameter.
getToleranceParameter() - Method in class classifiers.PC_SMO: Get the value of tolerance parameter.
getToleranceParameter() - Method in class classifiers.AlphaProb_SMO: Get the value of tolerance parameter.
getToolTipText(MouseEvent) - Method in class weka.gui.AttributeVisualizationPanel: Returns "<nominal value> [<nominal value count>]" if displaying a bar plot and mouse is on some bar.
getTop() - Method in class weka.gui.treevisualizer.Node: Get the value of top.
getTotalCount(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of nodes there are.
getTotalGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of groups of siblings there are.
getTotalHeight(Node, int) - Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of levels there are.
getTPRate() - Method in class weka.associations.tertius.Rule: Get the rate of True Positive instances of this rule.
getTrainingSet() - Method in class weka.gui.beans.TrainingSetEvent: Get the training instances
getTrainingTime() - Method in class weka.classifiers.functions.MultilayerPerceptron
getTrainIterations() - Method in class weka.classifiers.BVDecompose: Gets the maximum number of boost iterations
getTrainPercent() - Method in class weka.gui.beans.TrainTestSplitMaker: Get the percentage of the data that will be in the training portion of the split
getTrainPercent() - Method in class weka.experiment.RandomSplitResultProducer: Get the value of TrainPercent.
getTrainPoolSize() - Method in class weka.classifiers.BVDecompose: Get the number of instances in the training pool.
getTrainSize() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the training size
getTrainTestPredictions(Classifier, Instances, Instances) - Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.
getTransformBackToOriginal() - Method in class weka.attributeSelection.PrincipalComponents: Gets whether the data is to be transformed back to the original space.
getTrimingThreshold() - Method in class weka.classifiers.functions.pace.ChisqMixture: Gets the triming thresholding value.
getTrimingThreshold() - Method in class weka.classifiers.functions.pace.NormalMixture: Gets the triming thresholding value.
getTrueNegative() - Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of negative instances predicted as negative
getTruePositive() - Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of positive instances predicted as positive
getTruePositiveRate() - Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the true positive rate.
getTwoClassStats(int) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the performance with respect to one of the classes as a TwoClassStats object.
getType() - Method in class weka.associations.tertius.IndividualLiteral
getType() - Method in class weka.associations.tertius.LiteralSet: Give the type of properties in this set (individual or part properties).
getType() - Method in class weka.classifiers.functions.neural.NeuralConnection
getU() - Method in class weka.core.Matrix: Returns the U part of the matrix.
getUnpruned() - Method in class weka.classifiers.rules.PART: Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.J48: Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.m5.Rule: Get whether unpruned tree/rules are being generated
getUnpruned() - Method in class weka.classifiers.trees.m5.M5Base: Get whether unpruned tree/rules are being generated
getUpdateIncrementalClassifier() - Method in class weka.gui.beans.Classifier
getUpper() - Method in class weka.gui.experiment.RunNumberPanel: Gets the current upper run number.
getUpperBoundMinSupport() - Method in class weka.associations.Apriori: Get the value of upperBoundMinSupport.
getUpperNumericBound() - Method in class weka.core.Attribute: Returns the upper bound of a numeric attribute.
getUpperSize() - Method in class weka.experiment.LearningRateResultProducer: Get the value of UpperSize.
getURL() - Method in class weka.gui.DatabaseConnectionDialog: Returns URL from dialog
getUseADTree() - Method in class weka.classifiers.bayes.BayesNet: Method declaration
getUseBetterEncoding() - Method in class weka.filters.supervised.attribute.Discretize: Gets whether better encoding is to be used for MDL.
getUseCrossValidation() - Method in class weka.classifiers.functions.SimpleLogistic: Get the value of useCrossValidation.
getUsedAttributes() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns an array of the indices of the attributes used in the logistic model.
getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Get the value of UseEqualFrequency.
getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.Discretize: Get the value of UseEqualFrequency.
getUseIBk() - Method in class weka.classifiers.rules.DecisionTable: Gets whether IBk is being used instead of the majority class
getUseKernelEstimator() - Method in class weka.classifiers.bayes.NaiveBayes: Gets if kernel estimator is being used.
getUseKononenko() - Method in class weka.filters.supervised.attribute.Discretize: Gets whether Kononenko's MDL criterion is to be used.
getUseLaplace() - Method in class weka.classifiers.trees.J48: Get the value of useLaplace.
getUseMissing() - Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the flag if missing values are treated as extra values.
getUsePropertyIterator() - Method in class weka.experiment.Experiment: Gets whether the custom property iterator should be used.
getUsePruning() - Method in class weka.classifiers.rules.JRip
getUseRBF() - Method in class weka.classifiers.functions.SMO: Check if the RBF kernel is to be used.
getUseRBF() - Method in class weka.classifiers.functions.SMOreg: Check if the RBF kernel is to be used.
getUseRBF() - Method in class classifiers.PC_SMO: Check if the RBF kernel is to be used.
getUseRBF() - Method in class classifiers.AlphaProb_SMO: Check if the RBF kernel is to be used.
getUseResampling() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.AdaBoostM1: Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.LogitBoost: Get whether resampling is turned on
getUsername() - Method in class weka.gui.DatabaseConnectionDialog: Returns Username from dialog
getUsername() - Method in class weka.experiment.DatabaseUtils: Get the database username
getUseStoplist() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether if the words on the stoplist are to be ignored (The stoplist is in weka.core.StopWords).
getUseSupervisedDiscretization() - Method in class weka.classifiers.bayes.NaiveBayes: Get whether supervised discretization is to be used.
getUseTraining() - Method in class weka.attributeSelection.ClassifierSubsetEval: Get if training data is to be used instead of hold out/test data
getUseTree() - Method in class weka.classifiers.trees.m5.Rule: get whether an m5 tree is being used rather than rules
getUseUnsmoothed() - Method in class weka.classifiers.trees.m5.M5Base: Get whether or not smoothing is being used
getValidationChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the validation chunk size
getValidationSetSize() - Method in class weka.classifiers.functions.MultilayerPerceptron
getValidationThreshold() - Method in class weka.classifiers.functions.MultilayerPerceptron
getValue() - Method in class weka.gui.GenericObjectEditor: Gets the current Object.
getValue() - Method in class weka.gui.GenericArrayEditor: Gets the current object array.
getValue() - Method in class weka.gui.HierarchyPropertyParser: Get the value of current node
getValue() - Method in class weka.gui.CostMatrixEditor: Gets the cost matrix that is being edited.
getValue() - Method in class weka.classifiers.trees.adtree.PredictionNode: Gets the prediction value of the node.
getValueIndices() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the indices of the indicator values.
getValueRange() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the range containing the indicator values.
getValues() - Method in class weka.gui.visualize.VisualizePanelEvent
getValuesOutput() - Method in class weka.associations.Tertius: Get the value of valuesOutput.
getVarbValues() - Method in class weka.core.Optimization: Get the variable values.
getVariance() - Method in class weka.classifiers.BVDecompose: Get the calculated variance
getVarianceCovered() - Method in class weka.attributeSelection.PrincipalComponents: Gets the proportion of total variance to account for when retaining principal components
getVerbose() - Method in class weka.attributeSelection.ExhaustiveSearch: get whether or not output is verbose
getVerbose() - Method in class weka.attributeSelection.RandomSearch: get whether or not output is verbose
getVisible() - Method in class weka.gui.treevisualizer.Node: Get the value of visible.
getVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer: Get the visual for this bean
getVisual() - Method in class weka.gui.beans.StripChart: Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.Classifier: Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.Filter: Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.AbstractDataSink: Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.TextViewer: Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.PredictionAppender: Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.ClassAssigner
getVisual() - Method in class weka.gui.beans.AbstractDataSource: Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.GraphViewer: Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.AbstractEvaluator: Get the visual
getVisual() - Method in interface weka.gui.beans.Visible: Get the visual representation
getVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Get the visual for this bean
getVisual() - Method in class weka.gui.beans.AbstractTestSetProducer: Get the visual for this bean
getVisual() - Method in class weka.gui.beans.DataVisualizer: Return the visual appearance of this bean
getVoteFlag() - Method in class weka.datagenerators.RDG1: Gets the vote flag.
getWBias() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated bias according to the Webb definition
getWeightByConfidence() - Method in class weka.classifiers.misc.VFI: Get whether feature intervals are being weighted by confidence
getWeightByDistance() - Method in class weka.attributeSelection.ReliefFAttributeEval: Get whether nearest neighbours are being weighted by distance
getWeightingKernel() - Method in class weka.classifiers.lazy.LWL: Gets the kernel weighting method to use.
getWeights() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
getWeights() - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Get weights
getWeights() - Method in class weka.classifiers.functions.neural.NeuralNode: call this function to get the weights array.
getWeightThreshold() - Method in class weka.classifiers.meta.AdaBoostM1: Get the degree of weight thresholding
getWeightThreshold() - Method in class weka.classifiers.meta.LogitBoost: Get the degree of weight thresholding
getWholeDataErr() - Method in class weka.classifiers.rules.Ridor
getWidth() - Method in class weka.gui.beans.BeanInstance: Gets the width of this bean
getWindowSize() - Method in class weka.classifiers.lazy.IBk: Gets the maximum number of instances allowed in the training pool.
getWindowSize() - Method in class classifiers.AltDist_IBk: Gets the maximum number of instances allowed in the training pool.
getWordsToKeep() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets the number of words (per class if there is a class attribute assigned) to attempt to keep.
getWrappedAlgorithm() - Method in class weka.gui.beans.Loader: Get the loader
getWrappedAlgorithm() - Method in class weka.gui.beans.Classifier: Returns the wrapped classifier
getWrappedAlgorithm() - Method in class weka.gui.beans.Filter: Get the filter wrapped by this bean
getWrappedAlgorithm() - Method in interface weka.gui.beans.WekaWrapper: Get the algorithm
getWVariance() - Method in class weka.classifiers.BVDecomposeSegCVSub: Get the calculated variance according to the Webb definition
getX() - Method in class weka.gui.beans.BeanInstance: Gets the x coordinate of this bean
getX() - Method in class weka.classifiers.functions.neural.NeuralConnection
getXindex() - Method in class weka.gui.visualize.PlotData2D: Get the currently set x index of the data
getXIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute on the x axis
getXLabelFreq() - Method in class weka.gui.beans.StripChart: Get the frequency by which x axis values are printed
getY() - Method in class weka.gui.beans.BeanInstance: Gets the y coordinate of this bean
getY() - Method in class weka.classifiers.functions.neural.NeuralConnection
getYindex() - Method in class weka.gui.visualize.PlotData2D: Get the currently set y index of the data
getYIndex() - Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute on the y axis
globalBlendTipText() - Method in class weka.classifiers.lazy.KStar: Returns the tip text for this property
globalInfo() - Method in class weka.gui.beans.TrainingSetMaker: Global info for this bean
globalInfo() - Method in class weka.gui.beans.TrainTestSplitMaker: Global info for this bean
globalInfo() - Method in class weka.gui.beans.AttributeSummarizer: Global info for this bean
globalInfo() - Method in class weka.gui.beans.StripChart: Global info for this bean
globalInfo() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Global info for this bean
globalInfo() - Method in class weka.gui.beans.ScatterPlotMatrix: Global info for this bean
globalInfo() - Method in class weka.gui.beans.TextViewer: Global info for this bean
globalInfo() - Method in class weka.gui.beans.PredictionAppender: Global description of this bean
globalInfo() - Method in class weka.gui.beans.ClassAssigner: Global info for this bean
globalInfo() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Global info for this bean
globalInfo() - Method in class weka.gui.beans.TestSetMaker: Global info for this bean
globalInfo() - Method in class weka.gui.beans.GraphViewer: Global info for this bean
globalInfo() - Method in class weka.gui.beans.DataVisualizer: Global info for this bean
globalInfo() - Method in class weka.gui.beans.CrossValidationFoldMaker: Global info for this bean
globalInfo() - Method in class weka.core.converters.CSVLoader: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.core.converters.C45Loader: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.clusterers.SimpleKMeans: Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.EM: Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.FarthestFirst: Returns a string describing this clusterer
globalInfo() - Method in class weka.datagenerators.BIRCHCluster: Returns a string describing this data generator.
globalInfo() - Method in class weka.datagenerators.RDG1: Returns a string describing this data generator.
globalInfo() - Method in class weka.filters.AllFilter: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.attribute.Discretize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.instance.Resample: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns a string describing this filter
globalInfo() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Obfuscate: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Normalize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.SwapValues: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Remove: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Standardize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Copy: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Add: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.Resample: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.instance.Randomize: Returns a string describing this classifier
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse: Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns a string describing this classifier
globalInfo() - Method in class weka.experiment.DatabaseResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.AveragingResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CSVResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.InstancesResultListener: Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.LearningRateResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RegressionSplitEvaluator: Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CrossValidationResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RandomSplitResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.DatabaseResultProducer: Returns a string describing this result producer
globalInfo() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.CfsSubsetEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ForwardSelection: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.RankSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ConsistencySubsetEval: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.PrincipalComponents: Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.BestFirst: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.GeneticSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.Ranker: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.RaceSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.RandomSearch: Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.associations.Apriori: Returns a string describing this associator
globalInfo() - Method in class weka.associations.Tertius: Returns a string describing this associator.
globalInfo() - Method in class weka.classifiers.lazy.LBR
globalInfo() - Method in class weka.classifiers.lazy.LWL: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.IB1: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.KStar: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.lazy.IBk: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
globalInfo() - Method in class weka.classifiers.meta.Bagging: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.CVParameterSelection: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.meta.MetaCost: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MultiScheme: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.Grading: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.ClassificationViaRegression: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.ThresholdSelector
globalInfo() - Method in class weka.classifiers.meta.FilteredClassifier: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.meta.MultiClassClassifier
globalInfo() - Method in class weka.classifiers.meta.AdditiveRegression: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.meta.Vote: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.Stacking: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MultiBoostAB: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.meta.Decorate: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.AdaBoostM1: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.RandomCommittee: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.StackingC: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.LogitBoost: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.CostSensitiveClassifier
globalInfo() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.misc.HyperPipes: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.misc.FLR: Returns a description of the classifier suitable for displaying in the explorer/experimenter gui
globalInfo() - Method in class weka.classifiers.misc.VFI: Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.bayes.BayesNetK2: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.BayesNetB: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.AODE: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesSimple: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.BayesNetB2: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayes: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesUpdateable: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.rules.ZeroR: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.JRip: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.M5Rules: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.OneR: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.Prism: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.PART: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.DecisionTable: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.Ridor: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.rules.NNge: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.J48: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.ADTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.RandomForest: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.DecisionStump: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.UserClassifier: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.trees.Id3: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.REPTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.LMT: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.trees.RandomTree: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.LinearRegression: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.SimpleLogistic: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.LeastMedSq: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.MultilayerPerceptron: This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.functions.VotedPerceptron: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.SMO: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.Winnow: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.SMOreg: Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.functions.Logistic: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.PaceRegression: Returns a string describing this classifier
globalInfo() - Method in class weka.classifiers.functions.RBFNetwork: Returns a string describing this classifier
globalInfo() - Method in class classifiers.PC_SMO: Returns a string describing classifier
globalInfo() - Method in class classifiers.AlphaProb_SMO: Returns a string describing classifier
goDown(String) - Method in class weka.gui.HierarchyPropertyParser: Go to a certain node of the tree down from the current node according to the specified relative path.
goTo(String) - Method in class weka.gui.HierarchyPropertyParser: Go to a certain node of the tree according to the specified path Note that the path must be absolute path from the root.
goToChild(int) - Method in class weka.gui.HierarchyPropertyParser: Go to one child node from the current position in the tree according to the given position
goToChild(String) - Method in class weka.gui.HierarchyPropertyParser: Go to one child node from the current position in the tree according to the given value
If the child node with the given value cannot be found it returns false, true otherwise.
goToParent() - Method in class weka.gui.HierarchyPropertyParser: Go to the parent from the current position in the tree If the current position is the root, it stays there and does not move
goToRoot() - Method in class weka.gui.HierarchyPropertyParser: Go to the root of the tree
gr(double, double) - Static method in class weka.core.Utils: Tests if a is smaller than b.
Grading - class weka.classifiers.meta.Grading.: Implements Grading.
Grading() - Constructor for class weka.classifiers.meta.Grading
graph() - Method in interface weka.core.Drawable: Returns a string that describes a graph representing the object.
graph() - Method in class weka.clusterers.Cobweb: Generates the graph string of the Cobweb tree
graph() - Method in class weka.classifiers.meta.CVParameterSelection: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.ThresholdSelector: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.FilteredClassifier: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.bayes.BayesNet: Returns a BayesNet graph in XMLBIF ver 0.3 format.
graph() - Method in class weka.classifiers.trees.J48: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.ADTree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.UserClassifier
graph() - Method in class weka.classifiers.trees.REPTree: Outputs the decision tree as a graph
graph() - Method in class weka.classifiers.trees.M5P: Return a dot style String describing the tree.
graph() - Method in class weka.classifiers.trees.LMT: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns graph describing the tree.
graph(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode: Assign a unique identifier to each node in the tree and then calls graphTree
GraphConstants - interface weka.gui.graphvisualizer.GraphConstants.: GraphConstants.java
GraphEdge - class weka.gui.graphvisualizer.GraphEdge.: This class represents an edge in the graph
GraphEdge(int, int, int) - Constructor for class weka.gui.graphvisualizer.GraphEdge
GraphEdge(int, int, int, String, String) - Constructor for class weka.gui.graphvisualizer.GraphEdge
GraphEvent - class weka.gui.beans.GraphEvent.: Event for graphs
GraphEvent(Object, String, String) - Constructor for class weka.gui.beans.GraphEvent: Creates a new GraphEvent instance.
GraphListener - interface weka.gui.beans.GraphListener.: Describe interface TextListener here.
GraphNode - class weka.gui.graphvisualizer.GraphNode.: This class represents a node in the Graph.
GraphNode(String, String) - Constructor for class weka.gui.graphvisualizer.GraphNode: Constructor
GraphNode(String, String, int) - Constructor for class weka.gui.graphvisualizer.GraphNode: Constructor
graphType() - Method in interface weka.core.Drawable: Returns the type of graph representing the object.
graphType() - Method in class weka.clusterers.Cobweb: Returns the type of graphs this class represents
graphType() - Method in class weka.classifiers.meta.CVParameterSelection: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.ThresholdSelector: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.FilteredClassifier: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.bayes.BayesNet: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.J48: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.ADTree: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.UserClassifier: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.REPTree: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.M5P: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.LMT: Returns the type of graph this classifier represents.
graphType() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns the type of graph this classifier represents.
GraphViewer - class weka.gui.beans.GraphViewer.: A bean encapsulating weka.gui.treevisualize.TreeVisualizer
GraphViewer() - Constructor for class weka.gui.beans.GraphViewer
GraphViewerBeanInfo - class weka.gui.beans.GraphViewerBeanInfo.: Bean info class for the graph viewer
GraphViewerBeanInfo() - Constructor for class weka.gui.beans.GraphViewerBeanInfo
GraphVisualizer - class weka.gui.graphvisualizer.GraphVisualizer.: This class displays the graph we want to visualize.
GraphVisualizer() - Constructor for class weka.gui.graphvisualizer.GraphVisualizer: Constructor
Sets up the gui and initializes all the other previously uninitialized variables.
GRID - Static variable in class weka.datagenerators.BIRCHCluster
grOrEq(double, double) - Static method in class weka.core.Utils: Tests if a is greater or equal to b.
grouping(boolean) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
grow(Instances) - Method in class weka.classifiers.rules.Rule: Build this rule
GUIChooser - class weka.gui.GUIChooser.: The main class for the Weka GUIChooser.
GUIChooser() - Constructor for class weka.gui.GUIChooser: Creates the experiment environment gui with no initial experiment
GUITipText() - Method in class weka.classifiers.functions.MultilayerPerceptron

H

h(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of h(x) given the mixture.
h(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Computes the value of h(x) given the mixture.
h(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of h(x) given the mixture, where x is a vector.
h(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Computes the value of h(x) given the mixture, where x is a vector.
h1(int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Constructs single Householder transformation for a column
h2(int, int, double, PaceMatrix, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Performs single Householder transformation on one column of a matrix
hasAntds() - Method in class weka.classifiers.rules.Rule: Whether this rule has antecedents, i.e.
hasAntds() - Method in class weka.classifiers.rules.ConjunctiveRule: Whether this rule has antecedents, i.e.
hasFalseHead() - Method in class weka.associations.tertius.Rule: Test if the head of the rule is false.
hash - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: attribute value hash code
hashCode() - Method in class weka.core.SerializedObject: Returns a hashcode for this object.
hashCode() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Calculates a hash code
hashCode() - Method in class weka.associations.ItemSet: Produces a hash code for a item set.
hashCode() - Method in class weka.classifiers.rules.DecisionTable.hashKey: Calculates a hash code
hasIncomingBatchInstances() - Method in class weka.gui.beans.Classifier: Returns true if this classifier has an incoming connection that is a batch set of instances
hasIncomingStreamInstances() - Method in class weka.gui.beans.Classifier: Returns true if this classifier has an incoming connection that is an instance stream
hasMaxCounterInstances() - Method in class weka.associations.tertius.LiteralSet: Test if all the intances are counter-instances.
hasModels() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
hasMoreElements() - Method in class weka.core.FastVector.FastVectorEnumeration: Tests if there are any more elements to enumerate.
hasMoreIterations() - Method in class weka.experiment.Experiment: Returns true if there are more iterations to carry out in the experiment.
hasNext() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
hasPrevious() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
hasTrueBody() - Method in class weka.associations.tertius.Rule: Test if the body of the rule is true.
hasZeropoint() - Method in class weka.core.Attribute: Returns whether the attribute has a zeropoint and may be added meaningfully.
Head - class weka.associations.tertius.Head.: Class representing the head of a rule.
Head() - Constructor for class weka.associations.tertius.Head: Constructor without storing the counter-instances.
Head(Instances) - Constructor for class weka.associations.tertius.Head: Constructor storing the counter-instances.
headContains(Literal) - Method in class weka.associations.tertius.Rule: Test if the head of the rule contains a literal.
header(int) - Method in class weka.experiment.PairedTTester: Creates a "header" string describing the current resultsets.
heuristicStopTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
hf(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Computes the value of h(x) / f(x) given the mixture.
hf(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Computes the value of h(x) / f(x) given the mixture.
hiddenLayersTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
HierarchicalBCEngine - class weka.gui.graphvisualizer.HierarchicalBCEngine.: This class lays out the vertices of a graph in a hierarchy of vertical levels, with a number of nodes in each level.
HierarchicalBCEngine() - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine: SimpleConstructor If we want to instantiate the class first, and if information for nodes and edges is not available.
HierarchicalBCEngine(FastVector, FastVector, int, int) - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine: Constructor - takes in FastVectors of nodes and edges, and the initial width and height of a node
HierarchicalBCEngine(FastVector, FastVector, int, int, boolean) - Constructor for class weka.gui.graphvisualizer.HierarchicalBCEngine: Constructor - takes in FastVectors of nodes and edges, the initial width and height of a node, and a boolean value to indicate if the edges should be concentrated.
HierarchyPropertyParser - class weka.gui.HierarchyPropertyParser.: This class implements a parser to read properties that have a hierarchy(i.e.
HierarchyPropertyParser() - Constructor for class weka.gui.HierarchyPropertyParser: Default constructor
HierarchyPropertyParser(String, String) - Constructor for class weka.gui.HierarchyPropertyParser: Constructor that builds a tree from the given property with the given delimitor
HLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
holdOutFileTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the tip text for this property
HoldOutSubsetEvaluator - class weka.attributeSelection.HoldOutSubsetEvaluator.: Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
HoldOutSubsetEvaluator() - Constructor for class weka.attributeSelection.HoldOutSubsetEvaluator
hornClausesTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
HostListPanel - class weka.gui.experiment.HostListPanel.: This panel controls setting a list of hosts for a RemoteExperiment to use.
HostListPanel() - Constructor for class weka.gui.experiment.HostListPanel: Create the host list panel initially disabled.
HostListPanel(RemoteExperiment) - Constructor for class weka.gui.experiment.HostListPanel: Creates the host list panel with the given experiment.
HyperPipes - class weka.classifiers.misc.HyperPipes.: Class implementing a HyperPipe classifier.
HyperPipes() - Constructor for class weka.classifiers.misc.HyperPipes
hypot(double, double) - Static method in class weka.classifiers.functions.pace.Maths: sqrt(a^2 + b^2) without under/overflow.

I

IB1 - class weka.classifiers.lazy.IB1.: IB1-type classifier.
IB1() - Constructor for class weka.classifiers.lazy.IB1
IBk - class weka.classifiers.lazy.IBk.: K-nearest neighbours classifier.
IBk() - Constructor for class weka.classifiers.lazy.IBk: IB1 classifer.
IBk(int) - Constructor for class weka.classifiers.lazy.IBk: IBk classifier.
ICON_PATH - Static variable in class weka.gui.beans.BeanVisual
Id3 - class weka.classifiers.trees.Id3.: Class implementing an Id3 decision tree classifier.
Id3() - Constructor for class weka.classifiers.trees.Id3
identity(int, int) - Static method in class weka.classifiers.functions.pace.Matrix: Generate identity matrix
IDFTransformTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property
IDLE - Static variable in class weka.gui.beans.BeanInstance
ignoredAttributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns the tip text for this property
ignoredAttributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns the tip text for this property
IMPLICIT - Static variable in class weka.associations.Tertius
Impurity - class weka.classifiers.trees.m5.Impurity.: Class for handling the impurity values when spliting the instances
Impurity(int, int, Instances, int) - Constructor for class weka.classifiers.trees.m5.Impurity: Constructs an Impurity object containing the impurity values of partitioning the instances using an attribute
incompleteBeta(double, double, double) - Static method in class weka.core.Statistics: Returns the Incomplete Beta Function evaluated from zero to xx.
incorrect() - Method in class weka.classifiers.Evaluation: Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incorrect() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of incorrect classifications (that is, for which an incorrect prediction was made).
incorrect() - Method in class evaluationMethods.EstimatorEvaluation: Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incremental(double, int) - Method in class weka.classifiers.trees.m5.Impurity: Incrementally computes the impurirty values
IncrementalClassifierEvaluator - class weka.gui.beans.IncrementalClassifierEvaluator.: Bean that evaluates incremental classifiers
IncrementalClassifierEvaluator() - Constructor for class weka.gui.beans.IncrementalClassifierEvaluator
IncrementalClassifierEvaluatorBeanInfo - class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo.: Bean info class for the incremental classifier evaluator bean
IncrementalClassifierEvaluatorBeanInfo() - Constructor for class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
IncrementalClassifierEvent - class weka.gui.beans.IncrementalClassifierEvent.: Class encapsulating an incrementally built classifier and current instance
IncrementalClassifierEvent(Object) - Constructor for class weka.gui.beans.IncrementalClassifierEvent
IncrementalClassifierEvent(Object, Classifier, Instance, int) - Constructor for class weka.gui.beans.IncrementalClassifierEvent: Creates a new IncrementalClassifierEvent instance.
IncrementalClassifierListener - interface weka.gui.beans.IncrementalClassifierListener.: Interface to something that can process a IncrementalClassifierEvent
IncrementalLoader - interface weka.core.converters.IncrementalLoader.: Marker interface for a loader that can retrieve instances incrementally
index() - Method in class weka.core.Attribute: Returns the index of this attribute.
index() - Method in class coreComponents.DoubleWithIndex: returns the index
index(int) - Method in class weka.core.Instance: Returns the index of the attribute stored at the given position.
index(int) - Method in class weka.core.SparseInstance: Returns the index of the attribute stored at the given position.
indexOf(Literal) - Method in class weka.associations.tertius.Predicate
indexOf(Object) - Method in class weka.core.FastVector: Searches for the first occurence of the given argument, testing for equality using the equals method.
indexOfMax() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the index of the maximum.
indexOfValue(String) - Method in class weka.core.Attribute: Returns the index of a given attribute value.
indexToString(int) - Static method in class weka.core.SingleIndex: Creates a string representation of the given index.
indicesToRangeList(int[]) - Static method in class weka.core.Range: Creates a string representation of the indices in the supplied array.
INDIVIDUAL_PROPERTY - Static variable in class weka.associations.tertius.IndividualLiteral
IndividualInstance - class weka.associations.tertius.IndividualInstance.
IndividualInstance(IndividualInstance) - Constructor for class weka.associations.tertius.IndividualInstance
IndividualInstance(Instance, Instances) - Constructor for class weka.associations.tertius.IndividualInstance
IndividualInstances - class weka.associations.tertius.IndividualInstances.
IndividualInstances(Instances, Instances) - Constructor for class weka.associations.tertius.IndividualInstances
IndividualLiteral - class weka.associations.tertius.IndividualLiteral.
IndividualLiteral(Predicate, String, int, int, int, int) - Constructor for class weka.associations.tertius.IndividualLiteral
individualPredictions(Instance) - Method in class weka.classifiers.meta.MultiClassClassifier: Returns the individual predictions of the base classifiers for an instance.
info(int[]) - Static method in class weka.core.Utils: Computes entropy for an array of integers.
infoGain() - Method in class weka.classifiers.trees.j48.C45Split: Returns (C4.5-type) information gain for the generated split.
infoGain() - Method in class weka.classifiers.trees.j48.BinC45Split: Returns (C4.5-type) information gain for the generated split.
InfoGainAttributeEval - class weka.attributeSelection.InfoGainAttributeEval.: Class for Evaluating attributes individually by measuring information gain with respect to the class.
InfoGainAttributeEval() - Constructor for class weka.attributeSelection.InfoGainAttributeEval: Constructor
InfoGainSplitCrit - class weka.classifiers.trees.j48.InfoGainSplitCrit.: Class for computing the information gain for a given distribution.
InfoGainSplitCrit() - Constructor for class weka.classifiers.trees.j48.InfoGainSplitCrit
initAsNaiveBayesTipText() - Method in class weka.classifiers.bayes.BayesNet
initClassifier(Instances) - Method in interface weka.classifiers.IterativeClassifier: Inits an iterative classifier.
initClassifier(Instances) - Method in class weka.classifiers.trees.ADTree: Sets up the tree ready to be trained, using two-class optimized method.
INITIAL_STEP - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
initialize() - Method in class weka.experiment.Experiment: Prepares an experiment for running, initializing current iterator settings.
initialize() - Method in class weka.experiment.RemoteExperiment: Prepares a remote experiment for running, creates sub experiments
initialize() - Method in class weka.classifiers.CostMatrix: Sets the cost of all correct classifications to 0, and all misclassifications to 1.
initialize() - Method in class weka.classifiers.trees.j48.Distribution: Sets all counts to zero.
initialize(int, int, int) - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Resets the object of split information
initialize(int, int, int) - Method in class weka.classifiers.trees.m5.YongSplitInfo: Resets the object of split information
initInternalFields() - Method in class weka.gui.visualize.MatrixPanel: Initializes internal data fields, i.e.
initStructure() - Method in class weka.classifiers.bayes.BayesNet: Init structure initializes the structure to an empty graph or a Naive Bayes graph (depending on the -N flag).
innerProduct(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the inner product of two DoubleVectors
INPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is an input unit.
input(Instance) - Method in class weka.gui.streams.InstanceJoiner
input(Instance) - Method in class weka.gui.streams.InstanceCounter
input(Instance) - Method in class weka.gui.streams.InstanceSavePanel
input(Instance) - Method in class weka.gui.streams.InstanceViewer
input(Instance) - Method in class weka.gui.streams.InstanceTable
input(Instance) - Method in class weka.filters.NullFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.Filter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.AllFilter: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.AttributeSelection: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.NominalToBinary: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.ClassOrder: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.Discretize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.Resample: Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.SpreadSubsample: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Obfuscate: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RemoveType: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.StringToNominal: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddCluster: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Normalize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddExpression: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AddNoise: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.SwapValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Remove: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Standardize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Copy: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Add: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Discretize: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.SparseToNonSparse: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.Resample: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.NonSparseToSparse: Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Input an instance for filtering.
inputFileSpecified() - Method in class coreComponents.SVMToArff
inputFileSpecified() - Method in class coreComponents.DataToArff
inputFileSpecified() - Method in class evaluationMethods.CreateROCCurve
inputFileSpecified() - Method in class evaluationMethods.CalculateLoss
inputFileSpecified() - Method in class evaluationMethods.CreateReliabilityCurve
inputFormat(Instances) - Method in class weka.gui.streams.InstanceJoiner: Sets the format of the input instances.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceCounter
inputFormat(Instances) - Method in class weka.gui.streams.InstanceSavePanel
inputFormat(Instances) - Method in class weka.gui.streams.InstanceViewer
inputFormat(Instances) - Method in class weka.gui.streams.InstanceTable
inputFormat(Instances) - Method in class weka.filters.Filter: Deprecated. use setInputFormat(Instances) instead.
insert(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Inserts a new entry in the hashtable using the specified key.
insert(int) - Method in class weka.classifiers.functions.supportVector.SMOset: Inserts an element into the set.
insertAttributeAt(Attribute, int) - Method in class weka.core.Instances: Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertAttributeAt(int) - Method in class weka.core.Instance: Inserts an attribute at the given position (0 to numAttributes()).
insertElementAt(Object, int) - Method in class weka.core.FastVector: Inserts an element at the given position.
installLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode: Traverses the tree and installs linear models at each node.
installSmoothedModels() - Method in class weka.classifiers.trees.m5.RuleNode
Instance - class weka.core.Instance.: Class for handling an instance.
INSTANCE_AVAILABLE - Static variable in class weka.gui.beans.InstanceEvent
INSTANCE_AVAILABLE - Static variable in class weka.gui.streams.InstanceEvent: Specifies that an instance is available
Instance(double, double[]) - Constructor for class weka.core.Instance: Constructor that inititalizes instance variable with given values.
Instance(Instance) - Constructor for class weka.core.Instance: Constructor that copies the attribute values and the weight from the given instance.
instance(int) - Method in class weka.core.Instances: Returns the instance at the given position.
Instance(int) - Constructor for class weka.core.Instance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
InstanceCounter - class weka.gui.streams.InstanceCounter.: A bean that counts instances streamed to it.
InstanceCounter() - Constructor for class weka.gui.streams.InstanceCounter
InstanceEvent - class weka.gui.beans.InstanceEvent.: Event that encapsulates a single instance
InstanceEvent - class weka.gui.streams.InstanceEvent.: An event encapsulating an instance stream event.
InstanceEvent(Object) - Constructor for class weka.gui.beans.InstanceEvent
InstanceEvent(Object, Instance, int) - Constructor for class weka.gui.beans.InstanceEvent: Creates a new InstanceEvent instance.
InstanceEvent(Object, int) - Constructor for class weka.gui.streams.InstanceEvent: Constructs an InstanceEvent with the specified source object and event type
InstanceJoiner - class weka.gui.streams.InstanceJoiner.: A bean that joins two streams of instances into one.
InstanceJoiner() - Constructor for class weka.gui.streams.InstanceJoiner: Setup the initial states of the member variables
InstanceListener - interface weka.gui.beans.InstanceListener.: Interface to something that can accept instance events
InstanceListener - interface weka.gui.streams.InstanceListener.: An interface for objects interested in listening to streams of instances.
InstanceLoader - class weka.gui.streams.InstanceLoader.: A bean that produces a stream of instances from a file.
InstanceLoader() - Constructor for class weka.gui.streams.InstanceLoader
instanceProduced(InstanceEvent) - Method in interface weka.gui.streams.InstanceListener
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceCounter
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceSavePanel
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceViewer
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceTable
InstanceProducer - interface weka.gui.streams.InstanceProducer.: An interface for objects capable of producing streams of instances.
InstanceQuery - class weka.experiment.InstanceQuery.: Convert the results of a database query into instances.
InstanceQuery() - Constructor for class weka.experiment.InstanceQuery: Sets up the database drivers
instanceRangeTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns the tip text for this property
Instances - class weka.core.Instances.: Class for handling an ordered set of weighted instances.
Instances(Instances) - Constructor for class weka.core.Instances: Constructor copying all instances and references to the header information from the given set of instances.
Instances(Instances, int) - Constructor for class weka.core.Instances: Constructor creating an empty set of instances.
Instances(Instances, int, int) - Constructor for class weka.core.Instances: Creates a new set of instances by copying a subset of another set.
Instances(Reader) - Constructor for class weka.core.Instances: Reads an ARFF file from a reader, and assigns a weight of one to each instance.
Instances(Reader, int) - Constructor for class weka.core.Instances: Reads the header of an ARFF file from a reader and reserves space for the given number of instances.
Instances(String, FastVector, int) - Constructor for class weka.core.Instances: Creates an empty set of instances.
InstanceSavePanel - class weka.gui.streams.InstanceSavePanel.: A bean that saves a stream of instances to a file.
InstanceSavePanel() - Constructor for class weka.gui.streams.InstanceSavePanel
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.Splitter: Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Gets the subset of instances that apply to a particluar branch of the split.
instancesIndicesTipText() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns the tip text for this property
InstancesResultListener - class weka.experiment.InstancesResultListener.: InstancesResultListener outputs the received results in arff format to a Writer.
InstancesResultListener() - Constructor for class weka.experiment.InstancesResultListener
InstancesSummaryPanel - class weka.gui.InstancesSummaryPanel.: This panel just displays relation name, number of instances, and number of attributes.
InstancesSummaryPanel() - Constructor for class weka.gui.InstancesSummaryPanel: Creates the instances panel with no initial instances.
InstanceTable - class weka.gui.streams.InstanceTable.: A bean that takes a stream of instances and displays in a table.
InstanceTable() - Constructor for class weka.gui.streams.InstanceTable
InstanceViewer - class weka.gui.streams.InstanceViewer.: This is a very simple instance viewer - just displays the dataset as text output as it would be written to a file.
InstanceViewer() - Constructor for class weka.gui.streams.InstanceViewer
intCount - Variable in class weka.core.AttributeStats: The number of int-like values
INTEGER - Static variable in class weka.experiment.DatabaseUtils
intercept() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Return the intercept
IntVector - class weka.classifiers.functions.pace.IntVector.
IntVector() - Constructor for class weka.classifiers.functions.pace.IntVector: Constructs a null vector.
IntVector(int) - Constructor for class weka.classifiers.functions.pace.IntVector: Constructs an n-vector of zeros.
IntVector(int[]) - Constructor for class weka.classifiers.functions.pace.IntVector: Constructs a vector given an int array
IntVector(int, int) - Constructor for class weka.classifiers.functions.pace.IntVector: Constructs an n-vector of a constant
inverseIterator() - Method in class weka.associations.tertius.SimpleLinkedList
invertSelectionTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Remove: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Copy: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveRange: Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
invertTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
isAveragable() - Method in class weka.core.Attribute: Returns whether the attribute can be averaged meaningfully.
isClass() - Method in class weka.associations.tertius.Predicate
isConnected() - Method in class weka.experiment.DatabaseUtils: Returns true if a database connection is active.
isCover(Instance) - Method in class weka.classifiers.rules.ConjunctiveRule: Whether the instance covered by this rule
isDate() - Method in class weka.core.Attribute: Tests if the attribute is a date type.
isEmpty() - Method in class weka.associations.tertius.Rule: Test if this rule is empty.
isEmpty() - Method in class weka.associations.tertius.SimpleLinkedList
isEmpty() - Method in class weka.associations.tertius.LiteralSet: Test if this set is empty.
isEmpty() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Tests if this hashtable maps no keys to values.
isEmpty() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns true if it is empty.
isEmpty() - Method in class weka.classifiers.functions.pace.DoubleVector: Checks if it is an empty vector
isEmpty() - Method in class weka.classifiers.functions.pace.PaceMatrix: Check if the matrix is empty
isEmpty() - Method in class weka.classifiers.functions.pace.IntVector: Returns true if the vector is empty
isHierachic(String) - Method in class weka.gui.HierarchyPropertyParser: Whether the given string has a hierachy structure with the seperators
isIncludedIn(Rule) - Method in class weka.associations.tertius.Head: Test if this Head is included in a rule.
isIncludedIn(Rule) - Method in class weka.associations.tertius.LiteralSet: Test if this LiteralSet is included in a rule.
isIncludedIn(Rule) - Method in class weka.associations.tertius.Body: Test if this Body is included in a rule.
isInRange(double) - Method in class weka.core.Attribute: Determines whether a value lies within the bounds of the attribute.
isInRange(int) - Method in class weka.core.Range: Gets whether the supplied cardinal number is included in the current range.
isLeaf() - Method in class weka.classifiers.trees.m5.RuleNode: Return true if this node is a leaf
isLeafReached() - Method in class weka.gui.HierarchyPropertyParser: Whether the current position is a leaf
isMissing(Attribute) - Method in class weka.core.Instance: Tests if a specific value is "missing".
isMissing(int) - Method in class weka.core.Instance: Tests if a specific value is "missing".
isMissing(int) - Method in class weka.core.SparseInstance: Tests if a specific value is "missing".
isMissingSparse(int) - Method in class weka.core.Instance: Tests if a specific value is "missing".
isMissingValue(double) - Static method in class weka.core.Instance: Tests if the given value codes "missing".
isNominal() - Method in class weka.core.Attribute: Test if the attribute is nominal.
isNominal() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns true if selection attribute is nominal.
isNumeric() - Method in class weka.core.Attribute: Tests if the attribute is numeric.
isNumeric() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns true if selection attribute is numeric.
isOutputFormatDefined() - Method in class weka.filters.Filter: Returns whether the output format is ready to be collected
isOutputFormatDefined() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns whether the output format is ready to be collected
isPaintable() - Method in class weka.gui.FileEditor: Returns true since this editor is paintable.
isPaintable() - Method in class weka.gui.GenericObjectEditor: Returns true to indicate that we can paint a representation of the Object.
isPaintable() - Method in class weka.gui.GenericArrayEditor: Returns true to indicate that we can paint a representation of the string array
isPaintable() - Method in class weka.gui.CostMatrixEditor: Indicates whether the object can be represented graphically.
isRegular() - Method in class weka.core.Attribute: Returns whether the attribute values are equally spaced.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener: Always says a result is required.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.AveragingResultProducer: Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in interface weka.experiment.ResultListener: Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.CSVResultListener: Always says a result is required.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.LearningRateResultProducer: Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultProducer: Determines whether the results for a specified key must be generated.
isRootReached() - Method in class weka.gui.HierarchyPropertyParser: Whether the current position is the root
isSequentialAttIndexValid() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns whether or not the Sequential Attribute Index requires rebuilding due to a change
isSequentialInstanceIndexValid() - Method in class weka.classifiers.lazy.LBR.Indexes: Returns whether or not the Sequential Instance Index requires rebuilding due to a change
isStopword(String) - Static method in class weka.core.Stopwords: Returns true if the given string is a stop word.
isString() - Method in class weka.core.Attribute: Tests if the attribute is a string.
isSymmetric() - Method in class weka.core.Matrix: Returns true if the matrix is symmetric.
ItemSet - class weka.associations.ItemSet.: Class for storing a set of items.
ItemSet(int) - Constructor for class weka.associations.ItemSet: Constructor
itemStateChanged(ItemEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Performs the action associated with the ItemEvent.
IteratedSingleClassifierEnhancer - class weka.classifiers.IteratedSingleClassifierEnhancer.: Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
IteratedSingleClassifierEnhancer() - Constructor for class weka.classifiers.IteratedSingleClassifierEnhancer
IterativeClassifier - interface weka.classifiers.IterativeClassifier.: Interface for classifiers that can induce models of growing complexity one step at a time.
iterator() - Method in class weka.associations.tertius.SimpleLinkedList

J

J48 - class weka.classifiers.trees.J48.: Class for generating an unpruned or a pruned C4.5 decision tree.
J48() - Constructor for class weka.classifiers.trees.J48
joinOptions(String[]) - Static method in class weka.core.Utils: Joins all the options in an option array into a single string, as might be used on the command line.
JRip - class weka.classifiers.rules.JRip.: This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which is proposed by William W.
JRip() - Constructor for class weka.classifiers.rules.JRip

K

kappa() - Method in class weka.classifiers.Evaluation: Returns value of kappa statistic if class is nominal.
kappa() - Method in class evaluationMethods.EstimatorEvaluation: Returns value of kappa statistic if class is nominal.
KBInformation() - Method in class weka.classifiers.Evaluation: Return the total Kononenko & Bratko Information score in bits
KBInformation() - Method in class evaluationMethods.EstimatorEvaluation: Return the total Kononenko & Bratko Information score in bits
KBMeanInformation() - Method in class weka.classifiers.Evaluation: Return the Kononenko & Bratko Information score in bits per instance.
KBMeanInformation() - Method in class evaluationMethods.EstimatorEvaluation: Return the Kononenko & Bratko Information score in bits per instance.
KBRelativeInformation() - Method in class weka.classifiers.Evaluation: Return the Kononenko & Bratko Relative Information score
KBRelativeInformation() - Method in class evaluationMethods.EstimatorEvaluation: Return the Kononenko & Bratko Relative Information score
KDConditionalEstimator - class weka.estimators.KDConditionalEstimator.: Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate kernel estimators for each discrete conditioning value).
KDConditionalEstimator(int, double) - Constructor for class weka.estimators.KDConditionalEstimator: Constructor
KDDataGenerator - class weka.gui.boundaryvisualizer.KDDataGenerator.: KDDataGenerator.
KDDataGenerator() - Constructor for class weka.gui.boundaryvisualizer.KDDataGenerator
Kernel - class weka.classifiers.functions.supportVector.Kernel.: Abstract kernel.
Kernel() - Constructor for class weka.classifiers.functions.supportVector.Kernel
KernelEstimator - class weka.estimators.KernelEstimator.: Simple kernel density estimator.
KernelEstimator(double) - Constructor for class weka.estimators.KernelEstimator: Constructor that takes a precision argument.
key - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: attribute value
keyFieldNameTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
KKConditionalEstimator - class weka.estimators.KKConditionalEstimator.: Conditional probability estimator for a numeric domain conditional upon a numeric domain.
KKConditionalEstimator(double) - Constructor for class weka.estimators.KKConditionalEstimator: Constructor
KNNTipText() - Method in class weka.classifiers.lazy.LWL: Returns the tip text for this property
KNNTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property
KnowledgeFlow - class weka.gui.beans.KnowledgeFlow.: Main GUI class for the KnowledgeFlow
KnowledgeFlow() - Constructor for class weka.gui.beans.KnowledgeFlow: Creates a new KnowledgeFlow instance.
KStar - class weka.classifiers.lazy.KStar.: K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
KStar() - Constructor for class weka.classifiers.lazy.KStar
KStarCache - class weka.classifiers.lazy.kstar.KStarCache.: A class representing the caching system used to keep track of each attribute value and its corresponding scale factor or stop parameter.
KStarCache.CacheTable - class weka.classifiers.lazy.kstar.KStarCache.CacheTable.: A custom hashtable class to support the caching system.
KStarCache.CacheTable() - Constructor for class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Constructs a new hashtable with a default capacity and load factor.
KStarCache.CacheTable(int, float) - Constructor for class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Constructs a new hashtable with a default capacity and load factor.
KStarCache.TableEntry - class weka.classifiers.lazy.kstar.KStarCache.TableEntry.: Hashtable collision list.
KStarCache.TableEntry(int, double, double, double, KStarCache.TableEntry) - Constructor for class weka.classifiers.lazy.kstar.KStarCache.TableEntry: Constructor
KStarCache() - Constructor for class weka.classifiers.lazy.kstar.KStarCache
KStarConstants - interface weka.classifiers.lazy.kstar.KStarConstants.
KStarNominalAttribute - class weka.classifiers.lazy.kstar.KStarNominalAttribute.: A custom class which provides the environment for computing the transformation probability of a specified test instance nominal attribute to a specified train instance nominal attribute.
KStarNominalAttribute(Instance, Instance, int, Instances, int[][], KStarCache) - Constructor for class weka.classifiers.lazy.kstar.KStarNominalAttribute: Constructor
KStarNumericAttribute - class weka.classifiers.lazy.kstar.KStarNumericAttribute.: A custom class which provides the environment for computing the transformation probability of a specified test instance numeric attribute to a specified train instance numeric attribute.
KStarNumericAttribute(Instance, Instance, int, Instances, int[][], KStarCache) - Constructor for class weka.classifiers.lazy.kstar.KStarNumericAttribute: Constructor
KStarWrapper - class weka.classifiers.lazy.kstar.KStarWrapper.
KStarWrapper() - Constructor for class weka.classifiers.lazy.kstar.KStarWrapper
KValueTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property

L

laplaceProb(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns relative frequency of class over all bags with Laplace correction.
laplaceProb(int, int) - Method in class weka.classifiers.trees.j48.Distribution: Returns relative frequency of class for given bag.
lastElement() - Method in class weka.core.FastVector: Returns the last element of the vector.
lastInstance() - Method in class weka.core.Instances: Returns the last instance in the set.
launchNext(int, int) - Method in class weka.experiment.RemoteExperiment: Launch a sub experiment on a remote host
layoutCompleted(LayoutCompleteEvent) - Method in class weka.gui.graphvisualizer.GraphVisualizer: This method is an implementation for LayoutCompleteEventListener class.
layoutCompleted(LayoutCompleteEvent) - Method in interface weka.gui.graphvisualizer.LayoutCompleteEventListener
LayoutCompleteEvent - class weka.gui.graphvisualizer.LayoutCompleteEvent.: This is an event which is fired by a LayoutEngine once a LayoutEngine finishes laying out the graph, so that the Visualizer can repaint the screen to show the changes.
LayoutCompleteEvent(Object) - Constructor for class weka.gui.graphvisualizer.LayoutCompleteEvent
LayoutCompleteEventListener - interface weka.gui.graphvisualizer.LayoutCompleteEventListener.: This interface should be implemented by any class which needs to receive LayoutCompleteEvents from the LayoutEngine.
LayoutEngine - interface weka.gui.graphvisualizer.LayoutEngine.: This interface class has been added to facilitate the addition of other layout engines to this package.
layoutGraph() - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: This method does a complete layout of the graph which includes removing cycles, assigning levels to nodes, reducing edge crossings and laying out the vertices horizontally for better visibility.
layoutGraph() - Method in class weka.gui.graphvisualizer.GraphVisualizer: This method lays out the graph by calling the LayoutEngine's layoutGraph() method.
layoutGraph() - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method lays out the graph for better visualization
LBR - class weka.classifiers.lazy.LBR.: Lazy Bayesian Rules implement a lazy learning approach to lessening the attribute-independence assumption of naive Bayes.
LBR.Indexes - class weka.classifiers.lazy.LBR.Indexes.: Class for handling instances and the associated attributes.
LBR.Indexes(int, int, boolean, int) - Constructor for class weka.classifiers.lazy.LBR.Indexes: constructor
LBR.Indexes(LBR.Indexes) - Constructor for class weka.classifiers.lazy.LBR.Indexes: constructor
LBR() - Constructor for class weka.classifiers.lazy.LBR
LearningRateResultProducer - class weka.experiment.LearningRateResultProducer.: LearningRateResultProducer takes the results from a ResultProducer and submits the average to the result listener.
LearningRateResultProducer() - Constructor for class weka.experiment.LearningRateResultProducer
learningRateTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
leastExplainingColumn(PaceMatrix, IntVector, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the index of the column that has the smallest (squared) response, when the column is moved to become the (ks-1)-th column.
LeastMedSq - class weka.classifiers.functions.LeastMedSq.: Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
LeastMedSq() - Constructor for class weka.classifiers.functions.LeastMedSq
leaveOneOut(LBR.Indexes, int[][][], int[], boolean[]) - Method in class weka.classifiers.lazy.LBR: Leave-one-out strategy.
leftNode() - Method in class weka.classifiers.trees.m5.RuleNode: Get the left child of this node
leftSide(Instances) - Method in class weka.classifiers.trees.j48.C45Split: Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Prints left side of condition satisfied by instances.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.NoSplit: Does nothing because no condition has to be satisfied.
leftSide(Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Returns name of splitting attribute (left side of condition).
legend() - Method in class weka.classifiers.trees.ADTree: Returns the legend of the tree, describing how results are to be interpreted.
LegendPanel - class weka.gui.visualize.LegendPanel.: This panel displays legends for a list of plots.
LegendPanel() - Constructor for class weka.gui.visualize.LegendPanel: Constructor
leverageForRule(ItemSet, ItemSet, int, int) - Method in class weka.associations.ItemSet: Outputs the leverage for a rule.
liftForRule(ItemSet, ItemSet, int) - Method in class weka.associations.ItemSet: Outputs the lift for a rule.
likelihoodThresholdTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
LINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
LinearRegression - class weka.classifiers.functions.LinearRegression.: Class for using linear regression for prediction.
LinearRegression() - Constructor for class weka.classifiers.functions.LinearRegression
LinearUnit - class weka.classifiers.functions.neural.LinearUnit.: This can be used by the neuralnode to perform all it's computations (as a Linear unit).
LinearUnit() - Constructor for class weka.classifiers.functions.neural.LinearUnit
listOptions() - Method in interface weka.core.OptionHandler: Returns an enumeration of all the available options..
listOptions() - Method in class weka.clusterers.SimpleKMeans: Returns an enumeration describing the available options..
listOptions() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.clusterers.EM: Returns an enumeration describing the available options..
listOptions() - Method in class weka.clusterers.FarthestFirst: Returns an enumeration describing the available options..
listOptions() - Method in class weka.clusterers.Cobweb: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.BIRCHCluster: Returns an enumeration describing the available options.
listOptions() - Method in class weka.datagenerators.RDG1: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.Discretize: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.Resample: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Remove: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Copy: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Add: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Discretize: Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.Resample: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Randomize: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange: Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns an enumeration describing the available options.
listOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.AveragingResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.PairedTTester: Lists options understood by this object.
listOptions() - Method in class weka.experiment.InstanceQuery: Returns an enumeration describing the available options
listOptions() - Method in class weka.experiment.Experiment: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CSVResultListener: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.LearningRateResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.RegressionSplitEvaluator: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CrossValidationResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.RandomSplitResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DatabaseResultProducer: Returns an enumeration describing the available options..
listOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.CfsSubsetEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ForwardSelection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.SVMAttributeEval: Returns an enumeration describing all the available options
listOptions() - Method in class weka.attributeSelection.RankSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns an enumeration describing the available options
listOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.PrincipalComponents: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.BestFirst: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.OneRAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.GeneticSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.Ranker: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RaceSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RandomSearch: Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.Apriori: Returns an enumeration describing the available options.
listOptions() - Method in class weka.associations.Tertius: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.CheckClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.Classifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.BVDecompose: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.BVDecomposeSegCVSub: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.SingleClassifierEnhancer: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.MultipleClassifiersCombiner: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.RandomizableClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.LWL: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.KStar: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.IBk: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.Bagging: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.CVParameterSelection: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MetaCost: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MultiScheme: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.ThresholdSelector: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.FilteredClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MultiClassClassifier: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.AdditiveRegression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Stacking: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MultiBoostAB: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Decorate: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.AdaBoostM1: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.LogitBoost: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.misc.FLR: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.misc.VFI: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.BayesNetK2: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.BayesNet: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.AODE: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.bayes.NaiveBayes: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.JRip: Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.OneR: Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.rules.PART: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.DecisionTable: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.Ridor: Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.NNge: Returns an enumeration of all the available options..
listOptions() - Method in class weka.classifiers.trees.J48: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.ADTree: Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.trees.RandomForest: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.REPTree: Lists the command-line options for this classifier.
listOptions() - Method in class weka.classifiers.trees.M5P: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.trees.LMT: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.RandomTree: Lists the command-line options for this classifier.
listOptions() - Method in class weka.classifiers.trees.m5.M5Base: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.LinearRegression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SimpleLogistic: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.LeastMedSq: Returns an enumeration of all the available options..
listOptions() - Method in class weka.classifiers.functions.MultilayerPerceptron: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.VotedPerceptron: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SMO: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.Winnow: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.SMOreg: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.Logistic: Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.PaceRegression: Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.RBFNetwork: Returns an enumeration describing the available options
listOptions() - Method in class confidenceMachine.tcm.TCMBartsRMI: Returns an enumeration describing the available options
listOptions() - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Returns an enumeration describing the available options
listOptions() - Method in class coreComponents.SVMToArff: Returns an enumeration describing the available options
listOptions() - Method in class coreComponents.DataToArff: Returns an enumeration describing the available options
listOptions() - Method in class evaluationMethods.CreateROCCurve: Returns an enumeration describing the available options
listOptions() - Method in class evaluationMethods.CalculateLoss: Returns an enumeration describing the available options
listOptions() - Method in class evaluationMethods.CreateReliabilityCurve: Returns an enumeration describing the available options
listOptions() - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Returns an enumeration describing the available options
listOptions() - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Returns an enumeration describing the available options
listOptions() - Method in class probabilityMachine.VPMDistMetaLearner: Returns an enumeration describing the available options
listOptions() - Method in class probabilityMachine.vpm.VPMNaiveBayes: Returns an enumeration describing the available options
listOptions() - Method in class probabilityMachine.vpm.VPMBartsRMI2: Returns an enumeration describing the available options
listOptions() - Method in class probabilityMachine.vpm.VPMBartsRMI: Returns an enumeration describing the available options
listOptions() - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Returns an enumeration describing the available options
listOptions() - Method in class classifiers.PC_SMO: Returns an enumeration describing the available options.
listOptions() - Method in class classifiers.AlphaProb_SMO: Returns an enumeration describing the available options.
listOptions() - Method in class classifiers.AltDist_IBk: Returns an enumeration describing the available options.
listOptions() - Method in class classifiers.usm.distance.USMWavDistance: Returns an enumeration describing the available options.
listOptions() - Method in class classifiers.stbarts.BartsRMI: Returns an enumeration describing the available options..
ListSelectorDialog - class weka.gui.ListSelectorDialog.: A dialog to present the user with a list of items, that the user can make a selection from, or cancel the selection.
ListSelectorDialog(Frame, JList) - Constructor for class weka.gui.ListSelectorDialog: Create the list selection dialog.
Literal - class weka.associations.tertius.Literal.
Literal(Predicate, int, int) - Constructor for class weka.associations.tertius.Literal
LiteralSet - class weka.associations.tertius.LiteralSet.: Class representing a set of literals, being either the body or the head of a rule.
LiteralSet() - Constructor for class weka.associations.tertius.LiteralSet: Constructor for a set that does not store its counter-instances.
LiteralSet(Instances) - Constructor for class weka.associations.tertius.LiteralSet: Constructor initializing the set of counter-instances to all the instances.
LMT - class weka.classifiers.trees.LMT.: Class for "logistic model tree" classifier.
LMT() - Constructor for class weka.classifiers.trees.LMT: Creates an instance of LMT with standard options
LMTNode - class weka.classifiers.trees.lmt.LMTNode.: Class for logistic model tree structure.
LMTNode(ModelSelection, int, boolean, boolean, int) - Constructor for class weka.classifiers.trees.lmt.LMTNode: Constructor for logistic model tree node.
lnFactorial(double) - Static method in class weka.core.SpecialFunctions: Returns natural logarithm of factorial using gamma function.
lnFactorial(int) - Method in class weka.classifiers.bayes.NaiveBayesMultinomial: Fast computation of ln(n!) for non-negative ints negative ints are passed on to the general gamma-function based version in weka.core.SpecialFunctions if the current n value is higher than any previous one, the cache is extended and filled to cover it the common case is reduced to a simple array lookup
lnGamma(double) - Static method in class weka.core.Statistics: Returns natural logarithm of gamma function.
lnsrch(double[], double[], double[], double, boolean[], double[][], FastVector) - Method in class weka.core.Optimization: Find a new point x in the direction p from a point xold at which the value of the function has decreased sufficiently, the positive definiteness of B matrix (approximation of the inverse of the Hessian) is preserved and no bound constraints are violated.
load(InputStream) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
Loader - class weka.gui.beans.Loader.: Loads data sets using weka.core.converter classes
Loader - interface weka.core.converters.Loader.: Interface to something that can load Instances from an input source in some format.
Loader() - Constructor for class weka.gui.beans.Loader
LoaderBeanInfo - class weka.gui.beans.LoaderBeanInfo.: Bean info class for the loader bean
LoaderBeanInfo() - Constructor for class weka.gui.beans.LoaderBeanInfo
LoaderCustomizer - class weka.gui.beans.LoaderCustomizer.: GUI Customizer for the loader bean
LoaderCustomizer() - Constructor for class weka.gui.beans.LoaderCustomizer
loadIcons(String, String) - Method in class weka.gui.beans.BeanVisual: Loads static and animated versions of a beans icons.
localDistributionForInstance(Instance, LBR.Indexes) - Method in class weka.classifiers.lazy.LBR: Calculates the class membership probabilities.
locallyPredictiveTipText() - Method in class weka.attributeSelection.CfsSubsetEval: Returns the tip text for this property
localNaiveBayes(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR: Class for building and using a simple Naive Bayes classifier.
locateIndex(int) - Method in class weka.core.SparseInstance: Locates the greatest index that is not greater than the given index.
log2 - Static variable in class weka.core.Utils: The natural logarithm of 2.
LOG2 - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
log2(double) - Static method in class weka.core.Utils: Returns the logarithm of a for base 2.
log2Binomial(double, double) - Static method in class weka.core.SpecialFunctions: Returns base 2 logarithm of binomial coefficient using gamma function.
log2Multinomial(double, double[]) - Static method in class weka.core.SpecialFunctions: Returns base 2 logarithm of multinomial using gamma function.
log2MultipleHypergeometric(double[][]) - Static method in class weka.core.ContingencyTables: Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.
logDensityForInstance(Instance) - Method in class weka.clusterers.DensityBasedClusterer: Computes the density for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.DensityBasedClusterer: Computes the log of the conditional density (per cluster) for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.MakeDensityBasedClusterer: Computes the log of the conditional density (per cluster) for a given instance.
logDensityPerClusterForInstance(Instance) - Method in class weka.clusterers.EM: Computes the log of the conditional density (per cluster) for a given instance.
logFunc(double) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit: Help method for computing entropy.
Logger - interface weka.gui.Logger.: Interface for objects that display log (permanent historical) and status (transient) messages.
Logistic - class weka.classifiers.functions.Logistic.: Second implementation for building and using a multinomial logistic regression model with a ridge estimator.
Logistic() - Constructor for class weka.classifiers.functions.Logistic
LogisticBase - class weka.classifiers.trees.lmt.LogisticBase.: Base/helper class for building logistic regression models with the LogitBoost algorithm.
LogisticBase() - Constructor for class weka.classifiers.trees.lmt.LogisticBase: Constructor that creates LogisticBase object with standard options.
LogisticBase(int, boolean, boolean) - Constructor for class weka.classifiers.trees.lmt.LogisticBase: Constructor to create LogisticBase object.
LogitBoost - class weka.classifiers.meta.LogitBoost.: Class for performing additive logistic regression..
LogitBoost() - Constructor for class weka.classifiers.meta.LogitBoost: Constructor.
logMessage(String) - Method in class weka.gui.LogPanel: Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.SysErrLog: Sends the supplied message to the log area.
logMessage(String) - Method in interface weka.gui.Logger: Sends the supplied message to the log area.
LogPanel - class weka.gui.LogPanel.: This panel allows log and status messages to be posted.
LogPanel() - Constructor for class weka.gui.LogPanel: Creates the log panel with no task monitor and the log always visible.
LogPanel(WekaTaskMonitor) - Constructor for class weka.gui.LogPanel: Creates the log panel with a task monitor, where the log is hidden.
LogPanel(WekaTaskMonitor, boolean) - Constructor for class weka.gui.LogPanel: Creates the log panel, possibly with task monitor, where the log is optionally hidden.
logPSI - Static variable in class weka.classifiers.functions.pace.Maths: The constant - log( sqrt(2 pi) )
logs2probs(double[]) - Static method in class weka.core.Utils: Converts an array containing the natural logarithms of probabilities stored in a vector back into probabilities.
logScore(int) - Method in class weka.classifiers.bayes.BayesNet: logScore returns the log of the quality of a network (e.g.
logScore(int) - Method in interface weka.classifiers.bayes.Scoreable: Returns log-score
logScore(int) - Method in class weka.classifiers.bayes.DiscreteEstimatorBayes: Gets the log score contribution of this distribution
LONG - Static variable in class weka.experiment.DatabaseUtils
lookupCacheSizeTipText() - Method in class weka.attributeSelection.BestFirst: Returns the tip text for this property
lowerBoundMinSupportTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
lowerCaseTokensTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
lowerNumericBoundIsOpen() - Method in class weka.core.Attribute: Returns whether the lower numeric bound of the attribute is open.
lowerOrderTermsTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
lowerOrderTermsTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
lowerOrderTermsTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
lowerOrderTermsTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
lsqr(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: QR transformation for a least squares problem A x = b
lsqrSelection(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: QR transformation for a least squares problem A x = b
LUDecomposition() - Method in class weka.core.Matrix: Performs a LUDecomposition on the matrix.
LWL - class weka.classifiers.lazy.LWL.: Locally-weighted learning.
LWL() - Constructor for class weka.classifiers.lazy.LWL: Constructor.

M

m_ADNodes - Variable in class weka.classifiers.bayes.VaryNode: list of ADNode children
m_alpha - Variable in class weka.classifiers.trees.lmt.LMTNode: Alpha-value (for pruning) at the node
m_alpha - Variable in class classifiers.AlphaProb_SMO.BinarySMO: The Lagrange multipliers.
m_AttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes: the array attribute indexes
M_AVERAGE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
m_AvPredInPattern - Variable in class coreComponents.PatternCounter.PatternObject
m_ClassIndex - Variable in class weka.classifiers.lazy.LBR.Indexes: the Class Index for the data set
m_col - Variable in class weka.gui.treevisualizer.NamedColor: The actual color object
m_cols - Variable in class weka.gui.treevisualizer.Colors: The array with all the colors input
m_CombinedCompressData - Variable in class classifiers.usm.distance.USMWavDistance: Combined compressed wav information data set
m_CombinedCompressFileName - Variable in class classifiers.usm.distance.USMWavDistance: File name and directory of the combined wav information data set
m_customColour - Variable in class weka.gui.visualize.PlotData2D
M_DELETE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants: Missing value handling mode
m_displayAllPoints - Variable in class weka.gui.visualize.PlotData2D: Display all points (ie.
m_DistanceTrain - Variable in class classifiers.vdm.ValueDifferenceMetric: The training set used to generate the VD Matrices
m_ErraticBernoulliProb - Variable in class evaluationMethods.OnlineEvaluation: Defines the probability of introducing a training example in the online setting
m_ErraticRandomSeed - Variable in class evaluationMethods.OnlineEvaluation: Defines the random seed used to generate the erratic experience plan
m_errors - Variable in class classifiers.AlphaProb_SMO.BinarySMO: The current set of errors for all non-bound examples.
m_experimentFinished - Variable in class weka.experiment.RemoteExperimentEvent: True if a remote experiment has finished
m_indexVal - Variable in class weka.gui.visualize.AttributePanelEvent: The index for the new attribute
m_iNode - Variable in class weka.classifiers.bayes.VaryNode: index of the node varied
m_Instances - Variable in class weka.classifiers.bayes.BayesNet: The dataset header for the purposes of printing out a semi-intelligible model
m_Instances - Variable in class weka.classifiers.bayes.ADNode: list of Instance children (either m_Instances or m_VaryNodes is instantiated)
m_InstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes: the array instance indexes
m_K - Variable in class classifiers.vdm.ValueDifferenceMetric: The type of Minkowski type distance to use (default K=1 Manhatten)
m_logMessage - Variable in class weka.experiment.RemoteExperimentEvent: A log type message
M_MAXDIFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
m_messageString - Variable in class weka.experiment.RemoteExperimentEvent: The message
m_Model - Variable in class coreComponents.EuclideanDistanceMetric
m_name - Variable in class weka.gui.treevisualizer.NamedColor: The name of the color
m_nCount - Variable in class weka.classifiers.bayes.ADNode: count
m_nMCV - Variable in class weka.classifiers.bayes.VaryNode: most common value
M_NORMAL - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
m_nStartNode - Variable in class weka.classifiers.bayes.ADNode: first node in VaryNode array
m_NumAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes: the number of attributes "in use" or set to a the original value (true or false)
m_numIncorrectModel - Variable in class weka.classifiers.trees.lmt.LMTNode: Weighted number of training examples currently misclassified by the logistic model at the node
m_numIncorrectTree - Variable in class weka.classifiers.trees.lmt.LMTNode: Weighted number of training examples currently misclassified by the subtree rooted at the node
m_NumInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes: the number of instances "in use" or set to a the original value (true or false)
m_numParameters - Variable in class weka.classifiers.trees.m5.RuleNode: the number of paramters in the chosen model for this node---either the subtree model or the linear model.
m_NumSeqAttsSet - Variable in class weka.classifiers.lazy.LBR.Indexes: the number of sequential attributes "in use" or set to a the original value (true or false)
m_NumSeqInstsSet - Variable in class weka.classifiers.lazy.LBR.Indexes: the number of sequential instances "in use" or set to a the original value (true or false)
m_NumWithPattern - Variable in class coreComponents.PatternCounter.PatternObject
m_NumWithPatternPerClass - Variable in class coreComponents.PatternCounter.PatternObject
m_Pattern - Variable in class coreComponents.PatternCounter.PatternObject
m_PatternCounter - Variable in class coreComponents.PatternCounter
m_SequentialAttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes: an array of attribute indexes that are set to either true or false
m_SequentialInstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes: the array of instance indexes that are set to a either true or false
m_SingleCompressData - Variable in class classifiers.usm.distance.USMWavDistance: Single compressed wav information data set
m_SingleCompressFileName - Variable in class classifiers.usm.distance.USMWavDistance: File name and directory of the single wav information data set
m_SlowLazyFixedGap - Variable in class evaluationMethods.OnlineEvaluation: Defines the fixed gap size for both the slow and lazy settings
m_SlowLazyGrowGapBase - Variable in class evaluationMethods.OnlineEvaluation: Defines the base in the Arithmetic/Geometric progression growing gap
m_SlowLazyGrowGapPower - Variable in class evaluationMethods.OnlineEvaluation: Defines the base in the Arithmetic/Geometric progression growing gap
m_statusMessage - Variable in class weka.experiment.RemoteExperimentEvent: A status type message
m_TentativeDistanceMetric - Variable in class classifiers.vdm.ValueDifferenceMetric
m_TotalPredInPattern - Variable in class coreComponents.PatternCounter.PatternObject
m_useCustomColour - Variable in class weka.gui.visualize.PlotData2D: Custom colour for this plot
m_VaryNodes - Variable in class weka.classifiers.bayes.ADNode: list of VaryNode children
m_VDMatrix - Variable in class classifiers.vdm.ValueDifferenceMetric: The VD Matrices for each attribute
m_xChange - Variable in class weka.gui.visualize.AttributePanelEvent: True if the x selection changed
m_yChange - Variable in class weka.gui.visualize.AttributePanelEvent: True if the y selection changed
M5Base - class weka.classifiers.trees.m5.M5Base.: M5Base.
M5Base() - Constructor for class weka.classifiers.trees.m5.M5Base: Constructor
M5P - class weka.classifiers.trees.M5P.: M5P.
M5P() - Constructor for class weka.classifiers.trees.M5P: Creates a new M5P instance.
M5Rules - class weka.classifiers.rules.M5Rules.: Generates a decision list for regression problems using separate-and-conquer.
M5Rules() - Constructor for class weka.classifiers.rules.M5Rules
MahalanobisEstimator - class weka.estimators.MahalanobisEstimator.: Simple probability estimator that places a single normal distribution over the observed values.
MahalanobisEstimator(Matrix, double, double) - Constructor for class weka.estimators.MahalanobisEstimator: Constructor
mahanobolisDistanceFrom(double, double, double) - Method in class probabilityMachine.VPMDistMetaLearner: Finds the number of standard deviations from the mean of a Gaussian
mahanobolisDistanceFrom(double, double, double) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Finds the number of standard deviations from the mean of a Gaussian
mahanobolisDistanceFrom(double, double, double) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Finds the number of standard deviations from the mean of a Gaussian
main(String[]) - Static method in class weka.gui.DatabaseConnectionDialog
main(String[]) - Static method in class weka.gui.SelectedTagEditor: Tests out the selectedtag editor from the command line.
main(String[]) - Static method in class weka.gui.GUIChooser: Tests out the GUIChooser environment.
main(String[]) - Static method in class weka.gui.SaveBuffer: Main method for testing this class
main(String[]) - Static method in class weka.gui.AttributeListPanel: Tests the attribute list panel from the command line.
main(String[]) - Static method in class weka.gui.SimpleCLI: Method to start up the simple cli
main(String[]) - Static method in class weka.gui.ListSelectorDialog: Tests out the list selector from the command line.
main(String[]) - Static method in class weka.gui.PropertySelectorDialog: Tests out the property selector from the command line.
main(String[]) - Static method in class weka.gui.GenericObjectEditor: Tests out the Object editor from the command line.
main(String[]) - Static method in class weka.gui.LogPanel: Tests out the log panel from the command line.
main(String[]) - Static method in class weka.gui.InstancesSummaryPanel: Tests out the instance summary panel from the command line.
main(String[]) - Static method in class weka.gui.WekaTaskMonitor: Main method for testing this class
main(String[]) - Static method in class weka.gui.AttributeVisualizationPanel: Main method to test this class from command line
main(String[]) - Static method in class weka.gui.GenericArrayEditor: Tests out the array editor from the command line.
main(String[]) - Static method in class weka.gui.HierarchyPropertyParser: Tests out the parser.
main(String[]) - Static method in class weka.gui.AttributeSummaryPanel: Tests out the attribute summary panel from the command line.
main(String[]) - Static method in class weka.gui.ResultHistoryPanel: Tests out the result history from the command line.
main(String[]) - Static method in class weka.gui.AttributeSelectionPanel: Tests the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClassifierPanel: Tests out the classifier panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.AssociationsPanel: Tests out the Associator panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClustererPanel: Tests out the clusterer panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.Explorer: Tests out the explorer environment.
main(String[]) - Static method in class weka.gui.explorer.PreprocessPanel: Tests out the instance-preprocessing panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.AttributeSelectionPanel: Tests out the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.treevisualizer.TreeVisualizer: Main method for testing this class.
main(String[]) - Static method in class weka.gui.beans.AttributeSummarizer
main(String[]) - Static method in class weka.gui.beans.StripChart: Tests out the StripChart from the command line
main(String[]) - Static method in class weka.gui.beans.KnowledgeFlow: Main method.
main(String[]) - Static method in class weka.gui.beans.Loader
main(String[]) - Static method in class weka.gui.beans.ScatterPlotMatrix
main(String[]) - Static method in class weka.gui.beans.TextViewer
main(String[]) - Static method in class weka.gui.beans.DataVisualizer
main(String[]) - Static method in class weka.gui.experiment.Experimenter: Tests out the experiment environment.
main(String[]) - Static method in class weka.gui.experiment.HostListPanel: Tests out the host list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DatasetListPanel: Tests out the dataset list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DistributeExperimentPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunNumberPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.ResultsPanel: Tests out the results panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.SetupPanel: Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.experiment.SimpleSetupPanel: Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.experiment.AlgorithmListPanel: Tests out the algorithm list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunPanel: Tests out the run panel from the command line.
main(String[]) - Static method in class weka.gui.graphvisualizer.GraphVisualizer: Main method to load a text file with the description of a graph from the command line
main(String[]) - Static method in class weka.gui.visualize.LegendPanel: Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.ClassPanel: Main method for testing this class.
main(String[]) - Static method in class weka.gui.visualize.VisualizePanel: Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.MatrixPanel: Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.Plot2D: Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.AttributePanel: Main method for testing this class.
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Main method for testing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanel: Main method for testing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Main method for testing this class
main(String[]) - Static method in class weka.core.Instance: Main method for testing this class.
main(String[]) - Static method in class weka.core.BinarySparseInstance: Main method for testing this class.
main(String[]) - Static method in class weka.core.Utils: Main method for testing this class.
main(String[]) - Static method in class weka.core.SpecialFunctions: Main method for testing this class.
main(String[]) - Static method in class weka.core.Attribute: Simple main method for testing this class.
main(String[]) - Static method in class weka.core.SparseInstance: Main method for testing this class.
main(String[]) - Static method in class weka.core.Instances: Main method for this class -- just prints a summary of a set of instances.
main(String[]) - Static method in class weka.core.RandomVariates: Main method for testing this class.
main(String[]) - Static method in class weka.core.Range: Main method for testing this class.
main(String[]) - Static method in class weka.core.Matrix: Main method for testing this class.
main(String[]) - Static method in class weka.core.Statistics: Main method for testing this class.
main(String[]) - Static method in class weka.core.CheckOptionHandler: Main method for using the CheckOptionHandler.
main(String[]) - Static method in class weka.core.Queue: Main method for testing this class.
main(String[]) - Static method in class weka.core.SingleIndex: Main method for testing this class.
main(String[]) - Static method in class weka.core.ContingencyTables: Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.CSVLoader: Main method.
main(String[]) - Static method in class weka.core.converters.C45Loader: Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.SerializedInstancesLoader: Main method.
main(String[]) - Static method in class weka.core.converters.ArffLoader: Main method.
main(String[]) - Static method in class weka.clusterers.SimpleKMeans: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.MakeDensityBasedClusterer: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.EM: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.ClusterEvaluation: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.FarthestFirst: Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.Cobweb
main(String[]) - Static method in class weka.datagenerators.BIRCHCluster: Main method for testing this class.
main(String[]) - Static method in class weka.datagenerators.RDG1: Main method for testing this class.
main(String[]) - Static method in class weka.filters.NullFilter: Main method for testing this class.
main(String[]) - Static method in class weka.filters.Filter: Main method for testing this class.
main(String[]) - Static method in class weka.filters.AllFilter: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.AttributeSelection: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.NominalToBinary: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.ClassOrder: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.Discretize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.Resample: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.SpreadSubsample: Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Obfuscate: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveType: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToNominal: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PKIDiscretize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveUseless: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddCluster: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericToBinary: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToWordVector: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Normalize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesDelta: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddExpression: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddNoise: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ClusterMembership: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MergeTwoValues: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NominalToBinary: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SwapValues: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Remove: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Standardize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Copy: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.FirstOrder: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.NumericTransform: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Add: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RandomProjection: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Discretize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MakeIndicator: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFolds: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemovePercentage: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.SparseToNonSparse: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Resample: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Randomize: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveMisclassified: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveRange: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.NonSparseToSparse: Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveWithValues: Main method for testing this class.
main(String[]) - Static method in class weka.experiment.RemoteEngine: Main method.
main(String[]) - Static method in class weka.experiment.OutputZipper: Main method for testing this class
main(String[]) - Static method in class weka.experiment.PairedTTester: Test the class from the command line.
main(String[]) - Static method in class weka.experiment.InstanceQuery: Test the class from the command line.
main(String[]) - Static method in class weka.experiment.Experiment: Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.Stats: Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.PairedCorrectedTTester: Test the class from the command line.
main(String[]) - Static method in class weka.experiment.PairedStats: Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.CrossValidationResultProducer
main(String[]) - Static method in class weka.experiment.RemoteExperiment: Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.estimators.DiscreteEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KKConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NNConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KDConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DKConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DDConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.PoissonEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.MahalanobisEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.KernelEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NDConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NormalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DNConditionalEstimator: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.InfoGainAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.CfsSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ReliefFAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SVMAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ChiSquaredAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.AttributeSelection: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.GainRatioAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ConsistencySubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.PrincipalComponents: Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.OneRAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.WrapperSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ClassifierSubsetEval: Main method for testing this class.
main(String[]) - Static method in class weka.associations.Apriori: Main method for testing this class.
main(String[]) - Static method in class weka.associations.Tertius: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.CheckClassifier: Test method for this class
main(String[]) - Static method in class weka.classifiers.BVDecompose: Test method for this class
main(String[]) - Static method in class weka.classifiers.BVDecomposeSegCVSub: Test method for this class
main(String[]) - Static method in class weka.classifiers.Evaluation: A test method for this class.
main(String[]) - Static method in class weka.classifiers.lazy.LBR: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LWL: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.IB1: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.KStar: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.IBk: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Main method for this class.
main(String[]) - Static method in class weka.classifiers.meta.Bagging: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CVParameterSelection: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MetaCost: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiScheme: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Grading: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ClassificationViaRegression: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ThresholdSelector: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.FilteredClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiClassClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdditiveRegression: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Vote: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Stacking: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiBoostAB: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AttributeSelectedClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Decorate: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdaBoostM1: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RandomCommittee: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.StackingC: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.LogitBoost: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CostSensitiveClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.RegressionByDiscretization: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.OrdinalClassClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.HyperPipes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.FLR: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.VFI: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNetK2: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNet: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNetB: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.AODE: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesSimple: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.BayesNetB2: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesMultinomial: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesUpdateable: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.DiscreteEstimatorBayes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.ComplementNaiveBayes: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.ZeroR: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.JRip: Main method.
main(String[]) - Static method in class weka.classifiers.rules.ConjunctiveRule: Main method.
main(String[]) - Static method in class weka.classifiers.rules.M5Rules: Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.rules.OneR: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.rules.Prism: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.rules.PART: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.DecisionTable: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.Ridor: Main method.
main(String[]) - Static method in class weka.classifiers.rules.NNge: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.J48: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.ADTree: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.RandomForest: Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.DecisionStump: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.UserClassifier: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.Id3: Main method.
main(String[]) - Static method in class weka.classifiers.trees.REPTree: Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.M5P: Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.trees.LMT: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.RandomTree: Main method for this class.
main(String[]) - Static method in class weka.classifiers.evaluation.ThresholdCurve: Tests the ThresholdCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.MarginCurve: Tests the MarginCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.CostCurve: Tests the CostCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.functions.LinearRegression: Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.SimpleLogistic: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.SimpleLinearRegression: Main method for testing this class
main(String[]) - Static method in class weka.classifiers.functions.LeastMedSq: generate a Linear regression predictor for testing
main(String[]) - Static method in class weka.classifiers.functions.MultilayerPerceptron: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.VotedPerceptron: Main method.
main(String[]) - Static method in class weka.classifiers.functions.SMO: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.Winnow: Main method.
main(String[]) - Static method in class weka.classifiers.functions.SMOreg: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.Logistic: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.PaceRegression: Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.RBFNetwork: Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.pace.DiscreteFunction
main(String[]) - Static method in class weka.classifiers.functions.pace.DoubleVector
main(String[]) - Static method in class weka.classifiers.functions.pace.PaceMatrix
main(String[]) - Static method in class weka.classifiers.functions.pace.ChisqMixture: Method to test this class
main(String[]) - Static method in class weka.classifiers.functions.pace.IntVector: Tests the IntVector class
main(String[]) - Static method in class weka.classifiers.functions.pace.NormalMixture: Method to test this class
main(String[]) - Static method in class confidenceMachine.tcm.TCMBartsRMI: Main method for testing this class.
main(String[]) - Static method in class confidenceMachine.tcm.TCMKNearestNeighbours: Main method for testing this class.
main(String[]) - Static method in class coreComponents.SVMToArff: Testing area for this object
main(String[]) - Static method in class coreComponents.EuclideanDistanceMetric: Main method for testing this class.
main(String[]) - Static method in class coreComponents.DataToArff: Testing area for this object
main(String[]) - Static method in class coreComponents.ArffCreator: Testing area for this object
main(String[]) - Static method in class evaluationMethods.CreateROCCurve: Testing area for this object
main(String[]) - Static method in class evaluationMethods.CalculateLoss: Testing area for this object
main(String[]) - Static method in class evaluationMethods.CreateReliabilityCurve: Testing area for this object
main(String[]) - Static method in class evaluationMethods.CrossValidEvaluation
main(String[]) - Static method in class evaluationMethods.EstimatorEvaluation: A test method for this class.
main(String[]) - Static method in class evaluationMethods.OnlineEvaluation: A test method for this class.
main(String[]) - Static method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Main method for testing this class.
main(String[]) - Static method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Main method for testing this class.
main(String[]) - Static method in class probabilityMachine.VPMDistMetaLearner: Main method for testing this class.
main(String[]) - Static method in class probabilityMachine.vpm.VPMNaiveBayes: Main method for testing this class.
main(String[]) - Static method in class probabilityMachine.vpm.VPMBartsRMI2: Main method for testing this class.
main(String[]) - Static method in class probabilityMachine.vpm.VPMBartsRMI: Main method for testing this class.
main(String[]) - Static method in class probabilityMachine.vpm.VPMKNearestNeighbours: Main method for testing this class.
main(String[]) - Static method in class classifiers.PC_SMO: Main method for testing this class.
main(String[]) - Static method in class classifiers.AlphaProb_SMO: Main method for testing this class.
main(String[]) - Static method in class classifiers.AltDist_IBk: Main method for testing this class.
main(String[]) - Static method in class classifiers.vdm.ValueDifferenceMetric
main(String[]) - Static method in class classifiers.usm.distance.USMStringDistance: Test class for the object!
main(String[]) - Static method in class classifiers.usm.distance.USMWavDistance: Test class for the object!
main(String[]) - Static method in class classifiers.stbarts.BartsRMI: Main method for testing this class.
majorityClassTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
MakeADTree(Instances) - Static method in class weka.classifiers.bayes.ADNode: create AD tree from set of instances
MakeADTree(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.ADNode: create sub tree
makeBinaryTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
makeBinaryTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
makeCopies(ASEvaluation, int) - Static method in class weka.attributeSelection.ASEvaluation: Creates copies of the current evaluator.
makeCopies(Associator, int) - Static method in class weka.associations.Associator: Creates copies of the current associator.
makeCopies(Classifier, int) - Static method in class weka.classifiers.Classifier: Creates copies of the current classifier, which can then be used for boosting etc.
makeCopies(Clusterer, int) - Static method in class weka.clusterers.Clusterer: Creates copies of the current clusterer.
makeData(ClusterGenerator, String[]) - Static method in class weka.datagenerators.ClusterGenerator: Calls the data generator.
makeData(Generator, String[]) - Static method in class weka.datagenerators.Generator: Calls the data generator.
MakeDecList - class weka.classifiers.rules.part.MakeDecList.: Class for handling a decision list.
MakeDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.MakeDecList: Constructor for dec list pruned using C4.5 pruning.
MakeDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.MakeDecList: Constructor for unpruned dec list.
MakeDecList(ModelSelection, int, int, int) - Constructor for class weka.classifiers.rules.part.MakeDecList: Constructor for dec list pruned using hold-out pruning.
MakeDensityBasedClusterer - class weka.clusterers.MakeDensityBasedClusterer.: Class for wrapping a Clusterer to make it return a distribution and density.
MakeDensityBasedClusterer() - Constructor for class weka.clusterers.MakeDensityBasedClusterer: Default constructor.
MakeDensityBasedClusterer(Clusterer) - Constructor for class weka.clusterers.MakeDensityBasedClusterer: Contructs a MakeDensityBasedClusterer wrapping a given Clusterer.
makeDistribution(double, int) - Static method in class weka.classifiers.evaluation.NominalPrediction: Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0.
MakeIndicator - class weka.filters.unsupervised.attribute.MakeIndicator.: Creates a new dataset with a boolean attribute replacing a nominal attribute.
MakeIndicator() - Constructor for class weka.filters.unsupervised.attribute.MakeIndicator
makeUniformDistribution(int) - Static method in class weka.classifiers.evaluation.NominalPrediction: Creates a uniform probability distribution -- where each of the possible classes is assigned equal probability.
MakeVaryNode(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.ADNode: create sub tree
makeWeighted(CostMatrix) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Makes a copy of this ConfusionMatrix after applying the supplied CostMatrix to the cells.
map(String, String) - Method in class weka.classifiers.functions.pace.DoubleVector: Applies a method to the vector
margin() - Method in class weka.classifiers.evaluation.NominalPrediction: Calculates the prediction margin.
MarginCurve - class weka.classifiers.evaluation.MarginCurve.: Generates points illustrating the prediction margin.
MarginCurve() - Constructor for class weka.classifiers.evaluation.MarginCurve
Matchable - interface weka.core.Matchable.: Interface to something that can be matched with tree matching algorithms.
matchMissingValuesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
Maths - class weka.classifiers.functions.pace.Maths.: Class for some utility mathematical or statistical functions.
Maths() - Constructor for class weka.classifiers.functions.pace.Maths
MatlabUtils - class coreComponents.MatlabUtils.: A very messy class used to store functions that are useful producing/working with Matlab.
MatlabUtils() - Constructor for class coreComponents.MatlabUtils
Matrix - class weka.core.Matrix.: Class for performing operations on a matrix of floating-point values.
Matrix - class weka.classifiers.functions.pace.Matrix.: Jama = Java Matrix class.
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.MetaCost
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.MetaCost
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
matrix() - Method in class weka.classifiers.trees.j48.Distribution: Returns matrix with distribution of class values.
Matrix(double[][]) - Constructor for class weka.core.Matrix: Constructs a matrix using a given array.
Matrix(double[][]) - Constructor for class weka.classifiers.functions.pace.Matrix: Construct a matrix from a 2-D array.
Matrix(double[][], int, int) - Constructor for class weka.classifiers.functions.pace.Matrix: Construct a matrix quickly without checking arguments.
Matrix(double[], int) - Constructor for class weka.classifiers.functions.pace.Matrix: Construct a matrix from a one-dimensional packed array
Matrix(int, int) - Constructor for class weka.core.Matrix: Constructs a matrix and initializes it with default values.
Matrix(int, int) - Constructor for class weka.classifiers.functions.pace.Matrix: Construct an m-by-n matrix of zeros.
Matrix(int, int, double) - Constructor for class weka.classifiers.functions.pace.Matrix: Construct an m-by-n constant matrix.
Matrix(Reader) - Constructor for class weka.core.Matrix: Reads a matrix from a reader.
MatrixPanel - class weka.gui.visualize.MatrixPanel.: This panel displays a plot matrix of the user selected attributes of a given data set.
MatrixPanel() - Constructor for class weka.gui.visualize.MatrixPanel: Constructor
max - Variable in class weka.experiment.Stats: The maximum value seen, or Double.NaN if no values seen
MAX_SHAPES - Static variable in class weka.gui.visualize.Plot2D
max() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the maximum value of all elements
maxAbs() - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the maximum absolute value of all elements
maxAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the maximum absolute value of some elements of a column, that is, the elements of A[i0:i1][j].
maxBag() - Method in class weka.classifiers.trees.j48.Distribution: Returns index of bag containing maximum number of instances.
maxBoostingIterationsTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
maxChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
maxClass() - Method in class weka.classifiers.trees.j48.Distribution: Returns class with highest frequency over all bags.
maxClass(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns class with highest frequency for given bag.
maxCountTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns the tip text for this property
maxDepthTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
maxGenerationsTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
maxImpurity() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the impurity of this split
maxImpurity() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Returns the impurity of this split
maxImpurity() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the impurity of this split
maximumVariancePercentageAllowedTipText() - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Returns the tip text for this property
maxIndex(double[]) - Static method in class weka.core.Utils: Returns index of maximum element in a given array of doubles.
maxIndex(int[]) - Static method in class weka.core.Utils: Returns index of maximum element in a given array of integers.
maxIterationsTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
maxIterationsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
maxItsTipText() - Method in class weka.classifiers.functions.Logistic: Returns the tip text for this property
maxItsTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
maxKTipText() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the tip text for this property
maxModelsTipText() - Method in class weka.classifiers.meta.AdditiveRegression: Returns the tip text for this property
maxNrOfParentsTipText() - Method in class weka.classifiers.bayes.BayesNet
MaxParentSetSize(int) - Method in class weka.classifiers.bayes.ParentSet: reserve memory for parent set
maxStaleTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
MDL - Static variable in interface weka.classifiers.bayes.Scoreable
mean - Variable in class weka.experiment.Stats: The mean of values at the last calculateDerived() call
mean(double[]) - Static method in class weka.core.Utils: Computes the mean for an array of doubles.
meanAbsoluteError() - Method in class weka.classifiers.Evaluation: Returns the mean absolute error.
meanAbsoluteError() - Method in class evaluationMethods.EstimatorEvaluation: Returns the mean absolute error.
meanOrMode(Attribute) - Method in class weka.core.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(int) - Method in class weka.core.Instances: Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanPriorAbsoluteError() - Method in class weka.classifiers.Evaluation: Returns the mean absolute error of the prior.
meanPriorAbsoluteError() - Method in class evaluationMethods.EstimatorEvaluation: Returns the mean absolute error of the prior.
meanSquaredTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property
measureAttributesUsed() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
measureExamplesProcessed() - Method in class weka.classifiers.trees.ADTree: Returns the number of examples "counted".
measureNodesExpanded() - Method in class weka.classifiers.trees.ADTree: Returns the number of nodes expanded.
measureNumAttributesSelected() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Additional measure --- number of attributes selected
measureNumIterations() - Method in class weka.classifiers.meta.AdditiveRegression: return the number of iterations (base classifiers) completed
measureNumLeaves() - Method in class weka.classifiers.trees.J48: Returns the number of leaves
measureNumLeaves() - Method in class weka.classifiers.trees.ADTree: Calls measure function for leaf size - the number of prediction nodes.
measureNumLeaves() - Method in class weka.classifiers.trees.LMT: Returns the number of leaves in the tree
measureNumPredictionLeaves() - Method in class weka.classifiers.trees.ADTree: Calls measure function for prediction leaf size - the number of prediction nodes without children.
measureNumRules() - Method in class weka.classifiers.misc.FLR: Additional measure Number of Rules
measureNumRules() - Method in class weka.classifiers.rules.PART: Return the number of rules.
measureNumRules() - Method in class weka.classifiers.rules.DecisionTable: Returns the number of rules
measureNumRules() - Method in class weka.classifiers.trees.J48: Returns the number of rules (same as number of leaves)
measureNumRules() - Method in class weka.classifiers.trees.m5.M5Base: return the number of rules
measureOutOfBagError() - Method in class weka.classifiers.meta.Bagging: Gets the out of bag error that was calculated as the classifier was built.
measureOutOfBagError() - Method in class weka.classifiers.trees.RandomForest: Gets the out of bag error that was calculated as the classifier was built.
measureSelectionTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Additional measure --- time taken (milliseconds) to select the attributes
measureTime() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Additional measure --- time taken (milliseconds) to select attributes and build the classifier
measureTreeSize() - Method in class weka.classifiers.trees.J48: Returns the size of the tree
measureTreeSize() - Method in class weka.classifiers.trees.ADTree: Calls measure function for tree size - the total number of nodes.
measureTreeSize() - Method in class weka.classifiers.trees.LMT: Returns the size of the tree
merge(ADTree) - Method in class weka.classifiers.trees.ADTree: Merges two trees together.
merge(PredictionNode, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode: Merges this node with another.
merge(SimpleLinkedList, Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
mergeAllItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet: Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeInstance(Instance) - Method in class weka.core.Instance: Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.BinarySparseInstance: Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.SparseInstance: Merges this instance with the given instance and returns the result.
mergeInstances(Instances, Instances) - Static method in class weka.core.Instances: Merges two sets of Instances together.
MergeTwoValues - class weka.filters.unsupervised.attribute.MergeTwoValues.: Merges two values of a nominal attribute.
MergeTwoValues() - Constructor for class weka.filters.unsupervised.attribute.MergeTwoValues
metaClassifierTipText() - Method in class weka.classifiers.meta.Stacking: Returns the tip text for this property
MetaCost - class weka.classifiers.meta.MetaCost.: This metaclassifier makes its base classifier cost-sensitive using the method specified in
MetaCost() - Constructor for class weka.classifiers.meta.MetaCost
METHOD_1_AGAINST_1 - Static variable in class weka.classifiers.meta.MultiClassClassifier
METHOD_1_AGAINST_ALL - Static variable in class weka.classifiers.meta.MultiClassClassifier: The error correction modes
METHOD_ERROR_EXHAUSTIVE - Static variable in class weka.classifiers.meta.MultiClassClassifier
METHOD_ERROR_RANDOM - Static variable in class weka.classifiers.meta.MultiClassClassifier
methodNameTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform: Returns the tip text for this property
methodTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
metricTypeTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
min - Variable in class weka.experiment.Stats: The minimum value seen, or Double.NaN if no values seen
minAbs(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the minimum absolute value of some elements of a column, that is, the elements of A[i0:i1][j].
minBucketSizeTipText() - Method in class weka.classifiers.rules.OneR: Returns the tip text for this property
minChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
minDataDLIfDeleted(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats: Compute the minimal data description length of the ruleset if the rule in the given position is deleted.
The min_data_DL_if_deleted = data_DL_if_deleted - potential
minDataDLIfExists(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats: Compute the minimal data description length of the ruleset if the rule in the given position is NOT deleted.
The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
minimizeExpectedCostTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
minimumBucketSizeTipText() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string for this option suitable for display in the gui as a tip text
minIndex(double[]) - Static method in class weka.core.Utils: Returns index of minimum element in a given array of doubles.
minIndex(int[]) - Static method in class weka.core.Utils: Returns index of minimum element in a given array of integers.
minMetricTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
minNoTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
minNumInstancesTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
minNumObjTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
minNumTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
minNumTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property
minProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the smallest transformation probability
minsAndMaxs(Instances, double[][], int) - Method in class weka.classifiers.trees.j48.C45Split: Returns the minsAndMaxs of the index.th subset.
minStdDevTipText() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns the tip text for this property
minStdDevTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
minus(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Subtracts a value
minus(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Subtracts another DoubleVector element by element
minus(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: C = A - B
minusEquals(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Subtracts a value in place
minusEquals(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Subtracts another DoubleVector element by element in place
minusEquals(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: A = A - B
minVariancePropTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
MISSING_SHAPE - Static variable in class weka.gui.visualize.Plot2D
MISSING_VALUE - Static variable in interface weka.classifiers.evaluation.Prediction: Constant representing a missing value.
missingCount - Variable in class weka.core.AttributeStats: The number of missing values
missingMergeTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval: Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.GainRatioAttributeEval: Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns the tip text for this property
missingModeTipText() - Method in class weka.classifiers.lazy.KStar: Returns the tip text for this property
missingSeperateTipText() - Method in class weka.attributeSelection.CfsSubsetEval: Returns the tip text for this property
missingValue() - Static method in class weka.core.Instance: Returns the double that codes "missing".
missingValuesTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
MixtureDistribution - class weka.classifiers.functions.pace.MixtureDistribution.: Abtract class for manipulating mixture distributions.
MixtureDistribution() - Constructor for class weka.classifiers.functions.pace.MixtureDistribution
MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClassifierPanel: The filename extension that should be used for model files
MODEL_FILE_EXTENSION - Static variable in class weka.gui.explorer.ClustererPanel: The filename extension that should be used for model files
modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the class probabilities for an instance according to the logistic model at the node.
modelErrors() - Method in class weka.classifiers.trees.lmt.LMTNode: Updates the numIncorrectModel field for all nodes.
ModelSelection - class weka.classifiers.trees.j48.ModelSelection.: Abstract class for model selection criteria.
ModelSelection() - Constructor for class weka.classifiers.trees.j48.ModelSelection
modelsToString() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns a string describing the logistic regression function at the node.
modifyHeaderTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
momentumTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
mostExplainingColumn(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns the index of the column that has the largest (squared) response, when each of columns pvt[ks:] is moved to become the ks-th column.
mouseClicked(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Does nothing.
mouseDragged(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Performs intermediate updates to what the user wishes to do.
mouseEntered(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Does nothing.
mouseExited(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Does nothing.
mouseMoved(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Does nothing.
mousePressed(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Determines what action the user wants to perform.
mouseReleased(MouseEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer: Performs the final stages of what the user wants to perform.
MultiBoostAB - class weka.classifiers.meta.MultiBoostAB.: Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
MultiBoostAB() - Constructor for class weka.classifiers.meta.MultiBoostAB
MultiClassClassifier - class weka.classifiers.meta.MultiClassClassifier.: Class for handling multi-class datasets with 2-class distribution classifiers.
MultiClassClassifier() - Constructor for class weka.classifiers.meta.MultiClassClassifier
MultilayerPerceptron - class weka.classifiers.functions.MultilayerPerceptron.: A Classifier that uses backpropagation to classify instances.
MultilayerPerceptron() - Constructor for class weka.classifiers.functions.MultilayerPerceptron: The constructor.
MultipleClassifiersCombiner - class weka.classifiers.MultipleClassifiersCombiner.: Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
MultipleClassifiersCombiner() - Constructor for class weka.classifiers.MultipleClassifiersCombiner
multiply(Matrix) - Method in class weka.core.Matrix: Returns the multiplication of two matrices
multiResultsetFull(int, int) - Method in class weka.experiment.PairedTTester: Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetRanking(int) - Method in class weka.experiment.PairedTTester
multiResultsetSummary(int) - Method in class weka.experiment.PairedTTester: Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetWins(int) - Method in class weka.experiment.PairedTTester: Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
MultiScheme - class weka.classifiers.meta.MultiScheme.: Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
MultiScheme() - Constructor for class weka.classifiers.meta.MultiScheme
mutationProbTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property

N

NaiveBayes - class weka.classifiers.bayes.NaiveBayes.: Class for a Naive Bayes classifier using estimator classes.
NaiveBayes() - Constructor for class weka.classifiers.bayes.NaiveBayes
NaiveBayesMultinomial - class weka.classifiers.bayes.NaiveBayesMultinomial.: The core equation for this classifier: P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule) where Ci is class i and D is a document
NaiveBayesMultinomial() - Constructor for class weka.classifiers.bayes.NaiveBayesMultinomial
NaiveBayesSimple - class weka.classifiers.bayes.NaiveBayesSimple.: Class for building and using a simple Naive Bayes classifier.
NaiveBayesSimple() - Constructor for class weka.classifiers.bayes.NaiveBayesSimple
NaiveBayesUpdateable - class weka.classifiers.bayes.NaiveBayesUpdateable.: Class for a Naive Bayes classifier using estimator classes.
NaiveBayesUpdateable() - Constructor for class weka.classifiers.bayes.NaiveBayesUpdateable
name() - Method in class weka.core.Attribute: Returns the attribute's name.
name() - Method in class weka.core.Option: Returns the option's name.
NamedColor - class weka.gui.treevisualizer.NamedColor.: This class contains a color name and the rgb values of that color
NamedColor(String, int, int, int) - Constructor for class weka.gui.treevisualizer.NamedColor
nameTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the tip text for this property
NBconditionalProb(Instance, int) - Method in class weka.classifiers.bayes.AODE: Calculates the probability of the specified class for the given test instance, using naive Bayes.
NDConditionalEstimator - class weka.estimators.NDConditionalEstimator.: Conditional probability estimator for a numeric domain conditional upon a discrete domain (utilises separate normal estimators for each discrete conditioning value).
NDConditionalEstimator(int, double) - Constructor for class weka.estimators.NDConditionalEstimator: Constructor
needExponentialFormat(double) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
NEG - Static variable in class weka.associations.tertius.Literal
negationIncludedIn(LiteralSet) - Method in class weka.associations.tertius.LiteralSet: Test if the negation of this LiteralSet is included in another LiteralSet.
negationSatisfies(Instance) - Method in class weka.associations.tertius.Literal
negationSatisfies(Instance) - Method in class weka.associations.tertius.AttributeValueLiteral
negationTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
negative() - Method in class weka.associations.tertius.Literal
nestedEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Returns the optimal nested model estimate of a vector.
NeuralConnection - class weka.classifiers.functions.neural.NeuralConnection.: Abstract unit in a NeuralNetwork.
NeuralConnection(String) - Constructor for class weka.classifiers.functions.neural.NeuralConnection: Constructs The unit with the basic connection information prepared for use.
NeuralMethod - interface weka.classifiers.functions.neural.NeuralMethod.: This is an interface used to create classes that can be used by the neuralnode to perform all it's computations.
NeuralNode - class weka.classifiers.functions.neural.NeuralNode.: This class is used to represent a node in the neuralnet.
NeuralNode(String, Random, NeuralMethod) - Constructor for class weka.classifiers.functions.neural.NeuralNode
NEW_BATCH - Static variable in class weka.gui.beans.IncrementalClassifierEvent
newEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit: Computes entropy of distribution after splitting.
newNominalRule(Attribute, Instances, int[]) - Method in class weka.classifiers.rules.OneR: Create a rule branching on this nominal attribute.
newNumericRule(Attribute, Instances, int[]) - Method in class weka.classifiers.rules.OneR: Create a rule branching on this numeric attribute
newRule(Attribute, Instances) - Method in class weka.classifiers.rules.OneR: Create a rule branching on this attribute.
next - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: next table entry (separate chaining)
next() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
next(int) - Method in interface weka.classifiers.IterativeClassifier: Performs one iteration.
next(int) - Method in class weka.classifiers.trees.ADTree: Performs one iteration.
nextElement() - Method in class weka.core.FastVector.FastVectorEnumeration: Returns the next element.
nextErlang(int) - Method in class weka.core.RandomVariates: Generate a value of a variate following standard Erlang distribution.
nextExponential() - Method in class weka.core.RandomVariates: Generate a value of a variate following standard exponential distribution using simple inverse method.
nextGamma(double) - Method in class weka.core.RandomVariates: Generate a value of a variate following standard Gamma distribution with shape parameter a.
nextIteration() - Method in class weka.experiment.Experiment: Carries out the next iteration of the experiment.
nextIteration() - Method in class weka.experiment.RemoteExperiment: Overides the one in Experiment
nextSplitAddedOrder() - Method in class weka.classifiers.trees.ADTree: Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.
NNConditionalEstimator - class weka.estimators.NNConditionalEstimator.: Conditional probability estimator for a numeric domain conditional upon a numeric domain (using Mahalanobis distance).
NNConditionalEstimator() - Constructor for class weka.estimators.NNConditionalEstimator
NNge - class weka.classifiers.rules.NNge.: NNge classifier.
NNge() - Constructor for class weka.classifiers.rules.NNge
nnls(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Solves the nonnegative linear squares problem.
nnlse(PaceMatrix, PaceMatrix, PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Solves the nonnegative least squares problem with equality constraint.
nnlse1(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Solves the nonnegative least squares problem with equality constraint.
NNMMethod - Static variable in class weka.classifiers.functions.pace.MixtureDistribution: The nonnegative-measure-based method
NO_COMMAND - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Node - class weka.gui.treevisualizer.Node.: This class records all the data about a particular node for displaying.
Node(String, String, int, int, Color, String) - Constructor for class weka.gui.treevisualizer.Node: This will setup all the values of the node except for its top and center.
NodePlace - interface weka.gui.treevisualizer.NodePlace.: This is an interface for classes that wish to take a node structure and arrange them
nodeToString() - Method in class weka.classifiers.trees.m5.RuleNode: Returns a description of this node (debugging purposes)
noiseThresholdTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
NOMINAL - Static variable in class weka.core.Attribute: Constant set for nominal attributes.
nominalCounts - Variable in class weka.core.AttributeStats: Counts of each nominal value
nominalIndicesTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
nominalLabelsTipText() - Method in class weka.filters.unsupervised.attribute.Add: Returns the tip text for this property
NominalPrediction - class weka.classifiers.evaluation.NominalPrediction.: Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value.
NominalPrediction(double, double[]) - Constructor for class weka.classifiers.evaluation.NominalPrediction: Creates the NominalPrediction object with a default weight of 1.0.
NominalPrediction(double, double[], double) - Constructor for class weka.classifiers.evaluation.NominalPrediction: Creates the NominalPrediction object.
NominalToBinary - class weka.filters.supervised.attribute.NominalToBinary.: Converts all nominal attributes into binary numeric attributes.
NominalToBinary - class weka.filters.unsupervised.attribute.NominalToBinary.: Converts all nominal attributes into binary numeric attributes.
NominalToBinary() - Constructor for class weka.filters.supervised.attribute.NominalToBinary
NominalToBinary() - Constructor for class weka.filters.unsupervised.attribute.NominalToBinary
nominalToBinaryFilterTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
NONE - Static variable in class weka.gui.visualize.VisualizePanelEvent: No longer used
NonExchangeableDistance - interface coreComponents.NonExchangeableDistance.: This interface is useful only for classifiers which require the exchangeability assumption to be made about the data - i.e.
noNormalizationTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property
NonSparseToSparse - class weka.filters.unsupervised.instance.NonSparseToSparse.: A filter that converts all incoming instances into sparse format.
NonSparseToSparse() - Constructor for class weka.filters.unsupervised.instance.NonSparseToSparse
noPruningTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
NORM_EXPECTED_COST_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
norm1() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the L1-norm of the vector
norm1() - Method in class weka.classifiers.functions.pace.Matrix: One norm
norm2() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the L2-norm of the vector
NORMAL - Static variable in interface weka.gui.graphvisualizer.GraphConstants: NORMAL node - node actually contained in graphs description
normalDistribution - Static variable in class weka.classifiers.functions.pace.Maths: Distribution type: noraml
NormalEstimator - class weka.estimators.NormalEstimator.: Simple probability estimator that places a single normal distribution over the observed values.
NormalEstimator(double) - Constructor for class weka.estimators.NormalEstimator: Constructor that takes a precision argument.
normalInverse(double) - Static method in class weka.core.Statistics: Returns the value, x, for which the area under the Normal (Gaussian) probability density function (integrated from minus infinity to x) is equal to the argument y (assumes mean is zero, variance is one).
Normalize - class weka.filters.unsupervised.attribute.Normalize.: Normalizes all numeric values in the given dataset.
normalize() - Method in class weka.classifiers.CostMatrix: Normalizes the matrix so that the diagonal contains zeros.
normalize() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Normalizes the function values with L1-norm.
Normalize() - Constructor for class weka.filters.unsupervised.attribute.Normalize
normalize(double[]) - Static method in class weka.core.Utils: Normalizes the doubles in the array by their sum.
normalize(double[], double) - Static method in class weka.core.Utils: Normalizes the doubles in the array using the given value.
normalizeAttributesTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
normalizeDocLengthTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property
NormalizedPolyKernel - class weka.classifiers.functions.supportVector.NormalizedPolyKernel.: The normalized polynomial kernel.
NormalizedPolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.NormalizedPolyKernel: Creates a new NormalizedPolyKernel instance.
normalizeNumericClassTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
normalizeTipText() - Method in class weka.attributeSelection.PrincipalComponents: Returns the tip text for this property
normalizeWordWeightsTipText() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns the tip text for this property
NormalMixture - class weka.classifiers.functions.pace.NormalMixture.: Class for manipulating normal mixture distributions.
NormalMixture() - Constructor for class weka.classifiers.functions.pace.NormalMixture: Contructs an empty NormalMixture
normalProbability(double) - Static method in class weka.core.Statistics: Returns the area under the Normal (Gaussian) probability density function, integrated from minus infinity to x (assumes mean is zero, variance is one).
normF() - Method in class weka.classifiers.functions.pace.Matrix: Frobenius norm
normInf() - Method in class weka.classifiers.functions.pace.Matrix: Infinity norm
NORTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
NoSplit - class weka.classifiers.trees.j48.NoSplit.: Class implementing a "no-split"-split.
NoSplit(Distribution) - Constructor for class weka.classifiers.trees.j48.NoSplit: Creates "no-split"-split for given distribution.
NoSupportForMissingValuesException - exception weka.core.NoSupportForMissingValuesException.: Exception that is raised by an object that is unable to process data with missing values.
NoSupportForMissingValuesException() - Constructor for class weka.core.NoSupportForMissingValuesException: Creates a new NoSupportForMissingValuesException with no message.
NoSupportForMissingValuesException(String) - Constructor for class weka.core.NoSupportForMissingValuesException: Creates a new NoSupportForMissingValuesException.
NOT_DRAWABLE - Static variable in interface weka.core.Drawable
notCoveredInstances() - Method in class weka.classifiers.trees.m5.Rule: Get the instances not covered by this rule
NullFilter - class weka.filters.NullFilter.: A simple instance filter that allows no instances to pass through.
NullFilter() - Constructor for class weka.filters.NullFilter
NUM_RAND_COLS - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
numAllConditions(Instances) - Static method in class weka.classifiers.rules.RuleStats: Compute the number of all possible conditions that could appear in a rule of a given data.
numAntdsTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
numArguments() - Method in class weka.core.Option: Returns the option's number of arguments.
numAttemptsOfGeneOptionTipText() - Method in class weka.classifiers.rules.NNge: Returns the tip text for this property
numAttributes() - Method in class weka.core.Instance: Returns the number of attributes.
numAttributes() - Method in class weka.core.SparseInstance: Returns the number of attributes.
numAttributes() - Method in class weka.core.Instances: Returns the number of attributes.
numBags() - Method in class weka.classifiers.trees.j48.Distribution: Returns number of bags.
numberAttributesSelected() - Method in class weka.attributeSelection.AttributeSelection: Return the number of attributes selected from the most recent run of attribute selection
numberLiteralsTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
numberOfAttributesTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
numberOfClusters() - Method in class weka.clusterers.Clusterer: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.SimpleKMeans: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.EM: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.FarthestFirst: Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.Cobweb: Returns the number of clusters.
numberOfLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode: Get the number of linear models in the tree
numBinsTipText() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns the tip text for this property
numBoostingIterationsTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
numBoostingIterationsTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
numChildren() - Method in class weka.gui.HierarchyPropertyParser: The number of the children nodes.
numClassAttributeValues() - Method in class weka.classifiers.functions.SMO
numClassAttributeValues() - Method in class classifiers.PC_SMO
numClassAttributeValues() - Method in class classifiers.AlphaProb_SMO
numClasses() - Method in class weka.core.Instance: Returns the number of class labels.
numClasses() - Method in class weka.core.Instances: Returns the number of class labels.
numClasses() - Method in class weka.classifiers.trees.j48.Distribution: Returns number of classes.
numClustersTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.FarthestFirst: Returns the tip text for this property
numClustersTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
numColumns() - Method in class weka.core.Matrix: Returns the number of columns in the matrix.
numCorrect() - Method in class weka.classifiers.trees.j48.Distribution: Returns perClass(maxClass()).
numCorrect(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns perClassPerBag(index,maxClass(index)).
numDistinctValues(Attribute) - Method in class weka.core.Instances: Returns the number of distinct values of a given attribute.
numDistinctValues(int) - Method in class weka.core.Instances: Returns the number of distinct values of a given attribute.
numElements() - Method in class weka.classifiers.functions.supportVector.SMOset: Returns the number of elements in the set.
NUMERIC - Static variable in class weka.core.Attribute: Constant set for numeric attributes.
NumericPrediction - class weka.classifiers.evaluation.NumericPrediction.: Encapsulates an evaluatable numeric prediction: the predicted class value plus the actual class value.
NumericPrediction(double, double) - Constructor for class weka.classifiers.evaluation.NumericPrediction: Creates the NumericPrediction object with a default weight of 1.0.
NumericPrediction(double, double, double) - Constructor for class weka.classifiers.evaluation.NumericPrediction: Creates the NumericPrediction object.
numericStats - Variable in class weka.core.AttributeStats: Stats on numeric value distributions
numericTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
NumericToBinary - class weka.filters.unsupervised.attribute.NumericToBinary.: Converts all numeric attributes into binary attributes (apart from the class attribute): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
NumericToBinary() - Constructor for class weka.filters.unsupervised.attribute.NumericToBinary
NumericTransform - class weka.filters.unsupervised.attribute.NumericTransform.: Transforms numeric attributes using a given transformation method.
NumericTransform() - Constructor for class weka.filters.unsupervised.attribute.NumericTransform: Default constructor -- sets the default transform method to java.lang.Math.abs().
numEvals() - Method in class weka.classifiers.functions.supportVector.RBFKernel: Returns the number of time Eval has been called.
numEvals() - Method in class weka.classifiers.functions.supportVector.Kernel: Returns the number of kernel evaluation performed.
numEvals() - Method in class weka.classifiers.functions.supportVector.PolyKernel: Returns the number of time Eval has been called.
numFalseNegatives(int) - Method in class weka.classifiers.Evaluation: Calculate number of false negatives with respect to a particular class.
numFalseNegatives(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate number of false negatives with respect to a particular class.
numFalsePositives(int) - Method in class weka.classifiers.Evaluation: Calculate number of false positives with respect to a particular class.
numFalsePositives(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate number of false positives with respect to a particular class.
numFeaturesTipText() - Method in class weka.classifiers.trees.RandomForest: Returns the tip text for this property
numFoldersMIOptionTipText() - Method in class weka.classifiers.rules.NNge: Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.CVParameterSelection: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.Stacking: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
numFoldsTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
numFoldsTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
numFoldsTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
numIncorrect() - Method in class weka.classifiers.trees.j48.Distribution: Returns total-numCorrect().
numIncorrect(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns perBag(index)-numCorrect(index).
numInstances() - Method in class weka.core.Instances: Returns the number of instances in the dataset.
numInstances() - Method in class weka.classifiers.Evaluation: Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
numInstances() - Method in class evaluationMethods.EstimatorEvaluation: Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
numIterationsTipText() - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.meta.Decorate: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the tip text for this property
numIterationsTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
numLeaves() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns number of leaves in tree structure.
numLeaves() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the number of leaves (normal count).
numLeaves(int) - Method in class weka.classifiers.trees.m5.RuleNode: Sets the leaves' numbers
numLiterals() - Method in class weka.associations.tertius.Rule: Give the number of literals in this rule.
numLiterals() - Method in class weka.associations.tertius.Predicate
numLiterals() - Method in class weka.associations.tertius.LiteralSet: Give the number of literals in this set.
numNeighboursTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
numNodes() - Method in class weka.classifiers.trees.REPTree: Computes size of the tree.
numNodes() - Method in class weka.classifiers.trees.RandomTree: Computes size of the tree.
numNodes() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns number of nodes in tree structure.
numNodes() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns the number of nodes.
numOfBoostingIterationsTipText() - Method in class weka.classifiers.trees.ADTree
numParameters() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Return the number of parameters (coefficients) in the linear model
numParameters() - Method in class weka.classifiers.functions.LinearRegression: Get the number of coefficients used in the model
numParameters() - Method in class weka.classifiers.functions.PaceRegression: Get the number of coefficients used in the model
numPatterns() - Method in class coreComponents.PatternCounter
numPendingOutput() - Method in class weka.filters.Filter: Returns the number of instances pending output
numPendingOutput() - Method in class weka.filters.unsupervised.attribute.RemoveType: Returns the number of instances pending output
numRows() - Method in class weka.core.Matrix: Returns the number of rows in the matrix.
numRules() - Method in class weka.classifiers.rules.part.MakeDecList: Outputs the number of rules in the classifier.
numRulesTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
numRunsTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
numSubCmtysTipText() - Method in class weka.classifiers.meta.MultiBoostAB: Returns the tip text for this property
numSubsets() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns the number of created subsets for the split.
numToSelectTipText() - Method in class weka.attributeSelection.ForwardSelection: Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
numTreesTipText() - Method in class weka.classifiers.trees.RandomForest: Returns the tip text for this property
numTrueNegatives(int) - Method in class weka.classifiers.Evaluation: Calculate the number of true negatives with respect to a particular class.
numTrueNegatives(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate the number of true negatives with respect to a particular class.
numTruePositives(int) - Method in class weka.classifiers.Evaluation: Calculate the number of true positives with respect to a particular class.
numTruePositives(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate the number of true positives with respect to a particular class.
numValues() - Method in class weka.core.Instance: Returns the number of values present.
numValues() - Method in class weka.core.Attribute: Returns the number of attribute values.
numValues() - Method in class weka.core.SparseInstance: Returns the number of values in the sparse vector.
numXValFoldsTipText() - Method in class weka.classifiers.meta.ThresholdSelector

O

Obfuscate - class weka.filters.unsupervised.attribute.Obfuscate.: A simple instance filter that renames the relation, all attribute names and all nominal (and string) attribute values.
Obfuscate() - Constructor for class weka.filters.unsupervised.attribute.Obfuscate
observedComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their observed number of counter-instances.
obtainVotes(Instance) - Method in class weka.classifiers.functions.SMO: Returns an array of votes for the given instance.
obtainVotes(Instance) - Method in class classifiers.PC_SMO: Returns an array of votes for the given instance.
obtainVotes(Instance) - Method in class classifiers.AlphaProb_SMO: Returns an array of votes for the given instance.
OFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
oldEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit: Computes entropy of distribution before splitting.
ON - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants: Some usefull constants
onDemandDirectoryTipText() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns the tip text for this property
onDemandDirectoryTipText() - Method in class weka.classifiers.meta.MetaCost: Returns the tip text for this property
onDemandDirectoryTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
OneR - class weka.classifiers.rules.OneR.: Class for building and using a 1R classifier.
OneR() - Constructor for class weka.classifiers.rules.OneR
OneRAttributeEval - class weka.attributeSelection.OneRAttributeEval.: Class for Evaluating attributes individually by using the OneR classifier.
OneRAttributeEval() - Constructor for class weka.attributeSelection.OneRAttributeEval: Constructor
ONLINE_ERRATIC - Static variable in class evaluationMethods.OnlineEvaluation
ONLINE_LAZY_AP_GAP - Static variable in class evaluationMethods.OnlineEvaluation
ONLINE_LAZY_FIXED - Static variable in class evaluationMethods.OnlineEvaluation
ONLINE_LAZY_GP_GAP - Static variable in class evaluationMethods.OnlineEvaluation
ONLINE_NORMAL - Static variable in class evaluationMethods.OnlineEvaluation
ONLINE_SLOW_AP_GAP - Static variable in class evaluationMethods.OnlineEvaluation
ONLINE_SLOW_FIXED - Static variable in class evaluationMethods.OnlineEvaluation
ONLINE_SLOW_GP_GAP - Static variable in class evaluationMethods.OnlineEvaluation
OnlineEvaluation - class evaluationMethods.OnlineEvaluation.: Class for evaluating machine learning models in the online setting.
OnlineEvaluation() - Constructor for class evaluationMethods.OnlineEvaluation
onlyAlphabeticTokensTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
onUnit(Graphics, int, int, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to determine if the point at x,y is on the unit.
openFrame(String) - Method in class weka.gui.ResultHistoryPanel: Opens the named result in a separate frame.
optimisticComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their optimistic estimate.
optimisticThenObservedComparator - Static variable in class weka.associations.tertius.Rule: Comparator used to compare two rules according to their optimistic estimate and then their observed number of counter-instances.
Optimization - class weka.core.Optimization.: Implementation of Active-sets method with BFGS update to solve optimization problem with only bounds constraints in multi-dimensions.
Optimization() - Constructor for class weka.core.Optimization
optimizationsTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
OPTIMIZE_0 - Static variable in class weka.classifiers.meta.ThresholdSelector
OPTIMIZE_1 - Static variable in class weka.classifiers.meta.ThresholdSelector
OPTIMIZE_LFREQ - Static variable in class weka.classifiers.meta.ThresholdSelector
OPTIMIZE_MFREQ - Static variable in class weka.classifiers.meta.ThresholdSelector
OPTIMIZE_POS_NAME - Static variable in class weka.classifiers.meta.ThresholdSelector
Option - class weka.core.Option.: Class to store information about an option.
Option(String, String, int, String) - Constructor for class weka.core.Option: Creates new option with the given parameters.
OptionHandler - interface weka.core.OptionHandler.: Interface to something that understands options.
orderAdded - Variable in class weka.classifiers.trees.adtree.Splitter: The number this node was in the order of nodes added to the tree
ORDERED - Static variable in class weka.datagenerators.BIRCHCluster
ORDERING_MODULO - Static variable in class weka.core.Attribute: Constant set for modulo-ordered attributes.
ORDERING_ORDERED - Static variable in class weka.core.Attribute: Constant set for ordered attributes.
ORDERING_SYMBOLIC - Static variable in class weka.core.Attribute: Constant set for symbolic attributes.
ordering() - Method in class weka.core.Attribute: Returns the ordering of the attribute.
OrdinalClassClassifier - class weka.classifiers.meta.OrdinalClassClassifier.: Meta classifier for transforming an ordinal class problem to a series of binary class problems.
OrdinalClassClassifier() - Constructor for class weka.classifiers.meta.OrdinalClassClassifier
originalValue(double) - Method in class weka.filters.supervised.attribute.ClassOrder: Return the original internal class value given the randomized class value, i.e.
OTHER - Static variable in class probabilityMachine.vpm.VPMKNearestNeighbours
ouputOnlineSummaryString(Classifier) - Method in class evaluationMethods.OnlineEvaluation: Used to replace the old method to summarise the online experiment
OUTPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is an output unit.
output() - Method in class weka.filters.Filter: Output an instance after filtering and remove from the output queue.
output() - Method in class weka.filters.unsupervised.attribute.RemoveType: Output an instance after filtering and remove from the output queue.
outputDebugPValuesProbs() - Method in class evaluationMethods.OnlineEvaluation: Creates an output string of the p-values or probabilities output by the learning machine at the last trial
outputDebugStatOutput(Classifier) - Method in class evaluationMethods.OnlineEvaluation: Gives a brief summary of some of that stats as you go along.
outputFileSpecified() - Method in class coreComponents.SVMToArff
outputFileSpecified() - Method in class coreComponents.DataToArff
outputFileSpecified() - Method in class evaluationMethods.CreateROCCurve
outputFileSpecified() - Method in class evaluationMethods.CalculateLoss
outputFileSpecified() - Method in class evaluationMethods.CreateReliabilityCurve
outputFileTipText() - Method in class weka.experiment.CSVResultListener: Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
outputFormat() - Method in class weka.gui.streams.InstanceJoiner: Gets the format of the output instances.
outputFormat() - Method in class weka.gui.streams.InstanceLoader
outputFormat() - Method in interface weka.gui.streams.InstanceProducer
outputFormat() - Method in class weka.filters.Filter: Deprecated. use getOutputFormat() instead.
outputPeek() - Method in class weka.gui.streams.InstanceJoiner: Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.gui.streams.InstanceLoader
outputPeek() - Method in interface weka.gui.streams.InstanceProducer
outputPeek() - Method in class weka.filters.Filter: Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.filters.unsupervised.attribute.RemoveType: Output an instance after filtering but do not remove from the output queue.
outputValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to get the output value of this unit.
outputValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to get the output value of this unit.
outputValue(NeuralNode) - Method in class weka.classifiers.functions.neural.SigmoidUnit: This function calculates what the output value should be.
outputValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod: This function calculates what the output value should be.
outputValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit: This function calculates what the output value should be.
outputWordCountsTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property
OutputZipper - class weka.experiment.OutputZipper.: OutputZipper writes output to either gzipped files or to a multi entry zip file.
OutputZipper(File) - Constructor for class weka.experiment.OutputZipper: Constructor.
OVAL - Static variable in class weka.gui.visualize.VisualizePanelEvent
overFrequencyThreshold(double) - Method in class weka.associations.tertius.Rule: Test if this rule is over the frequency threshold.
overFrequencyThreshold(double) - Method in class weka.associations.tertius.LiteralSet: Test if this LiteralSet has more counter-instances than the threshold.

P

pace2(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the pace2 estimate of a vector.
pace4(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the pace4 estimate of a vector.
pace6(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the pace6 estimate of a single value.
pace6(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Returns the pace6 estimate of a vector.
PaceMatrix - class weka.classifiers.functions.pace.PaceMatrix.: Class for matrix manipulation used for pace regression.
PaceMatrix(double[][]) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PACE matrix from a 2-D array.
PaceMatrix(double[][], int, int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PACE matrix quickly without checking arguments.
PaceMatrix(double[], int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PaceMatrix from a one-dimensional packed array
PaceMatrix(DoubleVector) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PaceMatrix with a single column from a DoubleVector
PaceMatrix(int, int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct an m-by-n PACE matrix of zeros.
PaceMatrix(int, int, double) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct an m-by-n constant PACE matrix.
PaceMatrix(Matrix) - Constructor for class weka.classifiers.functions.pace.PaceMatrix: Construct a PaceMatrix from a Matrix
PaceRegression - class weka.classifiers.functions.PaceRegression.: Class for building pace regression linear models and using them for prediction.
PaceRegression() - Constructor for class weka.classifiers.functions.PaceRegression
padLeft(String, int) - Static method in class weka.core.Utils: Pads a string to a specified length, inserting spaces on the left as required.
padRight(String, int) - Static method in class weka.core.Utils: Pads a string to a specified length, inserting spaces on the right as required.
paintComponent(Graphics) - Method in class weka.gui.PropertyPanel: Paints the component, using the property editor's paint method.
paintComponent(Graphics) - Method in class weka.gui.AttributeVisualizationPanel: Paints this component
paintComponent(Graphics) - Method in class weka.gui.treevisualizer.TreeVisualizer: Updates the screen contents.
paintComponent(Graphics) - Method in class weka.gui.beans.BeanVisual
paintComponent(Graphics) - Method in class weka.gui.visualize.ClassPanel: Renders this component
paintComponent(Graphics) - Method in class weka.gui.visualize.Plot2D: Renders this component
paintConnections(Graphics) - Static method in class weka.gui.beans.BeanConnection: Renders the connections and their names on the supplied graphics context
paintLabels(Graphics) - Static method in class weka.gui.beans.BeanInstance: Renders the textual labels for the beans.
paintValue(Graphics, Rectangle) - Method in class weka.gui.FileEditor: Paints a representation of the current Object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericObjectEditor: Paints a representation of the current Object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericArrayEditor: Paints a representation of the current classifier.
paintValue(Graphics, Rectangle) - Method in class weka.gui.CostMatrixEditor: Paints a graphical representation of the object.
PairedCorrectedTTester - class weka.experiment.PairedCorrectedTTester.: Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic.
PairedCorrectedTTester() - Constructor for class weka.experiment.PairedCorrectedTTester
PairedStats - class weka.experiment.PairedStats.: A class for storing stats on a paired comparison (t-test and correlation)
PairedStats(double) - Constructor for class weka.experiment.PairedStats: Creates a new PairedStats object with the supplied significance level.
PairedStatsCorrected - class weka.experiment.PairedStatsCorrected.: A class for storing stats on a paired comparison.
PairedStatsCorrected(double, double) - Constructor for class weka.experiment.PairedStatsCorrected: Creates a new PairedStatsCorrected object with the supplied significance level and train/test ratio.
PairedTTester - class weka.experiment.PairedTTester.: Calculates T-Test statistics on data stored in a set of instances.
PairedTTester() - Constructor for class weka.experiment.PairedTTester
pairwiseCoupling(double[][], double[][]) - Method in class weka.classifiers.functions.SMO: Implements pairwise coupling.
pairwiseCoupling(double[][], double[][]) - Method in class classifiers.PC_SMO: Implements pairwise coupling.
pairwiseCoupling(double[][], double[][]) - Method in class classifiers.AlphaProb_SMO: Implements pairwise coupling.
parentClass - Variable in class weka.experiment.PropertyNode: The class of the object with this property
parentNode() - Method in class weka.classifiers.trees.m5.RuleNode: Get the parent of this node
ParentSet - class weka.classifiers.bayes.ParentSet.: Helper class for Bayes Network classifiers.
ParentSet() - Constructor for class weka.classifiers.bayes.ParentSet: default constructor
ParentSet(int) - Constructor for class weka.classifiers.bayes.ParentSet: constructor
ParentSet(ParentSet) - Constructor for class weka.classifiers.bayes.ParentSet: copy constructor
parentValue() - Method in class weka.gui.HierarchyPropertyParser: The value in the parent node.
parse() - Method in class weka.gui.graphvisualizer.DotParser: This method parses the string or the InputStream that we passed in through the constructor and builds up the m_nodes and m_edges vectors
parse() - Method in class weka.gui.graphvisualizer.BIFParser: This method parses the string or the InputStream that we passed in through the constructor and builds up the m_nodes and m_edges vectors
parseDate(String) - Method in class weka.core.Attribute
PART - class weka.classifiers.rules.PART.: Class for generating a PART decision list.
PART_PROPERTY - Static variable in class weka.associations.tertius.IndividualLiteral
PART() - Constructor for class weka.classifiers.rules.PART
partFileTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
partition(Instances, int) - Static method in class weka.classifiers.rules.RuleStats: Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
partitionOptions(String[]) - Static method in class weka.core.Utils: Returns the secondary set of options (if any) contained in the supplied options array.
passesTest(Instance) - Method in class weka.datagenerators.Test: Determines whether an instance passes the test.
pattern(int, int) - Static method in class weka.classifiers.functions.pace.FloatingPointFormat
PatternCounter - class coreComponents.PatternCounter.: Here is a program for counting and assessing discretized patterns of attributes in a dataset.
PatternCounter.PatternObject - class coreComponents.PatternCounter.PatternObject.
PatternCounter.PatternObject(Instance) - Constructor for class coreComponents.PatternCounter.PatternObject
PatternCounter() - Constructor for class coreComponents.PatternCounter
PC_SMO - class classifiers.PC_SMO.: Implements John C.
PC_SMO() - Constructor for class classifiers.PC_SMO
pchisq(double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the cumulative probability of the Chi-squared distribution
pchisq(double, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the cumulative probability of the noncentral Chi-squared distribution.
pchisq(double, DoubleVector) - Static method in class weka.classifiers.functions.pace.Maths: Returns the cumulative probability of a set of noncentral Chi-squared distributions.
pctCorrect() - Method in class weka.classifiers.Evaluation: Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctCorrect() - Method in class evaluationMethods.EstimatorEvaluation: Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctIncorrect() - Method in class weka.classifiers.Evaluation: Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctIncorrect() - Method in class evaluationMethods.EstimatorEvaluation: Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctUnclassified() - Method in class weka.classifiers.Evaluation: Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
pctUnclassified() - Method in class evaluationMethods.EstimatorEvaluation: Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
peek() - Method in class weka.core.Queue: Gets object from the front of the queue.
perBag(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns number of (possibly fractional) instances in given bag.
percentageTipText() - Method in class weka.filters.unsupervised.instance.RemovePercentage: Returns the tip text for this property
percentAttributesUsed() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
percentThresholdTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
percentTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the tip text for this property
percentTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
percentToEliminatePerIterationTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
perClass(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns number of (possibly fractional) instances of given class.
perClassPerBag(int, int) - Method in class weka.classifiers.trees.j48.Distribution: Returns number of (possibly fractional) instances of given class in given bag.
performRequest(String) - Method in class weka.gui.beans.TrainTestSplitMaker: Perform the named request
performRequest(String) - Method in class weka.gui.beans.AttributeSummarizer: Perform a named user request
performRequest(String) - Method in class weka.gui.beans.StripChart: Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.Loader: Perform the named request
performRequest(String) - Method in class weka.gui.beans.Classifier: Perform a particular request
performRequest(String) - Method in class weka.gui.beans.Filter: Perform the named request
performRequest(String) - Method in class weka.gui.beans.ScatterPlotMatrix: Perform a named user request
performRequest(String) - Method in class weka.gui.beans.TextViewer: Perform the named request
performRequest(String) - Method in interface weka.gui.beans.UserRequestAcceptor: Perform the named request
performRequest(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Perform the named request
performRequest(String) - Method in class weka.gui.beans.GraphViewer: Perform the named request
performRequest(String) - Method in class weka.gui.beans.DataVisualizer: Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.CrossValidationFoldMaker: Perform the named request
phaseIID(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: See Sugiyama et al.
phaseIIU(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: See Sugiyama et al.
phaseIU(int, int[][]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: See Sugiyama et al.
PKIDiscretize - class weka.filters.unsupervised.attribute.PKIDiscretize.: Discretizes numeric attributes using equal frequency binning where the number of bins is equal to the square root of the number of non-missing values.
PKIDiscretize() - Constructor for class weka.filters.unsupervised.attribute.PKIDiscretize
place(Node) - Method in class weka.gui.treevisualizer.PlaceNode2: The Funtion to call to have the nodes arranged.
place(Node) - Method in class weka.gui.treevisualizer.PlaceNode1: Call this function to have each node in the tree starting at 'r' placed in a visual (not logical, they already are) tree position.
place(Node) - Method in interface weka.gui.treevisualizer.NodePlace: The function to call to postion the tree that starts at Node r
PlaceNode1 - class weka.gui.treevisualizer.PlaceNode1.: This class will place the Nodes of a tree.
PlaceNode1() - Constructor for class weka.gui.treevisualizer.PlaceNode1
PlaceNode2 - class weka.gui.treevisualizer.PlaceNode2.: This class will place the Nodes of a tree.
PlaceNode2() - Constructor for class weka.gui.treevisualizer.PlaceNode2
Plot2D - class weka.gui.visualize.Plot2D.: This class plots datasets in two dimensions.
Plot2D() - Constructor for class weka.gui.visualize.Plot2D: Constructor
Plot2DCompanion - interface weka.gui.visualize.Plot2DCompanion.: Interface for classes that need to draw to the Plot2D panel *before* Plot2D renders anything (eg.
PlotData2D - class weka.gui.visualize.PlotData2D.: This class is a container for plottable data.
PlotData2D(Instances) - Constructor for class weka.gui.visualize.PlotData2D: Construct a new PlotData2D using the supplied instances
plotGraph() - Method in class evaluationMethods.OnlineEvaluation: This an attempt to add a graphical element to this package allowing to plot performance on a graph.
plotInstancesAsMatlabLineGraph(String, String, String, String, Instances, String, String) - Static method in class coreComponents.MatlabUtils: Useful function for plotting the a data set of instances as a Matlab line graph.
PLURAL_DUMMY - Static variable in interface weka.gui.graphvisualizer.GraphConstants: PLURAL_DUMMY node - node with more than one outgoing edge i.e.
PLUS_SHAPE - Static variable in class weka.gui.visualize.Plot2D
plus(DiscreteFunction) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns the combined of two discrete functions
plus(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Adds a value to all the elements
plus(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Adds another vector element by element
plus(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: C = A + B
plusEquals(DiscreteFunction) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns the combined of two discrete functions.
plusEquals(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Adds a value to all the elements in place
plusEquals(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Adds another vector in place element by element
plusEquals(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: A = A + B
pmiss - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: transformation probability to missing value
PMMethod - Static variable in class weka.classifiers.functions.pace.MixtureDistribution: The probability-measure-based method
pnorm(double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the cumulative probability of the standard normal.
pnorm(double, double, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the cumulative probability of a normal distribution.
pnorm(double, DoubleVector, double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the cumulative probability of a set of normal distributions with different means.
PoissonEstimator - class weka.estimators.PoissonEstimator.: Simple probability estimator that places a single Poisson distribution over the observed values.
PoissonEstimator() - Constructor for class weka.estimators.PoissonEstimator
POLYGON - Static variable in class weka.gui.visualize.VisualizePanelEvent
PolyKernel - class weka.classifiers.functions.supportVector.PolyKernel.: The polynomial kernel : K(x, y) = ^p or K(x, y) = (+1)^p
PolyKernel(Instances, int, double, boolean) - Constructor for class weka.classifiers.functions.supportVector.PolyKernel: Creates a new PolyKernel instance.
pop() - Method in class weka.core.Queue: Pops an object from the front of the queue.
populationSizeTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
POS - Static variable in class weka.associations.tertius.Literal
position() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the position of the split in the sorted values.
position() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Returns the position of the split in the sorted values.
position() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the position of the split in the sorted values.
positive() - Method in class weka.associations.tertius.Literal
positiveDiagonal(PaceMatrix, IntVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Sets all diagonal elements to be positive (or nonnegative) without changing the least squares solution
postProcess() - Method in interface weka.experiment.ResultProducer: Perform any postprocessing.
postProcess() - Method in class weka.experiment.AveragingResultProducer: When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.Experiment: Signals that the experiment is finished running, so that cleanup can be done.
postProcess() - Method in class weka.experiment.LearningRateResultProducer: When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.CrossValidationResultProducer: Perform any postprocessing.
postProcess() - Method in class weka.experiment.RandomSplitResultProducer: Perform any postprocessing.
postProcess() - Method in class weka.experiment.DatabaseResultProducer: When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.RemoteExperiment: overides the one in Experiment
postProcess(int[]) - Method in class weka.attributeSelection.CfsSubsetEval: Calls locallyPredictive in order to include locally predictive attributes (if requested).
postProcess(int[]) - Method in class weka.attributeSelection.ASEvaluation: Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.
postProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultListener: Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.AveragingResultProducer: When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess(ResultProducer) - Method in interface weka.experiment.ResultListener: Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.CSVResultListener: Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.InstancesResultListener: Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer: When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer: When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
potential(int, double, double[], double[], boolean) - Method in class weka.classifiers.rules.RuleStats: Calculate the potential to decrease DL of the ruleset, i.e.
PotentialClassIgnorer - class weka.filters.unsupervised.attribute.PotentialClassIgnorer.: This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
PotentialClassIgnorer() - Constructor for class weka.filters.unsupervised.attribute.PotentialClassIgnorer
PRECISION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
precision(int) - Method in class weka.classifiers.Evaluation: Calculate the precision with respect to a particular class.
precision(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate the precision with respect to a particular class.
PreConstructedLinearModel - class weka.classifiers.trees.m5.PreConstructedLinearModel.: This class encapsulates a linear regression function.
PreConstructedLinearModel(double[], double) - Constructor for class weka.classifiers.trees.m5.PreConstructedLinearModel: Constructor
Predicate - class weka.associations.tertius.Predicate.
Predicate(String, int, boolean) - Constructor for class weka.associations.tertius.Predicate
predicted() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the predicted class value.
predicted() - Method in interface weka.classifiers.evaluation.Prediction: Gets the predicted class value.
predicted() - Method in class weka.classifiers.evaluation.NumericPrediction: Gets the predicted class value.
Prediction - interface weka.classifiers.evaluation.Prediction.: Encapsulates a single evaluatable prediction: the predicted value plus the actual class value.
PredictionAppender - class weka.gui.beans.PredictionAppender.: Bean that can can accept batch or incremental classifier events and produce dataset or instance events which contain instances with predictions appended.
PredictionAppender() - Constructor for class weka.gui.beans.PredictionAppender: Creates a new PredictionAppender instance.
PredictionAppenderBeanInfo - class weka.gui.beans.PredictionAppenderBeanInfo.: Bean info class for PredictionAppender.
PredictionAppenderBeanInfo() - Constructor for class weka.gui.beans.PredictionAppenderBeanInfo
PredictionAppenderCustomizer - class weka.gui.beans.PredictionAppenderCustomizer.: GUI Customizer for the prediction appender bean
PredictionAppenderCustomizer() - Constructor for class weka.gui.beans.PredictionAppenderCustomizer
PredictionNode - class weka.classifiers.trees.adtree.PredictionNode.: Class representing a prediction node in an alternating tree.
PredictionNode(double) - Constructor for class weka.classifiers.trees.adtree.PredictionNode: Creates a new prediction node.
prefix() - Method in interface weka.core.Matchable: Returns a string that describes a tree representing the object in prefix order.
prefix() - Method in class weka.classifiers.trees.J48: Returns tree in prefix order.
prefix() - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns tree in prefix order.
prePlot(Graphics) - Method in interface weka.gui.visualize.Plot2DCompanion: Something to be drawn before the plot itself
preProcess() - Method in interface weka.experiment.ResultProducer: Prepare to generate results.
preProcess() - Method in class weka.experiment.AveragingResultProducer: Prepare to generate results.
preProcess() - Method in class weka.experiment.LearningRateResultProducer: Prepare to generate results.
preProcess() - Method in class weka.experiment.CrossValidationResultProducer: Prepare to generate results.
preProcess() - Method in class weka.experiment.RandomSplitResultProducer: Prepare to generate results.
preProcess() - Method in class weka.experiment.DatabaseResultProducer: Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultListener: Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.AveragingResultProducer: Prepare for the results to be received.
preProcess(ResultProducer) - Method in interface weka.experiment.ResultListener: Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.CSVResultListener: Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.InstancesResultListener: Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer: Prepare for the results to be received.
preProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer: Prepare for the results to be received.
PreprocessPanel - class weka.gui.explorer.PreprocessPanel.: This panel controls simple preprocessing of instances.
PreprocessPanel() - Constructor for class weka.gui.explorer.PreprocessPanel: Creates the instances panel with no initial instances.
previous() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
PrincipalComponents - class weka.attributeSelection.PrincipalComponents.: Class for performing principal components analysis/transformation.
PrincipalComponents() - Constructor for class weka.attributeSelection.PrincipalComponents
print_hash_code() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Prints the hash code
print_hash_code() - Method in class weka.classifiers.rules.DecisionTable.hashKey: Prints the hash code
print() - Method in class weka.classifiers.bayes.ADNode
print(int, int) - Method in class weka.classifiers.functions.pace.Matrix: Print the matrix to stdout.
print(NumberFormat, int) - Method in class weka.classifiers.functions.pace.Matrix: Print the matrix to stdout.
print(PrintWriter, int, int) - Method in class weka.classifiers.functions.pace.Matrix: Print the matrix to the output stream.
print(PrintWriter, NumberFormat, int) - Method in class weka.classifiers.functions.pace.Matrix: Print the matrix to the output stream.
print(String) - Method in class weka.classifiers.bayes.VaryNode
printAllModels() - Method in class weka.classifiers.trees.m5.RuleNode: Print all the linear models at the learf (debugging purposes)
printArray(double[]) - Method in class probabilityMachine.VennProbabilityClassifier: Debugging function
printArray(double[]) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Debugging function
printArray(int[]) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Debugging function
printArray(int[]) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Debugging function
printArray(int[]) - Method in class probabilityMachine.VPMDistMetaLearner: Debugging function
printArray(int[]) - Method in class probabilityMachine.VennProbabilityClassifier: Debugging function
printArray(int[]) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Debugging function
printArray(String[]) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Debugging function
printArray(String[]) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Debugging function
printArray(String[]) - Method in class probabilityMachine.VPMDistMetaLearner: Debugging function
printArray(String[]) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Debugging function
printElements() - Method in class weka.classifiers.functions.supportVector.SMOset: Prints all the current elements in the set.
printFeatures() - Method in class weka.classifiers.rules.DecisionTable: Returns a string description of the features selected
printLeafModels() - Method in class weka.classifiers.trees.m5.RuleNode: print all leaf models
printNodeLinearModel() - Method in class weka.classifiers.trees.m5.RuleNode: print the linear model at this node
printOptions(String[]) - Static method in class weka.core.CheckOptionHandler: Prints the given options to a string.
priorEntropy() - Method in class weka.classifiers.Evaluation: Calculate the entropy of the prior distribution
priorEntropy() - Method in class evaluationMethods.EstimatorEvaluation: Calculate the entropy of the prior distribution
Prism - class weka.classifiers.rules.Prism.: Class for building and using a PRISM rule set for classifcation.
Prism() - Constructor for class weka.classifiers.rules.Prism
PROB_COST_FUNC_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
prob(int) - Method in class weka.classifiers.trees.j48.Distribution: Returns relative frequency of class over all bags.
prob(int, int) - Method in class weka.classifiers.trees.j48.Distribution: Returns relative frequency of class for given bag.
probabilityMachine - package probabilityMachine
probabilityMachine.vpm - package probabilityMachine.vpm
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.ChisqMixture: Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.NormalMixture: Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probGaussian(double, double, double) - Method in class probabilityMachine.VPMDistMetaLearner: Computes prob from a Gaussian!
probGaussian(double, double, double) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Computes prob from a Gaussian!
probGaussian(double, double, double) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Computes prob from a Gaussian!
probRound(double, Random) - Static method in class weka.core.Utils: Rounds a double to the next nearest integer value in a probabilistic fashion (e.g.
PROCESSING - Static variable in class weka.experiment.TaskStatusInfo
property - Variable in class weka.experiment.PropertyNode: Other info about the property
propertyChange(PropertyChangeEvent) - Method in class weka.gui.PropertySheetPanel: Updates the property sheet panel with a changed property and also passed the event along.
propertyChange(PropertyChangeEvent) - Method in class weka.gui.beans.KnowledgeFlow: Accept property change events
PropertyDialog - class weka.gui.PropertyDialog.: Support for PropertyEditors with custom editors: puts the editor into a separate frame.
PropertyDialog(PropertyEditor, int, int) - Constructor for class weka.gui.PropertyDialog: Creates the editor frame.
PropertyNode - class weka.experiment.PropertyNode.: Stores information on a property of an object: the class of the object with the property; the property descriptor, and the current value.
PropertyNode(Object) - Constructor for class weka.experiment.PropertyNode: Creates a mostly empty property.
PropertyNode(Object, PropertyDescriptor, Class) - Constructor for class weka.experiment.PropertyNode: Creates a fully specified property node.
PropertyPanel - class weka.gui.PropertyPanel.: Support for drawing a property value in a component.
PropertyPanel(PropertyEditor) - Constructor for class weka.gui.PropertyPanel: Create the panel with the supplied property editor.
PropertyPanel(PropertyEditor, boolean) - Constructor for class weka.gui.PropertyPanel: Create the panel with the supplied property editor, optionally ignoring any custom panel the editor can provide.
PropertySelectorDialog - class weka.gui.PropertySelectorDialog.: Allows the user to select any (supported) property of an object, including properties that any of it's property values may have.
PropertySelectorDialog(Frame, Object) - Constructor for class weka.gui.PropertySelectorDialog: Create the property selection dialog.
PropertySheetPanel - class weka.gui.PropertySheetPanel.: Displays a property sheet where (supported) properties of the target object may be edited.
PropertySheetPanel() - Constructor for class weka.gui.PropertySheetPanel: Creates the property sheet panel.
ProtectedProperties - class weka.core.ProtectedProperties.: Simple class that extends the Properties class so that the properties are unable to be modified.
ProtectedProperties(Properties) - Constructor for class weka.core.ProtectedProperties: Creates a set of protected properties from a set of normal ones.
prune() - Method in class weka.classifiers.trees.m5.RuleNode: Recursively prune the tree
prune() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree: Prunes a tree using C4.5's pruning procedure.
prune() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree: Prunes a tree.
prune(double) - Method in class weka.classifiers.trees.lmt.LMTNode: Prunes a logistic model tree using the CART pruning scheme, given a cost-complexity parameter alpha.
prune(double[], double[], Instances) - Method in class weka.classifiers.trees.lmt.LMTNode: Method for performing one fold in the cross-validation of the cost-complexity parameter.
PruneableClassifierTree - class weka.classifiers.trees.j48.PruneableClassifierTree.: Class for handling a tree structure that can be pruned using a pruning set.
PruneableClassifierTree(ModelSelection, boolean, int, boolean, int) - Constructor for class weka.classifiers.trees.j48.PruneableClassifierTree: Constructor for pruneable tree structure.
PruneableDecList - class weka.classifiers.rules.part.PruneableDecList.: Class for handling a partial tree structure that can be pruned using a pruning set.
PruneableDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.PruneableDecList: Constructor for pruneable partial tree structure.
pruneItemSets(FastVector, Hashtable) - Static method in class weka.associations.ItemSet: Prunes a set of (k)-item sets using the given (k-1)-item sets.
pruneRules(FastVector[], double) - Static method in class weka.associations.ItemSet: Prunes a set of rules.
PRUNETYPE_LOGLIKELIHOOD - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
PRUNETYPE_NONE - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost: The pruning types
pruningTypeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
PSI - Static variable in class weka.classifiers.functions.pace.Maths: The constant 1 / sqrt(2 pi)
PURE_INPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is a pure input unit.
PURE_OUTPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is a pure output unit.
push(Object) - Method in class weka.core.Queue: Appends an object to the back of the queue.
put(Object, Object) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
putAll(Map) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
putResultInTable(String, ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseUtils: Executes a database query to insert a result for the supplied key into the database.
pValuesForInstance(Instance) - Method in class confidenceMachine.ConfidenceClassifier: Predicts the confidence p-values for class memberships a given instance.
pValuesForInstance(Instance) - Method in class confidenceMachine.tcm.TCMBartsRMI: Returns the p-values for a given test instance.
pValuesForInstance(Instance) - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Returns the p-values for a given test instance.

Q

queryTipText() - Method in class weka.experiment.InstanceQuery: Returns the tip text for this property
Queue - class weka.core.Queue.: Class representing a FIFO queue.
Queue() - Constructor for class weka.core.Queue
quote(String) - Static method in class weka.core.Utils: Quotes a string if it contains special characters.

R

RacedIncrementalLogitBoost - class weka.classifiers.meta.RacedIncrementalLogitBoost.: Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
RacedIncrementalLogitBoost() - Constructor for class weka.classifiers.meta.RacedIncrementalLogitBoost
RaceSearch - class weka.attributeSelection.RaceSearch.: Class for performing a racing search.
RaceSearch() - Constructor for class weka.attributeSelection.RaceSearch
raceTypeTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
randEntropy - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the random entropy
RANDOM - Static variable in class weka.datagenerators.BIRCHCluster
RANDOM - Static variable in class weka.filters.supervised.attribute.ClassOrder: The class values are sorted in random order
random(int) - Static method in class weka.classifiers.functions.pace.DoubleVector: Returns a random vector of uniform distribution
random(int, int) - Static method in class weka.classifiers.functions.pace.Matrix: Generate matrix with random elements
RandomCommittee - class weka.classifiers.meta.RandomCommittee.: Class for creating a committee of random classifiers.
RandomCommittee() - Constructor for class weka.classifiers.meta.RandomCommittee: Constructor.
RandomForest - class weka.classifiers.trees.RandomForest.: Class for constructing random forests.
RandomForest() - Constructor for class weka.classifiers.trees.RandomForest
Randomizable - interface weka.core.Randomizable.: Interface to something that has random behaviour that is able to be seeded with an integer.
RandomizableClassifier - class weka.classifiers.RandomizableClassifier.: Abstract utility class for handling settings common to randomizable classifiers.
RandomizableClassifier() - Constructor for class weka.classifiers.RandomizableClassifier
RandomizableIteratedSingleClassifierEnhancer - class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer.: Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
RandomizableIteratedSingleClassifierEnhancer() - Constructor for class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
RandomizableMultipleClassifiersCombiner - class weka.classifiers.RandomizableMultipleClassifiersCombiner.: Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.
RandomizableMultipleClassifiersCombiner() - Constructor for class weka.classifiers.RandomizableMultipleClassifiersCombiner
RandomizableSingleClassifierEnhancer - class weka.classifiers.RandomizableSingleClassifierEnhancer.: Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
RandomizableSingleClassifierEnhancer() - Constructor for class weka.classifiers.RandomizableSingleClassifierEnhancer
Randomize - class weka.filters.unsupervised.instance.Randomize.: This filter randomly shuffles the order of instances passed through it.
Randomize() - Constructor for class weka.filters.unsupervised.instance.Randomize
randomize(int[], Random) - Method in class weka.classifiers.BVDecomposeSegCVSub: Accepts an array of ints and randomises the values in the array, using the random seed.
randomize(Random) - Method in class weka.core.Instances: Shuffles the instances in the set so that they are ordered randomly.
RANDOMIZED - Static variable in class weka.datagenerators.BIRCHCluster
randomizeDataTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
randomNormal(int, int) - Static method in class weka.classifiers.functions.pace.PaceMatrix: Generate matrix with standard-normally distributed random elements
randomOrderTipText() - Method in class weka.classifiers.bayes.BayesNetK2
RandomProjection - class weka.filters.unsupervised.attribute.RandomProjection.: Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length (It will reduce the number of attributes in the data while preserving much of its variation like PCA, but at a much less computational cost).
RandomProjection() - Constructor for class weka.filters.unsupervised.attribute.RandomProjection
RandomSearch - class weka.attributeSelection.RandomSearch.: Class for performing a random search.
RandomSearch() - Constructor for class weka.attributeSelection.RandomSearch: Constructor
randomSeedTipText() - Method in class weka.filters.supervised.instance.Resample: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.supervised.instance.SpreadSubsample: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.instance.Resample: Returns the tip text for this property
randomSeedTipText() - Method in class weka.filters.unsupervised.instance.Randomize: Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.trees.ADTree
randomSeedTipText() - Method in class weka.classifiers.functions.LeastMedSq: Returns the tip text for this property
randomSeedTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
randomSeedTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
randomSeedTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
randomSeedTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
RandomSplitResultProducer - class weka.experiment.RandomSplitResultProducer.: Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
RandomSplitResultProducer() - Constructor for class weka.experiment.RandomSplitResultProducer
RandomTree - class weka.classifiers.trees.RandomTree.: Class for constructing a tree that considers K random features at each node.
RandomTree() - Constructor for class weka.classifiers.trees.RandomTree
RandomVariates - class weka.core.RandomVariates.: Class implementing some simple random variates generator.
RandomVariates() - Constructor for class weka.core.RandomVariates: Simply the constructor of super class
RandomVariates(long) - Constructor for class weka.core.RandomVariates: Simply the constructor of super class
randomWidthFactorTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
Range - class weka.core.Range.: Class representing a range of cardinal numbers.
RANGE_BOUNDS - Static variable in class weka.classifiers.meta.ThresholdSelector
RANGE_NONE - Static variable in class weka.classifiers.meta.ThresholdSelector
Range() - Constructor for class weka.core.Range: Default constructor.
Range(String) - Constructor for class weka.core.Range: Constructor to set initial range.
rangeCorrectionTipText() - Method in class weka.classifiers.meta.ThresholdSelector
rankedAttributes() - Method in interface weka.attributeSelection.RankedOutputSearch: Returns a X by 2 list of attribute indexes and corresponding evaluations from best (highest) to worst.
rankedAttributes() - Method in class weka.attributeSelection.ForwardSelection: Produces a ranked list of attributes.
rankedAttributes() - Method in class weka.attributeSelection.AttributeSelection: get the final ranking of the attributes.
rankedAttributes() - Method in class weka.attributeSelection.Ranker: Sorts the evaluated attribute list
rankedAttributes() - Method in class weka.attributeSelection.RaceSearch
RankedOutputSearch - interface weka.attributeSelection.RankedOutputSearch.: Interface for search methods capable of producing a ranked list of attributes.
Ranker - class weka.attributeSelection.Ranker.: Class for ranking the attributes evaluated by a AttributeEvaluator Valid options are:
Ranker() - Constructor for class weka.attributeSelection.Ranker: Constructor
RankSearch - class weka.attributeSelection.RankSearch.: Class for evaluating a attribute ranking (given by a specified evaluator) using a specified subset evaluator.
RankSearch() - Constructor for class weka.attributeSelection.RankSearch
rawOutputTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
RBFKernel - class weka.classifiers.functions.supportVector.RBFKernel.: The RBF kernel.
RBFKernel(Instances, int, double) - Constructor for class weka.classifiers.functions.supportVector.RBFKernel: Constructor.
RBFNetwork - class weka.classifiers.functions.RBFNetwork.: Class that implements a radial basis function network.
RBFNetwork() - Constructor for class weka.classifiers.functions.RBFNetwork
rbind(PaceMatrix) - Method in class weka.classifiers.functions.pace.PaceMatrix: Returns a new matrix which binds two matrices together with rows.
rchisq(int, double, Random) - Static method in class weka.classifiers.functions.pace.Maths: Generates a sample of a Chi-square distribution.
RDG1 - class weka.datagenerators.RDG1.: Class to generate data randomly by producing a decision list.
RDG1() - Constructor for class weka.datagenerators.RDG1
read(BufferedReader) - Static method in class weka.classifiers.functions.pace.Matrix: Read a matrix from a stream.
readBIF(InputStream) - Method in class weka.gui.graphvisualizer.GraphVisualizer: BIF reader
Reads a graph description in XMLBIF03 from an InputStrem
readBIF(String) - Method in class weka.gui.graphvisualizer.GraphVisualizer: BIF reader
Reads a graph description in XMLBIF03 from a string
readDOT(Reader) - Method in class weka.gui.graphvisualizer.GraphVisualizer: Dot reader
Reads a graph description in DOT format from a string
readInstance(Reader) - Method in class weka.core.Instances: Reads a single instance from the reader and appends it to the dataset.
readOldFormat(Reader) - Method in class weka.classifiers.CostMatrix: Loads a cost matrix in the old format from a reader.
readProperties(String) - Static method in class weka.core.Utils: Reads properties that inherit from three locations.
realCount - Variable in class weka.core.AttributeStats: The number of real-like values (i.e.
RECALL_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
recall(int) - Method in class weka.classifiers.Evaluation: Calculate the recall with respect to a particular class.
recall(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate the recall with respect to a particular class.
RECTANGLE - Static variable in class weka.gui.visualize.VisualizePanelEvent
reducedErrorPruningTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
reducedErrorPruningTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
reduceDimensionality(Instance) - Method in class weka.attributeSelection.AttributeSelection: reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection.
reduceDimensionality(Instances) - Method in class weka.attributeSelection.AttributeSelection: reduce the dimensionality of a set of instances to include only those attributes chosen by the last run of attribute selection.
reduceDL(double, boolean) - Method in class weka.classifiers.rules.RuleStats: Try to reduce the DL of the ruleset by testing removing the rules one by one in reverse order and update all the stats
reduceMatrix(double[][]) - Static method in class weka.core.ContingencyTables: Reduces a matrix by deleting all zero rows and columns.
ReferenceInstances - class weka.classifiers.trees.adtree.ReferenceInstances.: Simple class that extends the Instances class making it possible to create subsets of instances that reference their source set.
ReferenceInstances(Instances, int) - Constructor for class weka.classifiers.trees.adtree.ReferenceInstances: Creates an empty set of instances.
refine(ArrayList) - Method in class weka.associations.tertius.Rule: Refine a rule by adding literal from a set of predictes.
refreshFreqTipText() - Method in class weka.gui.beans.StripChart: GUI Tip text
regression(Matrix, double) - Method in class weka.core.Matrix: Performs a (ridged) linear regression.
regression(Matrix, double[], double) - Method in class weka.core.Matrix: Performs a weighted (ridged) linear regression.
RegressionByDiscretization - class weka.classifiers.meta.RegressionByDiscretization.: Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute (equal-width) discretized.
RegressionByDiscretization() - Constructor for class weka.classifiers.meta.RegressionByDiscretization: Default constructor.
RegressionSplitEvaluator - class weka.experiment.RegressionSplitEvaluator.: A SplitEvaluator that produces results for a classification scheme on a numeric class attribute.
RegressionSplitEvaluator() - Constructor for class weka.experiment.RegressionSplitEvaluator: No args constructor.
RELATION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve: The name of the relation used in threshold curve datasets
RELATION_NAME - Static variable in class weka.classifiers.evaluation.CostCurve: The name of the relation used in cost curve datasets
relationName() - Method in class weka.core.Instances: Returns the relation's name.
relativeAbsoluteError() - Method in class weka.classifiers.Evaluation: Returns the relative absolute error.
relativeAbsoluteError() - Method in class evaluationMethods.EstimatorEvaluation: Returns the relative absolute error.
relativeDL(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats: The description length (DL) of the ruleset relative to if the rule in the given position is deleted, which is obtained by:
MDL if the rule exists - MDL if the rule does not exist
Note the minimal possible DL of the ruleset is calculated(i.e.
ReliefFAttributeEval - class weka.attributeSelection.ReliefFAttributeEval.: Class for Evaluating attributes individually using ReliefF.
ReliefFAttributeEval() - Constructor for class weka.attributeSelection.ReliefFAttributeEval: Constructor
RemoteBoundaryVisualizerSubTask - class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask.: Class that encapsulates a sub task for distributed boundary visualization.
RemoteBoundaryVisualizerSubTask() - Constructor for class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask
RemoteEngine - class weka.experiment.RemoteEngine.: A general purpose server for executing Task objects sent via RMI.
RemoteEngine(String) - Constructor for class weka.experiment.RemoteEngine: Constructor
RemoteExperiment - class weka.experiment.RemoteExperiment.: Holds all the necessary configuration information for a distributed experiment.
RemoteExperiment(Experiment) - Constructor for class weka.experiment.RemoteExperiment: Construct a new RemoteExperiment using a base Experiment
RemoteExperimentEvent - class weka.experiment.RemoteExperimentEvent.: Class encapsulating information on progress of a remote experiment
RemoteExperimentEvent(boolean, boolean, boolean, String) - Constructor for class weka.experiment.RemoteExperimentEvent: Constructor
RemoteExperimentListener - interface weka.experiment.RemoteExperimentListener.: Interface for classes that want to listen for updates on RemoteExperiment progress
remoteExperimentStatus(RemoteExperimentEvent) - Method in interface weka.experiment.RemoteExperimentListener: Called when progress has been made in a remote experiment
RemoteExperimentSubTask - class weka.experiment.RemoteExperimentSubTask.: Class to encapsulate an experiment as a task that can be executed on a remote host.
RemoteExperimentSubTask() - Constructor for class weka.experiment.RemoteExperimentSubTask
RemoteResult - class weka.gui.boundaryvisualizer.RemoteResult.: Class that encapsulates a result (and progress info) for part of a distributed boundary visualization.
RemoteResult(int, int) - Constructor for class weka.gui.boundaryvisualizer.RemoteResult: Creates a new RemoteResult instance.
Remove - class weka.filters.unsupervised.attribute.Remove.: An instance filter that deletes a range of attributes from the dataset.
REMOVE_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
remove() - Method in class weka.gui.beans.BeanConnection: Remove this connection
remove() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
remove() - Method in class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
Remove() - Constructor for class weka.filters.unsupervised.attribute.Remove: Constructor so that we can initialize the Range variable properly.
remove(Object) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
removeActionListener(ActionListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Remove a listener
removeAllBeansFromContainer(JComponent) - Static method in class weka.gui.beans.BeanInstance: Removes all beans from containing component
removeAllElements() - Method in class weka.core.FastVector: Removes all components from this vector and sets its size to zero.
removeAllElements() - Method in class weka.core.Queue: Removes all objects from the queue.
removeAllInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: This function will remove all the inputs to this unit.
removeAllInputs() - Method in class weka.classifiers.functions.neural.NeuralNode: This function will remove all the inputs to this unit.
removeAllMissingColsTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
removeAllOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection: This function will remove all outputs to this unit.
removeAllPlots() - Method in class weka.gui.visualize.Plot2D: Clears all plots
removeBatchClassifierListener(BatchClassifierListener) - Method in class weka.gui.beans.Classifier: Remove a batch classifier listener
removeBean(JComponent) - Method in class weka.gui.beans.BeanInstance: Remove this bean from the list of beans and from the containing component
removeCancelListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to remove an action listener from the cancel button
removeChartListener(ChartListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Remove a chart listener
removeConnections(BeanInstance) - Static method in class weka.gui.beans.BeanConnection: Remove all connections for a bean.
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Loader: Remove a listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Filter: Remove a data source listener
removeDataSourceListener(DataSourceListener) - Method in interface weka.gui.beans.DataSource: Remove a data source listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.PredictionAppender: Remove a datasource listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassAssigner
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.AbstractDataSource: Remove a listener
removeElementAt(int) - Method in class weka.core.FastVector: Deletes an element from this vector.
removeFirst() - Method in class weka.associations.tertius.SimpleLinkedList
RemoveFolds - class weka.filters.unsupervised.instance.RemoveFolds.: This filter takes a dataset and outputs a specified fold for cross validation.
RemoveFolds() - Constructor for class weka.filters.unsupervised.instance.RemoveFolds
removeGraphListener(GraphListener) - Method in class weka.gui.beans.Classifier: Remove a graph listener
removeIncrementalClassifierListener(IncrementalClassifierListener) - Method in class weka.gui.beans.Classifier: Remove an incremental classifier listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.Loader: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.Filter: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in interface weka.gui.beans.DataSource: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.PredictionAppender: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.ClassAssigner
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.AbstractDataSource: Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceJoiner
removeInstanceListener(InstanceListener) - Method in class weka.gui.streams.InstanceLoader
removeInstanceListener(InstanceListener) - Method in interface weka.gui.streams.InstanceProducer
removeLast() - Method in class weka.classifiers.rules.RuleStats: Remove the last rule in the ruleset as well as it's stats.
removeLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Method to remove a LayoutCompleteEventListener.
removeLayoutCompleteEventListener(LayoutCompleteEventListener) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method removes a LayoutCompleteEventListener from the LayoutEngine.
removeLinkAt(int) - Method in class weka.attributeSelection.BestFirst.LinkedList2: removes an element (Link) at a specific index from the list.
removeLinkAt(int) - Method in class weka.classifiers.rules.DecisionTable.LinkedList: Removes an element (Link) at a specific index from the list.
RemoveMisclassified - class weka.filters.unsupervised.instance.RemoveMisclassified.: A filter that removes instances which are incorrectly classified.
RemoveMisclassified() - Constructor for class weka.filters.unsupervised.instance.RemoveMisclassified
removeNotify() - Method in class weka.gui.PropertyPanel: Cleans up when the panel is destroyed.
removeOkListener(ActionListener) - Method in class weka.gui.GenericObjectEditor.GOEPanel: This is used to remove an action listener from the ok button
RemovePercentage - class weka.filters.unsupervised.instance.RemovePercentage.: This filter removes a given percentage of a dataset.
RemovePercentage() - Constructor for class weka.filters.unsupervised.instance.RemovePercentage
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericObjectEditor: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.PropertySheetPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SetInstancesPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericArrayEditor: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.CostMatrixEditor: Removes an object from the list of those that wish to be informed when the cost matrix changes.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.PredictionAppenderCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.BeanVisual: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.FilterCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassifierCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.StripChartCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassAssignerCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.LoaderCustomizer: Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SetupPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SimpleSetupPanel: Removes a PropertyChangeListener.
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.TextViewer: Remove a property change listener from this bean
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.AbstractDataSource: Remove a property change listener from this bean
removePropertyChangeListener(String, PropertyChangeListener) - Method in class weka.gui.beans.DataVisualizer: Remove a property change listener from this bean
RemoveRange - class weka.filters.unsupervised.instance.RemoveRange.: This filter takes a dataset and removes a subset of it.
RemoveRange() - Constructor for class weka.filters.unsupervised.instance.RemoveRange
removeResult(String) - Method in class weka.gui.ResultHistoryPanel: Removes one of the result buffers from the history.
removeSubstring(String, String) - Static method in class weka.core.Utils: Removes all occurrences of a string from another string.
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.Filter: Remove a test set listener
removeTestSetListener(TestSetListener) - Method in interface weka.gui.beans.TestSetProducer: Remove a listener for test set events
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.ClassAssigner
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Remove a test set listener
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTestSetProducer: Remove a listener for test sets
removeTextListener(TextListener) - Method in class weka.gui.beans.Classifier: Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Remove a text listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.Filter: Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.ClassAssigner
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in interface weka.gui.beans.TrainingSetProducer: Remove a training set listener
RemoveType - class weka.filters.unsupervised.attribute.RemoveType.: A filter that removes attributes of a given type.
RemoveType() - Constructor for class weka.filters.unsupervised.attribute.RemoveType
RemoveUseless - class weka.filters.unsupervised.attribute.RemoveUseless.: This filter removes attributes that do not vary at all or that vary too much.
RemoveUseless() - Constructor for class weka.filters.unsupervised.attribute.RemoveUseless
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.TextViewer: Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.AbstractDataSource: Remove a vetoable change listener from this bean
removeVetoableChangeListener(String, VetoableChangeListener) - Method in class weka.gui.beans.DataVisualizer: Remove a vetoable change listener from this bean
RemoveWithValues - class weka.filters.unsupervised.instance.RemoveWithValues.: Filters instances according to the value of an attribute.
RemoveWithValues() - Constructor for class weka.filters.unsupervised.instance.RemoveWithValues: Default constructor
renameAttribute(Attribute, String) - Method in class weka.core.Instances: Renames an attribute.
renameAttribute(int, String) - Method in class weka.core.Instances: Renames an attribute.
renameAttributeValue(Attribute, String, String) - Method in class weka.core.Instances: Renames the value of a nominal (or string) attribute value.
renameAttributeValue(int, int, String) - Method in class weka.core.Instances: Renames the value of a nominal (or string) attribute value.
repeatLiteralsTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
ReplaceMissingValues - class weka.filters.unsupervised.attribute.ReplaceMissingValues.: Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
ReplaceMissingValues() - Constructor for class weka.filters.unsupervised.attribute.ReplaceMissingValues
replaceMissingValues(double[]) - Method in class weka.core.Instance: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class weka.core.BinarySparseInstance: Does nothing, since we don't support missing values.
replaceMissingValues(double[]) - Method in class weka.core.SparseInstance: Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValuesTipText() - Method in class weka.filters.unsupervised.attribute.RandomProjection: Returns the tip text for this property
replaceSubstring(String, String, String) - Static method in class weka.core.Utils: Replaces with a new string, all occurrences of a string from another string.
replot() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Quickly replot the display using cached probability estimates
reportFrequencyTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
REPTree - class weka.classifiers.trees.REPTree.: Fast decision tree learner.
REPTree() - Constructor for class weka.classifiers.trees.REPTree
Resample - class weka.filters.supervised.instance.Resample.: Produces a random subsample of a dataset.
Resample - class weka.filters.unsupervised.instance.Resample.: Produces a random subsample of a dataset.
Resample() - Constructor for class weka.filters.supervised.instance.Resample
Resample() - Constructor for class weka.filters.unsupervised.instance.Resample
resample(Random) - Method in class weka.core.Instances: Creates a new dataset of the same size using random sampling with replacement.
resampleWithWeights(Instances, Random, boolean[]) - Method in class weka.classifiers.meta.Bagging: Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
resampleWithWeights(Random) - Method in class weka.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the current instance weights.
resampleWithWeights(Random, double[]) - Method in class weka.core.Instances: Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
reset() - Static method in class weka.gui.beans.BeanConnection: Reset the list of connections
reset() - Method in class weka.core.converters.CSVLoader: Resets the loader ready to read a new data set
reset() - Method in class weka.core.converters.C45Loader: Resets the Loader ready to read a new data set
reset() - Method in class weka.core.converters.SerializedInstancesLoader: Resets the Loader ready to read a new data set
reset() - Method in class weka.core.converters.ArffLoader: Resets the Loader ready to read a new data set
reset() - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to reset the unit for another run.
reset() - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to reset the value and error for this unit, ready for the next run.
reset(Instances) - Method in class coreComponents.EuclideanDistanceMetric: This function is useful if we need to reinitialise the distance metric without using the constructor
reset(Instances) - Method in class coreComponents.DistanceMetric: This function is useful if we need to reinitialise the distance metric without using the constructor
reset(Instances) - Method in class classifiers.vdm.ValueDifferenceMetric: This function is useful if we need to reinitialise the distance metric without using the constructor
reset(Instances) - Method in class classifiers.usm.distance.USMWavDistance: Can we put constructor stuff in here?
reset(JComponent) - Static method in class weka.gui.beans.BeanInstance: Reset the list of beans
resetAttIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Resets the boolean value in AttIndexes array
resetAttIndexTo(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR.Indexes: Resets the boolean value in AttIndexes array based on another set of Indexes
resetDatasetBasedOn(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR.Indexes: Resets the boolean values in Attribute and Instance array to reflect an empty dataset withthe same attributes set as in the incoming Indexes Object
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.C45Split: Sets distribution associated with model.
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Sets distribution associated with model.
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Sets distribution associated with model.
resetInstanceIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Resets the boolean value in the Instance Indexes array to a specified value
resetOptions() - Method in class weka.associations.Apriori: Resets the options to the default values.
resetOptions() - Method in class weka.associations.Tertius: Resets the options to the default values.
resetTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
ResidualModelSelection - class weka.classifiers.trees.lmt.ResidualModelSelection.: Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals.
ResidualModelSelection(int) - Constructor for class weka.classifiers.trees.lmt.ResidualModelSelection: Constructor to create ResidualModelSelection object.
ResidualSplit - class weka.classifiers.trees.lmt.ResidualSplit.: Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals of the LogitBoost algorithm.
ResidualSplit(int) - Constructor for class weka.classifiers.trees.lmt.ResidualSplit: Creates a split object
ResultHistoryPanel - class weka.gui.ResultHistoryPanel.: A component that accepts named stringbuffers and displays the name in a list box.
ResultHistoryPanel.RKeyAdapter - class weka.gui.ResultHistoryPanel.RKeyAdapter.: Extension of KeyAdapter that implements Serializable.
ResultHistoryPanel.RKeyAdapter() - Constructor for class weka.gui.ResultHistoryPanel.RKeyAdapter
ResultHistoryPanel.RMouseAdapter - class weka.gui.ResultHistoryPanel.RMouseAdapter.: Extension of MouseAdapter that implements Serializable.
ResultHistoryPanel.RMouseAdapter() - Constructor for class weka.gui.ResultHistoryPanel.RMouseAdapter
ResultHistoryPanel(JTextComponent) - Constructor for class weka.gui.ResultHistoryPanel: Create the result history object
ResultListener - interface weka.experiment.ResultListener.: Interface for objects able to listen for results obtained by a ResultProducer
ResultProducer - interface weka.experiment.ResultProducer.: This interface defines the methods required for an object that produces results for different randomizations of a dataset.
resultProducerTipText() - Method in class weka.experiment.AveragingResultProducer: Returns the tip text for this property
resultProducerTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
resultProducerTipText() - Method in class weka.experiment.DatabaseResultProducer: Returns the tip text for this property
resultsetKey() - Method in class weka.experiment.PairedTTester: Creates a key that maps resultset numbers to their descriptions.
ResultsPanel - class weka.gui.experiment.ResultsPanel.: This panel controls simple analysis of experimental results.
ResultsPanel() - Constructor for class weka.gui.experiment.ResultsPanel: Creates the results panel with no initial experiment.
retrieveInstances() - Method in class weka.experiment.InstanceQuery: Makes a database query using the query set through the -Q option to convert a table into a set of instances
retrieveInstances(String) - Method in class weka.experiment.InstanceQuery: Makes a database query to convert a table into a set of instances
returnAvPredWithPattern(int) - Method in class coreComponents.PatternCounter
returnBoundsFromString(int, int) - Method in class coreComponents.PatternCounter
returnDistribution(Matrix) - Static method in class probabilityMachine.VennProbabilityClassifier: Converts the Venn Probability Matrix into a distribution for each class
returnInstanceCache(int) - Method in class classifiers.usm.distance.USMDistanceFunction: Simple function that returns the instance cache at a specified index
returnLeaves(FastVector[]) - Method in class weka.classifiers.trees.m5.RuleNode: Return a list containing all the leaves in the tree
returnLowBoundForPatternPos(int, int) - Method in class coreComponents.PatternCounter
returnNumWithPattern(int) - Method in class coreComponents.PatternCounter
returnNumWithPatternPerClass(int) - Method in class coreComponents.PatternCounter
returnRegionPrediction(double[], double) - Static method in class confidenceMachine.ConfidenceClassifier: Returns a region prediction (subset of possible classes) that are valid at a given significance level.
returnTheSizeOfCompressedConcatWav(String, String) - Method in class classifiers.usm.distance.USMWavDistance: Returns the compressed concatenated wav file size in bytes
returnTheSizeOfCompressedWav(String) - Method in class classifiers.usm.distance.USMWavDistance: Returns the compressed wav file size in bytes
returnUpBoundForPatternPos(int, int) - Method in class coreComponents.PatternCounter
returnUpperAndLowerProbability(Matrix) - Static method in class probabilityMachine.VennProbabilityClassifier: Calculates the upper and lower probabilities for the predicted label (a bit like a region prediction)
rev() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the reverse vector
REVERSED - Static variable in interface weka.gui.graphvisualizer.GraphConstants: Types of Edges
rhoaTipText() - Method in class weka.classifiers.misc.FLR: Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.functions.LinearRegression: Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.functions.Logistic: Returns the tip text for this property
ridgeTipText() - Method in class weka.classifiers.functions.RBFNetwork: Returns the tip text for this property
Ridor - class weka.classifiers.rules.Ridor.: The implementation of a RIpple-DOwn Rule learner.
Ridor() - Constructor for class weka.classifiers.rules.Ridor
rightNode() - Method in class weka.classifiers.trees.m5.RuleNode: Get the right child of this node
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split: Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Prints left side of condition satisfied by instances in subset index.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit: Does nothing because no condition has to be satisfied.
rightSide(int, Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Prints the condition satisfied by instances in a subset.
rmCoveredBySuccessives(Instances, FastVector, int) - Static method in class weka.classifiers.rules.RuleStats: Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them.
rnorm(int, double, double, Random) - Static method in class weka.classifiers.functions.pace.Maths: Generates a sample of a normal distribution.
rocAnalysisTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
rocToString() - Method in class weka.associations.tertius.Rule: Return a String giving the TP-rate and FP-rate of this rule.
ROOT_FINDER_ACCURACY - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
ROOT_FINDER_MAX_ITER - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants: How close the root finder for numeric and nominal have to get
rootMeanPriorSquaredError() - Method in class weka.classifiers.Evaluation: Returns the root mean prior squared error.
rootMeanPriorSquaredError() - Method in class evaluationMethods.EstimatorEvaluation: Returns the root mean prior squared error.
rootMeanSquaredError() - Method in class weka.classifiers.Evaluation: Returns the root mean squared error.
rootMeanSquaredError() - Method in class evaluationMethods.EstimatorEvaluation: Returns the root mean squared error.
rootRelativeSquaredError() - Method in class weka.classifiers.Evaluation: Returns the root relative squared error if the class is numeric.
rootRelativeSquaredError() - Method in class evaluationMethods.EstimatorEvaluation: Returns the root relative squared error if the class is numeric.
round(double) - Static method in class weka.core.Utils: Rounds a double to the next nearest integer value.
roundDouble(double, int) - Static method in class weka.core.Utils: Rounds a double to the given number of decimal places.
rsolve(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Solves upper-triangular equation R x = b
Rule - class weka.associations.tertius.Rule.: Class representing a rule with a body and a head.
Rule - class weka.classifiers.rules.Rule.: Abstract class of generic rule
Rule - class weka.classifiers.trees.m5.Rule.: Generates a single m5 tree or rule
Rule() - Constructor for class weka.classifiers.rules.Rule
Rule() - Constructor for class weka.classifiers.trees.m5.Rule: Constructor declaration
Rule(boolean, int, boolean, boolean, boolean, boolean) - Constructor for class weka.associations.tertius.Rule: Constructor for a rule when the counter-instances are not stored, giving all the constraints applied to this rule.
Rule(Instances, boolean, int, boolean, boolean, boolean, boolean) - Constructor for class weka.associations.tertius.Rule: Constructor for a rule when the counter-instances are stored, giving all the constraints applied to this rule.
RuleNode - class weka.classifiers.trees.m5.RuleNode.: Constructs a node for use in an m5 tree or rule
RuleNode(double, double, RuleNode) - Constructor for class weka.classifiers.trees.m5.RuleNode: Creates a new RuleNode instance.
RuleStats - class weka.classifiers.rules.RuleStats.: This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc.
RuleStats() - Constructor for class weka.classifiers.rules.RuleStats: Default constructor
RuleStats(Instances, FastVector) - Constructor for class weka.classifiers.rules.RuleStats: Constructor that provides ruleset and data
RUN_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
RUN_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
run() - Method in class weka.associations.Tertius: Run the search.
runCommand(String) - Method in class weka.gui.SimpleCLI: Executes a simple cli command.
runExperiment() - Method in class weka.experiment.Experiment: Runs all iterations of the experiment, continuing past errors.
runExperiment() - Method in class weka.experiment.RemoteExperiment: Overides runExperiment in Experiment
RunNumberPanel - class weka.gui.experiment.RunNumberPanel.: This panel controls configuration of lower and upper run numbers in an experiment.
RunNumberPanel() - Constructor for class weka.gui.experiment.RunNumberPanel: Creates the panel with no initial experiment.
RunNumberPanel(Experiment) - Constructor for class weka.gui.experiment.RunNumberPanel: Creates the panel with the supplied initial experiment.
RunPanel - class weka.gui.experiment.RunPanel.: This panel controls the running of an experiment.
RunPanel() - Constructor for class weka.gui.experiment.RunPanel: Creates the run panel with no initial experiment.
RunPanel(Experiment) - Constructor for class weka.gui.experiment.RunPanel: Creates the panel with the supplied initial experiment.

S

sameClauseAs(Rule) - Method in class weka.associations.tertius.Rule: Test if this rule and another rule correspond to the same clause.
sameClauseTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
samePattern(Instance) - Method in class coreComponents.PatternCounter.PatternObject
sampeSizePercentTipText() - Method in class weka.filters.supervised.instance.Resample: Returns the tip text for this property
sampleSizePercentTipText() - Method in class weka.filters.unsupervised.instance.Resample: Returns the tip text for this property
sampleSizeTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
sampleSizeTipText() - Method in class weka.classifiers.functions.LeastMedSq: Returns the tip text for this property
satisfies(Instance) - Method in class weka.associations.tertius.Literal
satisfies(Instance) - Method in class weka.associations.tertius.AttributeValueLiteral
save(StringBuffer) - Method in class weka.gui.SaveBuffer: Save a buffer
SaveBuffer - class weka.gui.SaveBuffer.: This class handles the saving of StringBuffers to files.
SaveBuffer(Logger, Component) - Constructor for class weka.gui.SaveBuffer: Constructor
saveInstanceDataTipText() - Method in class weka.clusterers.Cobweb: Returns the tip text for this property
saveInstanceDataTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
saveInstanceDataTipText() - Method in class weka.classifiers.trees.ADTree
saveWorkingInstancesToFileQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a file to save instances as, then saves the instances in a background process.
ScatterPlotMatrix - class weka.gui.beans.ScatterPlotMatrix.: Bean that encapsulates weka.gui.visualize.MatrixPanel for displaying a scatter plot matrix.
ScatterPlotMatrix() - Constructor for class weka.gui.beans.ScatterPlotMatrix
ScatterPlotMatrixBeanInfo - class weka.gui.beans.ScatterPlotMatrixBeanInfo.: Bean info class for the scatter plot matrix bean
ScatterPlotMatrixBeanInfo() - Constructor for class weka.gui.beans.ScatterPlotMatrixBeanInfo
Scoreable - interface weka.classifiers.bayes.Scoreable.: Interface for allowing to score a classifier
scoreTypeTipText() - Method in class weka.classifiers.bayes.BayesNet
search() - Method in class weka.associations.Tertius: Search in the space of hypotheses the rules that have the highest confirmation.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ExhaustiveSearch: Searches the attribute subset space using an exhaustive search.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ASSearch: Searches the attribute subset/ranking space.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ForwardSelection: Searches the attribute subset space by forward selection.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RankSearch: Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.BestFirst: Searches the attribute subset space by best first search
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.GeneticSearch: Searches the attribute subset space using a genetic algorithm.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.Ranker: Kind of a dummy search algorithm.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RaceSearch: Searches the attribute subset space by racing cross validation errors of competing subsets
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RandomSearch: Searches the attribute subset space randomly.
search(Vector, String) - Method in class weka.gui.HierarchyPropertyParser: Helper function to search for the given target string in a given vector in which the elements' value may hopefully is equal to the target.
SEARCHPATH_ALL - Static variable in class weka.classifiers.trees.ADTree: The search modes
SEARCHPATH_HEAVIEST - Static variable in class weka.classifiers.trees.ADTree
SEARCHPATH_RANDOM - Static variable in class weka.classifiers.trees.ADTree
SEARCHPATH_ZPURE - Static variable in class weka.classifiers.trees.ADTree
searchPathTipText() - Method in class weka.classifiers.trees.ADTree
searchPercentTipText() - Method in class weka.attributeSelection.RandomSearch: Returns the tip text for this property
searchPoints(int, int, boolean) - Method in class weka.gui.visualize.Plot2D: Pops up a window displaying attribute information on any instances at a point+-plotting_point_size (in panel coordinates)
searchTerminationTipText() - Method in class weka.attributeSelection.BestFirst: Returns the tip text for this property
searchTipText() - Method in class weka.filters.supervised.attribute.AttributeSelection: Returns the tip text for this property
searchTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Returns the tip text for this property
secondInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
secondInstanceProduced(InstanceEvent) - Method in interface weka.gui.streams.SerialInstanceListener
secondValueIndexTipText() - Method in class weka.filters.unsupervised.attribute.SwapValues
secondValueTipText() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
seedTipText() - Method in class weka.gui.beans.TrainTestSplitMaker: Tip text for this property
seedTipText() - Method in class weka.gui.beans.CrossValidationFoldMaker: Tip text for this property
seedTipText() - Method in class weka.clusterers.SimpleKMeans: Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.EM: Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.FarthestFirst: Returns the tip text for this property
seedTipText() - Method in class weka.filters.supervised.attribute.ClassOrder: Returns the tip text for this property
seedTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Returns the tip text for this property
seedTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.OneRAttributeEval: Returns a string for this option suitable for display in the gui as a tip text
seedTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
seedTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.RandomizableClassifier: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
seedTipText() - Method in class weka.classifiers.meta.MultiScheme: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.meta.ThresholdSelector
seedTipText() - Method in class weka.classifiers.meta.Decorate: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
seedTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.ConjunctiveRule: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.RandomForest: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.REPTree: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.trees.RandomTree: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.functions.VotedPerceptron: Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
SelectAttributes(ASEvaluation, String[]) - Static method in class weka.attributeSelection.AttributeSelection: Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.
SelectAttributes(ASEvaluation, String[], Instances) - Static method in class weka.attributeSelection.AttributeSelection: Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
SelectAttributes(Instances) - Method in class weka.attributeSelection.AttributeSelection: Perform attribute selection on the supplied training instances.
selectAttributesCVSplit(Instances) - Method in class weka.attributeSelection.AttributeSelection: Select attributes for a split of the data.
selectedAttributes() - Method in class weka.attributeSelection.AttributeSelection: get the final selected set of attributes.
SelectedTag - class weka.core.SelectedTag.: Represents a selected value from a finite set of values, where each value is a Tag (i.e.
SelectedTag(int, Tag[]) - Constructor for class weka.core.SelectedTag: Creates a new SelectedTag instance.
SelectedTagEditor - class weka.gui.SelectedTagEditor.: A PropertyEditor that uses tags, where the tags are obtained from a weka.core.SelectedTag object.
SelectedTagEditor() - Constructor for class weka.gui.SelectedTagEditor
SELECTION_GREEDY - Static variable in class weka.classifiers.functions.LinearRegression
SELECTION_M5 - Static variable in class weka.classifiers.functions.LinearRegression
SELECTION_NONE - Static variable in class weka.classifiers.functions.LinearRegression
selectionThresholdTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
selectModel(Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.ModelSelection: Selects a model for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Method not in use
selectModel(Instances, double[][], double[][]) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Selects split based on residuals for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection: Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.ModelSelection: Selects a model for the given train data using the given test data
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.lmt.ResidualModelSelection: Method not in use
SEND_INSTANCES - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent: Command to remove instances from this node and send them to the VisualizePanel.
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.NormalMixture: Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
seq(int, int) - Static method in class weka.classifiers.functions.pace.IntVector: Generates an IntVector that stores all integers inclusively between two integers.
SerialInstanceListener - interface weka.gui.streams.SerialInstanceListener.: Defines an interface for objects able to produce two output streams of instances.
SerializedInstancesLoader - class weka.core.converters.SerializedInstancesLoader.: Reads a source that contains serialized Instances.
SerializedInstancesLoader() - Constructor for class weka.core.converters.SerializedInstancesLoader
SerializedObject - class weka.core.SerializedObject.: Class for storing an object in serialized form in memory.
SerializedObject(Object) - Constructor for class weka.core.SerializedObject: Creates a new serialized object (without compression).
SerializedObject(Object, boolean) - Constructor for class weka.core.SerializedObject: Creates a new serialized object.
set(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Set all elements to a value
set(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Set the elements using a DoubleVector
set(int) - Method in class weka.classifiers.functions.pace.IntVector: Sets the value of an element.
set(int, double) - Method in class weka.classifiers.functions.pace.DoubleVector: Set a single element.
set(int, int) - Method in class weka.classifiers.functions.pace.IntVector: Sets a single element.
set(int, int, double) - Method in class weka.classifiers.functions.pace.DoubleVector: Set some elements to a value
set(int, int, double) - Method in class weka.classifiers.functions.pace.Matrix: Set a single element.
set(int, int, double[], int) - Method in class weka.classifiers.functions.pace.DoubleVector: Set some elements using a 2-D array
set(int, int, DoubleVector, int) - Method in class weka.classifiers.functions.pace.DoubleVector: Set some elements using a DoubleVector.
set(int, int, int[], int) - Method in class weka.classifiers.functions.pace.IntVector: Sets the values of elements from an int array.
set(int, int, IntVector, int) - Method in class weka.classifiers.functions.pace.IntVector: Sets the values of elements from another IntVector.
set(IntVector) - Method in class weka.classifiers.functions.pace.IntVector: Sets the values of elements from another IntVector.
setAcuity(double) - Method in class weka.clusterers.Cobweb: set the acuity.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.ResultProducer: Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.SplitEvaluator: Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.AveragingResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator: Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in class weka.experiment.LearningRateResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RegressionSplitEvaluator: Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in class weka.experiment.CrossValidationResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RandomSplitResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DatabaseResultProducer: Set a list of method names for additional measures to look for in SplitEvaluators.
setAdjustWeights(boolean) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets whether the instance weights will be adjusted to maintain total weight per class.
setAdvanceDataSetFirst(boolean) - Method in class weka.experiment.Experiment: Set the value of m_AdvanceDataSetFirst.
setAlpha(double) - Method in class weka.classifiers.bayes.BayesNet: Method declaration
setAlpha(double) - Method in class weka.classifiers.functions.Winnow: Set the value of Alpha.
setAnimated() - Method in class weka.gui.beans.BeanVisual: Set the animated version of the icon
setAppendPredictedProbabilities(boolean) - Method in class weka.gui.beans.PredictionAppender: Set whether to append predicted probabilities rather than class value (for discrete class data sets)
setArffFile(String) - Method in class weka.gui.streams.InstanceLoader
setArffFile(String) - Method in class weka.gui.streams.InstanceSavePanel
setArtificialSize(double) - Method in class weka.classifiers.meta.Decorate: Sets factor that determines number of artificial examples to generate.
setAsText(String) - Method in class weka.gui.SelectedTagEditor: Sets the current property value as text.
setAsText(String) - Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting/setting values as text.
setAsText(String) - Method in class weka.gui.GenericArrayEditor: Returns null as we don't support getting/setting values as text.
setAsText(String) - Method in class weka.gui.CostMatrixEditor: Some objects can be represented as text, but a cost matrix cannot.
setAsTrueGaussian(int) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Debugging class that sets the Gaussian to its true class
setAttIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Changes the boolean value at the specified index in the AttIndexes array
setAttList_Irr(boolean[]) - Method in class weka.datagenerators.RDG1: Sets the array that defines which of the attributes are seen to be irrelevant.
setAttribute(int) - Method in class weka.gui.AttributeVisualizationPanel: Tells the panel which attribute to visualize.
setAttribute(int) - Method in class weka.gui.AttributeSummaryPanel: Sets the attribute that statistics will be displayed for.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RankSearch: Set the attribute evaluator to use for generating the ranking.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RaceSearch: Set the attribute evaluator to use for generating the ranking.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.StringToNominal: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.Add: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets index of the attribute used.
setAttributeIndex(String) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Sets index of the attribute used.
setAttributeIndices(String) - Method in class weka.filters.supervised.attribute.Discretize: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Remove: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Copy: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Set which attributes are to be transformed (or kept if invert is true).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]) - Method in class weka.filters.supervised.attribute.Discretize: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Remove: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Copy: Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Set which attributes are to be transformed (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeName(String) - Method in class weka.filters.unsupervised.attribute.Add: Set the new attribute's name
setAttributeNamePrefix(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Set the attribute name prefix.
setAttributeSelectionMethod(SelectedTag) - Method in class weka.classifiers.functions.LinearRegression: Sets the method used to select attributes for use in the linear regression.
setAttributeType(SelectedTag) - Method in class weka.filters.unsupervised.attribute.RemoveType: Sets the attribute type to be deleted by the filter.
setAtts(int[], boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Changes the boolean value at the specified index in the InstIndexes array
setAttsToEliminatePerIteration(int) - Method in class weka.attributeSelection.SVMAttributeEval: Set the constant rate of attribute elimination per iteration
setAutoBuild(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron: This will set whether the network is automatically built or if it is left up to the user.
setBagSizePercent(int) - Method in class weka.classifiers.meta.Bagging: Sets the size of each bag, as a percentage of the training set size.
setBagSizePercent(int) - Method in class weka.classifiers.meta.MetaCost: Sets the size of each bag, as a percentage of the training set size.
setBalanced(boolean) - Method in class weka.classifiers.functions.Winnow: Set the value of Balanced.
setBaseExperiment(Experiment) - Method in class weka.experiment.RemoteExperiment: Set the base experiment.
setBeanContext(BeanContext) - Method in class weka.gui.beans.AttributeSummarizer: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.Loader: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.TextViewer: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.AbstractDataSource: Set a bean context for this bean
setBeanContext(BeanContext) - Method in class weka.gui.beans.DataVisualizer: Set a bean context for this bean
setBeanInstances(Vector, JComponent) - Static method in class weka.gui.beans.BeanInstance: Describe setBeanInstances method here.
setBeta(double) - Method in class weka.classifiers.functions.Winnow: Set the value of Beta.
setBetterMDL(boolean) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Set better MDL to be used.
setBias(double) - Method in class weka.classifiers.misc.VFI: Set the value of the exponential bias towards more confident intervals
setBiasToUniformClass(double) - Method in class weka.filters.supervised.instance.Resample: Sets the bias towards a uniform class.
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval: Binarize numeric attributes.
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Binarize numeric attributes.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.supervised.attribute.NominalToBinary: Sets if binary attributes are to be treates as nominal ones.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Sets if binary attributes are to be treates as nominal ones.
setBinarySplits(boolean) - Method in class weka.classifiers.rules.PART: Set the value of binarySplits.
setBinarySplits(boolean) - Method in class weka.classifiers.trees.J48: Set the value of binarySplits.
setBins(int) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Ignored
setBins(int) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets the number of bins to divide each selected numeric attribute into
setBlendFactor(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Set the blending factor
setBlendMethod(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Set the blending method
setBounds(Instances) - Method in class weka.classifiers.misc.FLR: Sets the metric space from the training set using the min-max stats, in case -B option is not used.
setBoundsFile(String) - Method in class weka.classifiers.misc.FLR: Set Boundaries File
setBuildLogisticModels(boolean) - Method in class weka.classifiers.functions.SMO: Set the value of buildLogisticModels.
setBuildLogisticModels(boolean) - Method in class classifiers.PC_SMO: Set the value of buildLogisticModels.
setBuildLogisticModels(boolean) - Method in class classifiers.AlphaProb_SMO: Set the value of buildLogisticModels.
setBuildRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.M5Base: Set the value of regressionTree.
setC(double) - Method in class weka.classifiers.functions.SMO: Set the value of C.
setC(double) - Method in class weka.classifiers.functions.SMOreg: Set the value of C.
setC(double) - Method in class classifiers.PC_SMO: Set the value of C.
setC(double) - Method in class classifiers.AlphaProb_SMO: Set the value of C.
setCacheKeyName(String) - Method in class weka.experiment.DatabaseResultListener: Set the value of CacheKeyName.
setCacheSize(int) - Method in class weka.classifiers.functions.SMO: Set the value of the kernel cache.
setCacheSize(int) - Method in class weka.classifiers.functions.SMOreg: Set the value of the kernel cache.
setCacheSize(int) - Method in class classifiers.PC_SMO: Set the value of the kernel cache.
setCacheSize(int) - Method in class classifiers.AlphaProb_SMO: Set the value of the kernel cache.
setCalcOutOfBag(boolean) - Method in class weka.classifiers.meta.Bagging: Set whether the out of bag error is calculated.
setCalculateStdDevs(boolean) - Method in class weka.experiment.AveragingResultProducer: Set the value of CalculateStdDevs.
setCapacity(int) - Method in class weka.core.FastVector: Sets the vector's capacity to the given value.
setCapacity(int) - Method in class weka.classifiers.functions.pace.DoubleVector: Sets the capacity of the vector
setCapacity(int) - Method in class weka.classifiers.functions.pace.IntVector: Sets the capacity of the vector
setCenter(double) - Method in class weka.gui.treevisualizer.Node: Set the value of center.
setCheckErrorRate(boolean) - Method in class weka.classifiers.rules.JRip
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.Splitter: Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit: Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit: Sets the child for a branch of the split.
setCindex(int) - Method in class weka.gui.visualize.ClassPanel: Set the index of the attribute to display coloured labels for
setCindex(int) - Method in class weka.gui.visualize.Plot2D: Set the index of the attribute to use for colouring
setCindex(int) - Method in class weka.gui.visualize.PlotData2D: Set the colouring index of the data
setCindex(int) - Method in class weka.gui.visualize.AttributePanel: Set the index of the attribute by which to colour the data.
setCindex(int, double, double) - Method in class weka.gui.visualize.AttributePanel: Set the index of the attribute by which to colour the data.
setClass(Attribute) - Method in class weka.core.Instances: Sets the class attribute.
setClassColumn(String) - Method in class weka.gui.beans.ClassAssigner
setClassFlag(boolean) - Method in class weka.datagenerators.ClusterGenerator: Sets the class flag, if class flag is set, the cluster is listed as class atrribute in an extra attribute.
setClassForIRStatistics(int) - Method in class weka.experiment.ClassifierSplitEvaluator: Set the value of ClassForIRStatistics.
setClassification(boolean) - Method in class weka.associations.Tertius: Set the value of classification.
setClassifier(Classifier) - Method in class weka.gui.beans.Classifier: Set the classifier for this wrapper
setClassifier(Classifier) - Method in class weka.gui.beans.IncrementalClassifierEvent
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Set a classifier to use
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the classifier to use.
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the classifier to use
setClassifier(Classifier) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the classifier to classify instances with.
setClassifier(Classifier) - Method in class weka.experiment.ClassifierSplitEvaluator: Sets the classifier.
setClassifier(Classifier) - Method in class weka.experiment.RegressionSplitEvaluator: Sets the classifier.
setClassifier(Classifier) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.classifiers.CheckClassifier: Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.BVDecompose: Set the classifiers being analysed
setClassifier(Classifier) - Method in class weka.classifiers.BVDecomposeSegCVSub: Set the classifiers being analysed
setClassifier(Classifier) - Method in class weka.classifiers.SingleClassifierEnhancer: Set the base learner.
setClassifier(Classifier) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.meta.ThresholdSelector: Set the Classifier for which threshold is set.
setClassifier(Classifier) - Method in class weka.classifiers.meta.FilteredClassifier: Sets the classifier
setClassifier(Classifier) - Method in class weka.classifiers.meta.MultiClassClassifier: Set the base classifier.
setClassifier(Classifier) - Method in class weka.classifiers.meta.AdditiveRegression: Sets the classifier
setClassifier(Classifier) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Sets the classifier
setClassifier(Classifier) - Method in class weka.classifiers.meta.Decorate: Set the base classifier for Decorate.
setClassifier(Classifier) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Sets the distribution classifier
setClassifier(Classifier) - Method in class weka.classifiers.meta.OrdinalClassClassifier: Set the base classifier.
setClassifierName(String) - Method in class weka.experiment.ClassifierSplitEvaluator: Set the Classifier to use, given it's class name.
setClassifierName(String) - Method in class weka.experiment.RegressionSplitEvaluator: Set the Classifier to use, given it's class name.
setClassifiers(Classifier[]) - Method in class weka.classifiers.MultipleClassifiersCombiner: Sets the list of possible classifers to choose from.
setClassifiers(Classifier[]) - Method in class weka.classifiers.meta.MultiScheme: Sets the list of possible classifers to choose from.
setClassifyIterations(int) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets the number of times an instance is classified
setClassIndex(int) - Method in class weka.core.Instances: Sets the class index of the set.
setClassIndex(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the attribute on which misclassifications are based.
setClassIndex(int) - Method in class weka.associations.Tertius: Set the value of classIndex.
setClassIndex(int) - Method in class weka.classifiers.BVDecompose: Sets index of attribute to discretize on
setClassIndex(int) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets index of attribute to discretize on
setClassMissing() - Method in class weka.core.Instance: Sets the class value of an instance to be "missing".
setClassName(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Sets the class containing the transformation method.
setClassOrder(int) - Method in class weka.filters.supervised.attribute.ClassOrder: Set the wanted class order
setClassType(Class) - Method in class weka.gui.GenericObjectEditor: Sets the class of values that can be edited.
setClassValue(double) - Method in class weka.core.Instance: Sets the class value of an instance to the given value (internal floating-point format).
setClassValue(String) - Method in class weka.core.Instance: Sets the class value of an instance to the given value.
setClearEachDataset(boolean) - Method in class weka.gui.streams.InstanceViewer
setClusterer(Clusterer) - Method in class weka.clusterers.MakeDensityBasedClusterer: Sets the clusterer to wrap.
setClusterer(Clusterer) - Method in class weka.clusterers.ClusterEvaluation: set the clusterer
setClusterer(Clusterer) - Method in class weka.filters.unsupervised.attribute.AddCluster: Sets the clusterer to assign clusters with.
setClusteringSeed(int) - Method in class weka.classifiers.functions.RBFNetwork: Set the random seed to be passed on to K-means.
setColor(Color) - Method in class weka.gui.treevisualizer.Node: Set the value of color.
setColoringIndex(int) - Method in class weka.gui.AttributeVisualizationPanel: Set the coloring index for the plot
setColoringIndex(int) - Method in class weka.gui.beans.AttributeSummarizer: Set the coloring index for the attribute summary plots
setColors(FastVector) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set a vector of Color objects for the classes
setColourIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Sets the index used for colouring.
setColours(FastVector) - Method in class weka.gui.visualize.ClassPanel: Set a list of colours to use for colouring labels
setColours(FastVector) - Method in class weka.gui.visualize.Plot2D: Set a list of colours to use when colouring points according to class values or cluster numbers
setColours(FastVector) - Method in class weka.gui.visualize.AttributePanel: Sets a list of colours to use for colouring data points
setColumn(int, double[]) - Method in class weka.core.Matrix: Sets a column of the matrix to the given column.
setColumnDimension(int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the column dimenion of the matrix
setCombinedCompressedData(String) - Method in class classifiers.usm.distance.USMWavDistance: Set the combined compressed wav file data
setComplexityParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval: Set the value of C for SMO
setConfidenceFactor(float) - Method in class weka.classifiers.rules.PART: Set the value of CF.
setConfidenceFactor(float) - Method in class weka.classifiers.trees.J48: Set the value of CF.
setConfirmationThreshold(double) - Method in class weka.associations.Tertius: Set the value of confirmationThreshold.
setConfirmationValues(int) - Method in class weka.associations.Tertius: Set the value of confirmationValues.
setConnections(Vector) - Static method in class weka.gui.beans.BeanConnection: Describe setConnections method here.
setConnectPoints(boolean[]) - Method in class weka.gui.visualize.PlotData2D: Set whether consecutive points should be connected by lines
setConnectPoints(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set whether consecutive points should be connected by lines
setConvertNominal(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of convertNominal.
setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.MetaCost: Sets the misclassification cost matrix.
setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Sets the misclassification cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.MetaCost: Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Sets the source location of the cost matrix.
setCrossoverProb(double) - Method in class weka.attributeSelection.GeneticSearch: set the probability of crossover
setCrossVal(int) - Method in class weka.classifiers.rules.DecisionTable: Sets the number of folds for cross validation (1 = leave one out)
setCrossValidate(boolean) - Method in class weka.classifiers.lazy.IBk: Sets whether hold-one-out cross-validation will be used to select the best k value
setCrossValidate(boolean) - Method in class classifiers.AltDist_IBk: Sets whether hold-one-out cross-validation will be used to select the best k value
setCurrentInstance(Instance) - Method in class weka.gui.beans.IncrementalClassifierEvent: Set the current instance for this event
setCustomColour(Color) - Method in class weka.gui.visualize.PlotData2D: Set a custom colour to use for this plot.
setCutoff(double) - Method in class weka.clusterers.Cobweb: set the cutoff
setCVisible(boolean) - Method in class weka.gui.treevisualizer.Node: Sets all the children of this node either to visible or invisible
setCVParameters(Object[]) - Method in class weka.classifiers.meta.CVParameterSelection: Set method for CVParameters.
setData(Instances) - Method in class weka.classifiers.rules.RuleStats: Set the data of the stats, overwriting the old one if any
setData(Instances) - Method in class evaluationMethods.OnlineEvaluation: Sets the data set to be used in the online experiment.
setDatabaseURL(String) - Method in class weka.experiment.DatabaseUtils: Set the value of DatabaseURL.
setDataFileName(String) - Method in class weka.classifiers.BVDecompose: Sets the maximum number of boost iterations
setDataFileName(String) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets the name of the dataset file.
setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the data generator to use for generating new instances
setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the density estimator to use
setDataPoint(double[]) - Method in class weka.gui.beans.ChartEvent: Set the data point
setDataset(Instances) - Method in class weka.core.Instance: Sets the reference to the dataset.
setDatasetFormat(Instances) - Method in class weka.datagenerators.BIRCHCluster: Sets the dataset format.
setDatasetFormat(Instances) - Method in class weka.datagenerators.RDG1: Sets the dataset format.
setDatasetKeyColumns(Range) - Method in class weka.experiment.PairedTTester: Set the value of DatasetKeyColumns.
setDatasetKeyFromDialog() - Method in class weka.gui.experiment.ResultsPanel
setDatasets(DefaultListModel) - Method in class weka.experiment.Experiment: Set the datasets to use in the experiment
setDatasets(DefaultListModel) - Method in class weka.experiment.RemoteExperiment: Set the datasets to use in the experiment
setDebug(boolean) - Method in class weka.gui.streams.InstanceJoiner
setDebug(boolean) - Method in class weka.gui.streams.InstanceLoader
setDebug(boolean) - Method in class weka.gui.streams.InstanceCounter
setDebug(boolean) - Method in class weka.gui.streams.InstanceSavePanel
setDebug(boolean) - Method in class weka.gui.streams.InstanceViewer
setDebug(boolean) - Method in class weka.gui.streams.InstanceTable
setDebug(boolean) - Method in class weka.core.Optimization: Set whether in debug mode
setDebug(boolean) - Method in class weka.clusterers.EM: Set debug mode - verbose output
setDebug(boolean) - Method in class weka.datagenerators.Generator: Sets the debug flag.
setDebug(boolean) - Method in class weka.datagenerators.ClusterGenerator: Sets the debug flag.
setDebug(boolean) - Method in class weka.filters.unsupervised.attribute.AddExpression: Set debug mode.
setDebug(boolean) - Method in class weka.attributeSelection.RaceSearch: Set whether verbose output should be generated.
setDebug(boolean) - Method in class weka.classifiers.CheckClassifier: Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.Classifier: Set debugging mode.
setDebug(boolean) - Method in class weka.classifiers.BVDecompose: Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.MultiScheme: Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.AdditiveRegression: Set whether debugging output is produced.
setDebug(boolean) - Method in class weka.classifiers.meta.Decorate: Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.rules.JRip
setDebug(boolean) - Method in class weka.classifiers.trees.RandomTree: Set the value of Debug.
setDebug(boolean) - Method in class weka.classifiers.functions.LinearRegression: Controls whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.functions.LeastMedSq: sets whether or not debugging output shouild be printed
setDebug(boolean) - Method in class weka.classifiers.functions.Logistic: Sets whether debugging output will be printed.
setDebug(boolean) - Method in class weka.classifiers.functions.PaceRegression: Controls whether debugging output will be printed
setDebug(boolean) - Method in class classifiers.AltDist_IBk: Set the value of Debug.
setDebugEvery(int) - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Set the TCM to debug its output, outputting progress as it goes along.
setDebugEvery(int) - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Set the VPM to debug its output, outputting its progress as it goes along.
setDecay(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
setDefaultOptions() - Method in class weka.datagenerators.BIRCHCluster: Sets all options to their default values.
setDefaultValue() - Method in class weka.gui.GenericObjectEditor: Sets the current object to be the default, taken as the first item in the chooser
setDefaultWeight(double) - Method in class weka.classifiers.functions.Winnow: Set the value of defaultWeight.
setDelimiters(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Set the value of delimiters.
setDelta(double) - Method in class weka.associations.Apriori: Set the value of delta.
setDensityBasedClusterer(DensityBasedClusterer) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Set the clusterer for use in filtering
setDesign(boolean) - Method in class weka.gui.beans.AttributeSummarizer: Set whether the appearance of this bean should be design or application
setDesignatedClass(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector: Sets the method to determine which class value to optimize.
setDesiredSize(int) - Method in class weka.classifiers.meta.Decorate: Sets the desired size of the committee.
setDesiredWeightOfInstancesPerInterval(double) - Method in class weka.filters.unsupervised.attribute.Discretize: Set the DesiredWeightOfInstancesPerInterval value.
setDirection(SelectedTag) - Method in class weka.attributeSelection.BestFirst: Set the search direction
setDisplayConnectors(boolean) - Method in class weka.gui.beans.BeanVisual: Turn on/off the connector points
setDisplayRules(boolean) - Method in class weka.classifiers.rules.DecisionTable: Sets whether rules are to be printed
setDistanceMetric(String, String[]) - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Set the distance metric
setDistanceMetric(String, String[]) - Method in class classifiers.AltDist_IBk: Set the distance metric
setDistanceWeighting(SelectedTag) - Method in class weka.classifiers.lazy.IBk: Sets the distance weighting method used.
setDistanceWeighting(SelectedTag) - Method in class classifiers.AltDist_IBk: Sets the distance weighting method used.
setDistMult(double) - Method in class weka.datagenerators.BIRCHCluster: Sets the distance multiplier.
setDistribution(SelectedTag) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the distribution to use for calculating the random matrix
setDistributionSpread(double) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets the value for the distribution spread
setDouble(int, double) - Method in class coreComponents.DoubleVector: Simply sets a double value to a vector at a particular index
setDoXval(boolean) - Method in class weka.clusterers.ClusterEvaluation: set whether or not to do cross validation
setElement(int, int, double) - Method in class weka.core.Matrix: Sets an element of the matrix to the given value.
setElementAt(Object, int) - Method in class weka.core.FastVector: Sets the element at the given index.
setEliminateColinearAttributes(boolean) - Method in class weka.classifiers.functions.LinearRegression: Set the value of EliminateColinearAttributes.
setEnabled(boolean) - Method in class weka.gui.GenericObjectEditor: Sets whether the editor is "enabled", meaning that the current values will be painted.
setEntropicAutoBlend(boolean) - Method in class weka.classifiers.lazy.KStar: Set whether entropic blending is to be used.
setEps(double) - Method in class weka.classifiers.functions.SMOreg: Set the value of eps.
setEpsilon(double) - Method in class weka.classifiers.functions.SMO: Set the value of epsilon.
setEpsilon(double) - Method in class weka.classifiers.functions.SMOreg: Set the value of epsilon.
setEpsilon(double) - Method in class classifiers.PC_SMO: Set the value of epsilon.
setEpsilon(double) - Method in class classifiers.AlphaProb_SMO: Set the value of epsilon.
setEpsilonParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval: Set the value of P for SMO
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of errorOnProbabilities.
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of errorOnProbabilities.
setEstimator(SelectedTag) - Method in class weka.classifiers.functions.PaceRegression: Sets the estimator.
setEvaluationMode(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector: Sets the evaluation mode used.
setEvaluator(ASEvaluation) - Method in class weka.filters.supervised.attribute.AttributeSelection: set a string holding the name of a attribute/subset evaluator
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.AttributeSelection: set the attribute/subset evaluator
setEvaluator(ASEvaluation) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Sets the attribute evaluator
setEvalUsingTrainingData(boolean) - Method in class weka.attributeSelection.OneRAttributeEval: Use the training data to evaluate attributes rather than cross validation
setExclusive(boolean) - Method in class weka.classifiers.rules.ConjunctiveRule
setExecutionStatus(int) - Method in class weka.experiment.TaskStatusInfo: Set the execution status of this Task.
setExpectedResultsPerAverage(int) - Method in class weka.experiment.AveragingResultProducer: Set the value of ExpectedResultsPerAverage.
setExperiment(Experiment) - Method in class weka.gui.experiment.DatasetListPanel: Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.DistributeExperimentPanel: Sets the experiment to be configured.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunNumberPanel: Sets the experiment to be configured.
setExperiment(Experiment) - Method in class weka.gui.experiment.ResultsPanel: Tells the panel to use a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.SetupPanel: Sets the experiment to configure.
setExperiment(Experiment) - Method in class weka.gui.experiment.SimpleSetupPanel: Sets the experiment to configure.
setExperiment(Experiment) - Method in class weka.gui.experiment.AlgorithmListPanel: Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel: Sets the experiment which will have the custom properties edited.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunPanel: Sets the experiment the panel operates on.
setExperiment(Experiment) - Method in class weka.experiment.RemoteExperimentSubTask: Set the experiment for this sub task
setExperiment(RemoteExperiment) - Method in class weka.gui.experiment.HostListPanel: Tells the panel to act on a new experiment.
setExponent(double) - Method in class weka.classifiers.functions.VotedPerceptron: Set the value of exponent.
setExponent(double) - Method in class weka.classifiers.functions.SMO: Set the value of exponent.
setExponent(double) - Method in class weka.classifiers.functions.SMOreg: Set the value of exponent.
setExponent(double) - Method in class classifiers.PC_SMO: Set the value of exponent.
setExponent(double) - Method in class classifiers.AlphaProb_SMO: Set the value of exponent.
setExpression(String) - Method in class weka.filters.unsupervised.attribute.AddExpression: Set the expression to apply
setFalseNegative(double) - Method in class weka.classifiers.evaluation.TwoClassStats: Sets the number of positive instances predicted as negative
setFalsePositive(double) - Method in class weka.classifiers.evaluation.TwoClassStats: Sets the number of negative instances predicted as positive
setFastRegression(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of fastRegression.
setFeatureSpaceNormalization(boolean) - Method in class weka.classifiers.functions.SMO: Set whether feature space is normalized.
setFeatureSpaceNormalization(boolean) - Method in class weka.classifiers.functions.SMOreg: Set whether feature space is normalized.
setFeatureSpaceNormalization(boolean) - Method in class classifiers.PC_SMO: Set whether feature space is normalized.
setFeatureSpaceNormalization(boolean) - Method in class classifiers.AlphaProb_SMO: Set whether feature space is normalized.
setFile(File) - Method in class weka.core.converters.ArffLoader: sets the source File
setFileStem(File) - Method in class weka.gui.beans.CSVDataSink: Sets the destination file stem
setFillWithMissing(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Sets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
setFilter(Filter) - Method in class weka.gui.beans.Filter: Set the filter to be wrapped by this bean
setFilter(Filter) - Method in class weka.classifiers.meta.FilteredClassifier: Sets the filter
setFilterType(SelectedTag) - Method in class weka.attributeSelection.SVMAttributeEval: The filtering mode to pass to SMO
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.SMO: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class weka.classifiers.functions.SMOreg: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class classifiers.PC_SMO: Sets how the training data will be transformed.
setFilterType(SelectedTag) - Method in class classifiers.AlphaProb_SMO: Sets how the training data will be transformed.
setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Set the value of FindNumBins.
setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize: Set the value of FindNumBins.
setFirstValueIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Sets index of the first value used.
setFirstValueIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues: Sets index of the first value used.
setFold(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Selects a fold.
setFold(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Selects a fold.
setFoldColumn(int) - Method in class weka.experiment.PairedTTester: Set the value of FoldColumn.
setFolds(int) - Method in class weka.gui.beans.CrossValidationFoldMaker: Set the number of folds for the cross validation
setFolds(int) - Method in class weka.clusterers.ClusterEvaluation: set the number of folds to use for cross validation
setFolds(int) - Method in class weka.attributeSelection.AttributeSelection: set the number of folds for cross validation
setFolds(int) - Method in class weka.attributeSelection.OneRAttributeEval: Set the number of folds to use for cross validation
setFolds(int) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the number of folds to use for accuracy estimation
setFolds(int) - Method in class weka.classifiers.rules.JRip
setFolds(int) - Method in class weka.classifiers.rules.ConjunctiveRule
setFolds(int) - Method in class weka.classifiers.rules.Ridor
setFoldsType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch: Set the xfold type
setFrequencyLimitForParentAttributes(int) - Method in class weka.classifiers.bayes.AODE: Set the frequency limit for parent attributes
setFrequencyThreshold(double) - Method in class weka.associations.Tertius: Set the value of frequencyThreshold.
setFunctionValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Sets a particular function value
setGamma(double) - Method in class weka.classifiers.functions.SMO: Set the value of gamma.
setGamma(double) - Method in class weka.classifiers.functions.SMOreg: Set the value of gamma.
setGamma(double) - Method in class classifiers.PC_SMO: Set the value of gamma.
setGamma(double) - Method in class classifiers.AlphaProb_SMO: Set the value of gamma.
setGenerateRanking(boolean) - Method in interface weka.attributeSelection.RankedOutputSearch: Sets whether or not ranking is to be performed.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.ForwardSelection: Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.Ranker: This is a dummy set method---Ranker is ONLY capable of producing a ranked list of attributes for attribute evaluators.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.RaceSearch: Records whether the user has requested a ranked list of attributes.
setGeneratorSamplesBase(double) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the base for computing the number of samples to obtain from each generator.
setGeneratorSamplesBase(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the base for computing the number of samples to obtain from each generator.
setGlobalBlend(int) - Method in class weka.classifiers.lazy.KStar: Set the global blend parameter
setGridWidth(int) - Method in class weka.gui.beans.AttributeSummarizer: Set the width of the grid of plots
setGUI(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron: This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.
setHandleRightClicks(boolean) - Method in class weka.gui.ResultHistoryPanel: Set whether the result history list should handle right clicks or whether the parent object will handle them.
setHeuristicStop(int) - Method in class weka.classifiers.trees.lmt.LogisticBase: Sets the option "heuristicStop".
setHeuristicStop(int) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of heuristicStop.
setHiddenLayers(String) - Method in class weka.classifiers.functions.MultilayerPerceptron: This will set what the hidden layers are made up of when auto build is enabled.
setHighlight(String) - Method in class weka.gui.treevisualizer.TreeVisualizer: Set the highlight for the node with the given id
setHoldOutFile(File) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set the file that contains hold out/test instances
setHornClauses(boolean) - Method in class weka.associations.Tertius: Set the value of hornClauses.
setIDFTransform(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the word frequencies in a document should be transformed into:
fij*log(num of Docs/num of Docs with word i)
where fij is the frequency of word i in document(instance) j.
setIgnoreClass(boolean) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Set the IgnoreClass value.
setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AddCluster: Sets the ranges of attributes to be ignored.
setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Sets the ranges of attributes to be ignored.
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.BayesNet: Method declaration
setInputFormat(Instances) - Method in class weka.filters.NullFilter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.Filter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.AllFilter: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.NominalToBinary: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.ClassOrder: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.Discretize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.Resample: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Obfuscate: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveType: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToNominal: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericToBinary: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Normalize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddExpression: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SwapValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Remove: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Standardize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Copy: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PotentialClassIgnorer: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Add: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.SparseToNonSparse: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Resample: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Randomize: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveRange: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.NonSparseToSparse: Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Sets the format of the input instances.
setInputOrder(int) - Method in class weka.datagenerators.BIRCHCluster: Sets the input order.
setInstance(Instance) - Method in class weka.gui.beans.InstanceEvent: Set the instance
setInstanceIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Changes the boolean value at the specified index in the InstIndexes array
setInstanceRange(int) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Sets the number of instances forward to translate values between.
setInstances(Instances) - Method in class weka.gui.AttributeListPanel: Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.InstancesSummaryPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.SetInstancesPanel: Updates the set of instances that is currently held by the panel
setInstances(Instances) - Method in class weka.gui.AttributeVisualizationPanel: Sets the instances for use
setInstances(Instances) - Method in class weka.gui.AttributeSummaryPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.AttributeSelectionPanel: Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.explorer.ClassifierPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.AssociationsPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.ClustererPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.PreprocessPanel: Tells the panel to use a new base set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.beans.AttributeSummarizer: Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.beans.ScatterPlotMatrix: Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.beans.DataVisualizer: Set instances for this bean.
setInstances(Instances) - Method in class weka.gui.experiment.ResultsPanel: Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.
setInstances(Instances) - Method in class weka.gui.visualize.ClassPanel: Set the instances.
setInstances(Instances) - Method in class weka.gui.visualize.VisualizePanel: Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.visualize.MatrixPanel: This method changes the Instances object of this class to a new one.
setInstances(Instances) - Method in class weka.gui.visualize.Plot2D: Sets the master plot from a set of instances
setInstances(Instances) - Method in class weka.gui.visualize.AttributePanel: This sets the instances to be drawn into the attribute panel
setInstances(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Set the training instances
setInstances(Instances) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the training data
setInstances(Instances) - Method in interface weka.experiment.ResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.AveragingResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.PairedTTester: Set the value of Instances.
setInstances(Instances) - Method in class weka.experiment.LearningRateResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.CrossValidationResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.RandomSplitResultProducer: Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.DatabaseResultProducer: Sets the dataset that results will be obtained for.
setInstancesFromDB(InstanceQuery) - Method in class weka.gui.explorer.PreprocessPanel: Loads instances from a database
setInstancesFromDBQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a URL to a database to load instances from, then loads the instances in a background process.
setInstancesFromFile(File) - Method in class weka.gui.explorer.PreprocessPanel: Loads results from a set of instances contained in the supplied file.
setInstancesFromFileQ() - Method in class weka.gui.SetInstancesPanel: Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromFileQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a file to load instances from, then loads the instances in a background process.
setInstancesFromURL(URL) - Method in class weka.gui.explorer.PreprocessPanel: Loads instances from a URL.
setInstancesFromURLQ() - Method in class weka.gui.SetInstancesPanel: Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesFromURLQ() - Method in class weka.gui.explorer.PreprocessPanel: Queries the user for a URL to load instances from, then loads the instances in a background process.
setInstancesIndices(String) - Method in class weka.filters.unsupervised.instance.RemoveRange: Sets the ranges of instances to be selected.
SetInstancesPanel - class weka.gui.SetInstancesPanel.: A panel that displays an instance summary for a set of instances and lets the user open a set of instances from either a file or URL.
SetInstancesPanel() - Constructor for class weka.gui.SetInstancesPanel: Create the panel.
setInstNums(String) - Method in class weka.datagenerators.BIRCHCluster: Sets the upper and lower boundary for instances per cluster.
setInsts(int[], boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Changes the boolean value at the specified index in the InstIndexes array
setInvert(boolean) - Method in class weka.core.Range: Sets whether the range sense is inverted, i.e.
setInvert(boolean) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Set whether selection is inverted.
setInvertSelection(boolean) - Method in class weka.filters.supervised.attribute.Discretize: Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.RemoveType: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Remove: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Copy: Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Set whether selected columns should be transformed or not.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveRange: Sets if selection is to be inverted.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Set whether selected values should be removed or kept.
setJitter(int) - Method in class weka.gui.visualize.Plot2D: Set level of jitter and repaint the plot using the new jitter value
setKernelBandwidth(int) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Set the kernel bandwidth (number of nearest neighbours to cover)
setKeyFieldName(String) - Method in class weka.experiment.AveragingResultProducer: Set the value of KeyFieldName.
setKNN(int) - Method in class weka.classifiers.lazy.LWL: Sets the number of neighbours used for kernel bandwidth setting.
setKNN(int) - Method in class weka.classifiers.lazy.IBk: Set the number of neighbours the learner is to use.
setKNN(int) - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Set the number of neighbours the learner is to use.
setKNN(int) - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Set the number of neighbours the VPM learner is to use.
setKNN(int) - Method in class classifiers.AltDist_IBk: Set the number of neighbours the learner is to use.
setKononekoMDL(boolean) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Set Kononeko MDL to be used.
setKValue(int) - Method in class weka.classifiers.trees.RandomTree: Set the value of K.
setLearningRate(double) - Method in class weka.classifiers.functions.MultilayerPerceptron: The learning rate can be set using this command.
setLegendText(Vector) - Method in class weka.gui.beans.ChartEvent: Set the legend text vector
setLikelihoodThreshold(double) - Method in class weka.classifiers.meta.LogitBoost: Set the value of Precision.
setLoader(Loader) - Method in class weka.gui.beans.Loader: Set the loader to use
setLocallyPredictive(boolean) - Method in class weka.attributeSelection.CfsSubsetEval: Include locally predictive attributes
setLocationProbs(int, double[]) - Method in class weka.gui.boundaryvisualizer.RemoteResult: Store the classifier's distribution for a particular pixel in the visualization
setLog(Logger) - Method in class weka.gui.explorer.ClassifierPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.AssociationsPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.ClustererPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.PreprocessPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.AttributeSelectionPanel: Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Set a logger
setLog(Logger) - Method in class weka.gui.beans.StripChart: Set a logger
setLog(Logger) - Method in class weka.gui.beans.Classifier: Set a logger
setLog(Logger) - Method in class weka.gui.beans.Filter: Set a logger
setLog(Logger) - Method in interface weka.gui.beans.BeanCommon: Set a logger
setLog(Logger) - Method in class weka.gui.beans.AbstractDataSink: Set a log for this bean
setLog(Logger) - Method in class weka.gui.beans.PredictionAppender: Set a logger
setLog(Logger) - Method in class weka.gui.beans.ClassAssigner
setLog(Logger) - Method in class weka.gui.beans.AbstractEvaluator: Set a logger
setLog(Logger) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Set a log for this bean
setLog(Logger) - Method in class weka.gui.beans.AbstractTestSetProducer: Set a logger
setLog(Logger) - Method in class weka.gui.visualize.VisualizePanel: Sets the Logger to receive informational messages
setLookupCacheSize(int) - Method in class weka.attributeSelection.BestFirst: Set the maximum size of the evaluated subset cache (hashtable).
setLowerBoundMinSupport(double) - Method in class weka.associations.Apriori: Set the value of lowerBoundMinSupport.
setLowerCaseTokens(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the tokens are to be downcased or not.
setLowerOrderTerms(boolean) - Method in class weka.classifiers.functions.SMO: Set whether lower-order terms are to be used.
setLowerOrderTerms(boolean) - Method in class weka.classifiers.functions.SMOreg: Set whether lower-order terms are to be used.
setLowerOrderTerms(boolean) - Method in class classifiers.PC_SMO: Set whether lower-order terms are to be used.
setLowerOrderTerms(boolean) - Method in class classifiers.AlphaProb_SMO: Set whether lower-order terms are to be used.
setLowerSize(int) - Method in class weka.experiment.LearningRateResultProducer: Set the value of LowerSize.
setMajorityClass(boolean) - Method in class weka.classifiers.rules.Ridor
setMakeBinary(boolean) - Method in class weka.filters.supervised.attribute.Discretize: Sets whether binary attributes should be made for discretized ones.
setMakeBinary(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize: Sets whether binary attributes should be made for discretized ones.
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel: Set the master plot for the visualize panel
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D: Set the master plot.
setMatchMissingValues(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Sets whether missing values are counted as a match.
setMatrix(double[], boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the whole matrix from a 1-D array
setMatrix(int[], int[], Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Set a submatrix.
setMatrix(int[], int, int, Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Set a submatrix.
setMatrix(int, int, int[], Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Set a submatrix.
setMatrix(int, int, int, DoubleVector) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the submatrix A[i0:i1][j] with the values stored in a DoubleVector
setMatrix(int, int, int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the submatrix A[i0:i1][j0:j1] with a same value
setMatrix(int, int, int, int, Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Set a submatrix.
setMax(double) - Method in class weka.gui.beans.ChartEvent: Set the max y value
setMaxBoostingIterations(int) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of maxBoostingIterations.
setMaxChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the maximum chunk size
setMaxCount(double) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets the value for the max count
setMaxDepth(int) - Method in class weka.classifiers.trees.REPTree: Set the value of MaxDepth.
setMaxGenerations(int) - Method in class weka.attributeSelection.GeneticSearch: set the number of generations to evaluate
setMaximumVariancePercentageAllowed(double) - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Sets the maximum variance attributes are allowed to have before they are deleted by the filter.
setMaxInstNum(int) - Method in class weka.datagenerators.BIRCHCluster: Sets the upper boundary for instances per cluster.
setMaxIteration(int) - Method in class weka.core.Optimization: Set the maximal number of iterations in searching (Default 200)
setMaxIterations(int) - Method in class weka.clusterers.EM: Set the maximum number of iterations to perform
setMaxIterations(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the maximum number of cleansing iterations to perform - < 1 means go until fully cleansed
setMaxIterations(int) - Method in class weka.classifiers.trees.lmt.LogisticBase: Sets the parameter "maxIterations".
setMaxIts(int) - Method in class weka.classifiers.functions.Logistic: Set the value of MaxIts.
setMaxIts(int) - Method in class weka.classifiers.functions.RBFNetwork: Set the value of MaxIts.
setMaxK(int) - Method in class weka.classifiers.functions.VotedPerceptron: Set the value of maxK.
setMaxModels(int) - Method in class weka.classifiers.meta.AdditiveRegression: Set the maximum number of models to generate
setMaxNrOfParents(int) - Method in class weka.classifiers.bayes.BayesNet: Method declaration
setMaxPlots(int) - Method in class weka.gui.beans.AttributeSummarizer: Set the maximum number of plots to display
setMaxRadius(double) - Method in class weka.datagenerators.BIRCHCluster: Sets the upper boundary for the radiuses of the clusters.
setMaxRuleSize(int) - Method in class weka.datagenerators.RDG1: Sets the maximum number of tests in rules.
setMaxStale(int) - Method in class weka.classifiers.rules.DecisionTable: Sets the number of non improving decision tables to consider before abandoning the search.
setMDLTheoryWeight(double) - Method in class weka.classifiers.rules.RuleStats: Set the weight of theory in MDL calcualtion
setMeanSquared(boolean) - Method in class weka.classifiers.lazy.IBk: Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
setMeanSquared(boolean) - Method in class classifiers.AltDist_IBk: Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
setMetaClassifier(Classifier) - Method in class weka.classifiers.meta.Stacking: Adds meta classifier
setMethod(NeuralMethod) - Method in class weka.classifiers.functions.neural.NeuralNode: Set how this node should operate (note that the neural method has no internal state, so the same object can be used by any number of nodes.
setMethod(SelectedTag) - Method in class weka.classifiers.meta.MultiClassClassifier: Sets the method used.
setMethodName(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Set the transformation method.
setMetricType(SelectedTag) - Method in class weka.associations.Apriori: Set the metric type for ranking rules
setMin(double) - Method in class weka.gui.beans.ChartEvent: Set the min y value
setMinBucketSize(int) - Method in class weka.classifiers.rules.OneR: Set the value of minBucketSize.
setMinChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the minimum chunk size
setMinimizeExpectedCost(boolean) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Set the value of MinimizeExpectedCost.
setMinimumBucketSize(int) - Method in class weka.attributeSelection.OneRAttributeEval: Set the minumum bucket size used by OneR
setMinInstNum(int) - Method in class weka.datagenerators.BIRCHCluster: Sets the lower boundary for instances per cluster.
setMinMaxX(double, double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the minimum and maximum values of the x axis fixed dimension
setMinMaxY(double, double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the minimum and maximum values of the y axis fixed dimension
setMinMetric(double) - Method in class weka.associations.Apriori: Set the value of minConfidence.
setMinNo(double) - Method in class weka.classifiers.rules.JRip
setMinNo(double) - Method in class weka.classifiers.rules.ConjunctiveRule
setMinNo(double) - Method in class weka.classifiers.rules.Ridor
setMinNum(double) - Method in class weka.classifiers.trees.REPTree: Set the value of MinNum.
setMinNum(double) - Method in class weka.classifiers.trees.RandomTree: Set the value of MinNum.
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.Rule: Set the minumum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.RuleNode: Set the minumum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.M5Base: Set the minumum number of instances to allow at a leaf node
setMinNumInstances(int) - Method in class weka.classifiers.trees.LMT: Set the value of minNumInstances.
setMinNumObj(int) - Method in class weka.classifiers.rules.PART: Set the value of minNumObj.
setMinNumObj(int) - Method in class weka.classifiers.trees.J48: Set the value of minNumObj.
setMinRadius(double) - Method in class weka.datagenerators.BIRCHCluster: Sets the lower boundary for the radiuses of the clusters.
setMinRuleSize(int) - Method in class weka.datagenerators.RDG1: Sets the minimum number of tests in rules.
setMinStdDev(double) - Method in class weka.clusterers.MakeDensityBasedClusterer: Set the minimum value for standard deviation when calculating normal density.
setMinStdDev(double) - Method in class weka.clusterers.EM: Set the minimum value for standard deviation when calculating normal density.
setMinVarianceProp(double) - Method in class weka.classifiers.trees.REPTree: Set the value of MinVarianceProp.
setMissing(Attribute) - Method in class weka.core.Instance: Sets a specific value to be "missing".
setMissing(int) - Method in class weka.core.Instance: Sets a specific value to be "missing".
setMissingMerge(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval: distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.GainRatioAttributeEval: distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: distribute the counts for missing values across observed values
setMissingMode(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Set the missing value mode.
setMissingMode(SelectedTag) - Method in class weka.classifiers.lazy.KStar: Sets the method to use for handling missing values.
setMissingSeperate(boolean) - Method in class weka.attributeSelection.CfsSubsetEval: Treat missing as a seperate value
setMissingValues(SelectedTag) - Method in class weka.associations.Tertius: Set the value of missingValues.
setMixingDistribution(DiscreteFunction) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Sets the mixing distribution
setModePanel(SetupModePanel) - Method in class weka.gui.experiment.SimpleSetupPanel: Sets the panel used to switch between simple and advanced modes.
setModifyHeader(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Sets whether the header will be modified when selecting on nominal attributes.
setMomentum(double) - Method in class weka.classifiers.functions.MultilayerPerceptron: The momentum can be set using this command.
setMutationProb(double) - Method in class weka.attributeSelection.GeneticSearch: set the probability of mutation
setName(String) - Method in class weka.gui.visualize.VisualizePanel: Set a name for this plot
setName(String) - Method in class weka.filters.unsupervised.attribute.AddExpression: Set the name for the new attribute.
setNegation(Literal) - Method in class weka.associations.tertius.Literal
setNegation(SelectedTag) - Method in class weka.associations.Tertius: Set the value of negation.
setNodesEdges(FastVector, FastVector) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Sets the nodes and edges for this LayoutEngine.
setNodesEdges(FastVector, FastVector) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method sets the nodes and edges vectors of the LayoutEngine
setNodeSize(int, int) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine: Sets the size of a node.
setNodeSize(int, int) - Method in interface weka.gui.graphvisualizer.LayoutEngine: This method sets the allowed size of the node
setNoiseRate(double) - Method in class weka.datagenerators.BIRCHCluster: Sets the percentage of noise set.
setNoiseThreshold(double) - Method in class weka.associations.Tertius: Set the value of noiseThreshold.
setNominalIndices(String) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Set which nominal labels are to be included in the selection.
setNominalIndicesArr(int[]) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Set which values of a nominal attribute are to be used for selection.
setNominalLabels(String) - Method in class weka.filters.unsupervised.attribute.Add: Set the labels for nominal attribute creation.
setNominalToBinaryFilter(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
setNoNormalization(boolean) - Method in class weka.classifiers.lazy.IBk: Set whether normalization is turned off.
setNoNormalization(boolean) - Method in class classifiers.AltDist_IBk: Set whether normalization is turned off.
setNoPruning(boolean) - Method in class weka.classifiers.trees.REPTree: Set the value of NoPruning.
setNormalize(boolean) - Method in class weka.attributeSelection.PrincipalComponents: Set whether input data will be normalized.
setNormalizeAttributes(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
setNormalizeDocLength(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the word frequencies for a document (instance) should be normalized or not.
setNormalizeNumericClass(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron
setNormalizeWordWeights(boolean) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Sets whether if the word weights for each class should be normalized
setNotes(String) - Method in class weka.experiment.Experiment: Set the user notes.
setNotes(String) - Method in class weka.experiment.RemoteExperiment: Set the user notes.
setNumAllConds(double) - Method in class weka.classifiers.rules.RuleStats: Set the number of all conditions that could appear in a rule in this RuleStats object, if the number set is smaller than 0 (typically -1), then it calcualtes based on the data store
setNumAntds(int) - Method in class weka.classifiers.rules.ConjunctiveRule
setNumAttemptsOfGeneOption(int) - Method in class weka.classifiers.rules.NNge: Sets the number of attempts for generalisation.
setNumAttributes(int) - Method in class weka.datagenerators.Generator: Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.ClusterGenerator: Sets the number of attributes the dataset should have.
setNumberLiterals(int) - Method in class weka.associations.Tertius: Set the value of numberLiterals.
setNumberOfAttributes(int) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the number of attributes (dimensions) the data should be reduced to
setNumberOfBins(int) - Method in class evaluationMethods.OnlineEvaluation: Set the number of calibration bins to use in testing the calibration of the online probabilities
setNumberVennTypes(double) - Method in class probabilityMachine.VPMDistMetaLearner: Sets the number of Venn probability types used!
setNumberVennTypes(int) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Sets the number of Venn probability types used!
setNumberVennTypes(int) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Sets the number of Venn probability types used!
setNumberVennTypes(int) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Sets the number of Venn probability types used!
setNumberVennTypes(int) - Method in class probabilityMachine.vpm.VPMBartsRMI: Sets the number of Venn probability types used!
setNumBins(int) - Method in class weka.classifiers.meta.RegressionByDiscretization: Sets the number of bins to divide each selected numeric attribute into
setNumBoostingIterations(int) - Method in class weka.classifiers.trees.LMT: Set the value of numBoostingIterations.
setNumBoostingIterations(int) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of numBoostingIterations.
setNumClasses(int) - Method in class weka.datagenerators.Generator: Sets the number of classes the dataset should have.
setNumClusters(int) - Method in class weka.clusterers.SimpleKMeans: set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.EM: Set the number of clusters (-1 to select by CV).
setNumClusters(int) - Method in class weka.clusterers.FarthestFirst: set the number of clusters to generate
setNumClusters(int) - Method in class weka.datagenerators.ClusterGenerator: Sets the number of clusters the dataset should have.
setNumClusters(int) - Method in class weka.classifiers.functions.RBFNetwork: Set the number of clusters for K-means to generate.
setNumCycles(int) - Method in class weka.datagenerators.BIRCHCluster: Sets the the number of cycles.
setNumeric(boolean) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets if the new Attribute is to be numeric.
setNumExamples(int) - Method in class weka.datagenerators.Generator: Sets the number of examples, given by option.
setNumExamplesAct(int) - Method in class weka.datagenerators.Generator: Sets the number of examples the dataset should have.
setNumExamplesAct(int) - Method in class weka.datagenerators.ClusterGenerator: Sets the number of examples the dataset should have.
setNumFeatures(int) - Method in class weka.classifiers.trees.RandomForest: Set the number of features to use in random selection.
setNumFoldersMIOption(int) - Method in class weka.classifiers.rules.NNge: Sets the number of folder for mutual information.
setNumFolds(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Sets the number of folds the dataset is split into.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Sets the number of folds the dataset is split into.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the number of cross-validation folds to use - < 2 means no cross-validation.
setNumFolds(int) - Method in class weka.experiment.CrossValidationResultProducer: Set the value of NumFolds.
setNumFolds(int) - Method in class weka.classifiers.meta.CVParameterSelection: Sets the number of folds for the cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.MultiScheme: Sets the number of folds for cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.Stacking: Sets the number of folds for the cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.LogitBoost: Set the value of NumFolds.
setNumFolds(int) - Method in class weka.classifiers.rules.PART: Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.trees.J48: Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.trees.REPTree: Set the value of NumFolds.
setNumFolds(int) - Method in class weka.classifiers.functions.SMO: Set the value of numFolds.
setNumFolds(int) - Method in class classifiers.PC_SMO: Set the value of numFolds.
setNumFolds(int) - Method in class classifiers.AlphaProb_SMO: Set the value of numFolds.
setNumIrrelevant(int) - Method in class weka.datagenerators.RDG1: Sets the number of irrelevant attributes.
setNumIterations(int) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.classifiers.meta.MetaCost: Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.classifiers.meta.Decorate: Sets the max number of Decorate iterations to run.
setNumIterations(int) - Method in class weka.classifiers.functions.VotedPerceptron: Set the value of NumIterations.
setNumIterations(int) - Method in class weka.classifiers.functions.Winnow: Set the value of numIterations.
setNumNeighbours(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the number of nearest neighbours
setNumNumeric(int) - Method in class weka.datagenerators.RDG1: Sets the number of numerical attributes.
setNumOfBoostingIterations(int) - Method in class weka.classifiers.trees.ADTree: Sets the number of boosting iterations.
setNumRules(int) - Method in class weka.associations.Apriori: Set the value of numRules.
setNumRuns(int) - Method in class weka.classifiers.meta.LogitBoost: Set the value of NumRuns.
setNumSamplesPerRegion(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the number of points to uniformly sample from a region (fixed dimensions).
setNumSamplesPerRegion(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the number of points to uniformly sample from a region (fixed dimensions).
setNumSubCmtys(int) - Method in class weka.classifiers.meta.MultiBoostAB: Set the number of sub committees to use
setNumToSelect(int) - Method in interface weka.attributeSelection.RankedOutputSearch: Specify the number of attributes to select from the ranked list.
setNumToSelect(int) - Method in class weka.attributeSelection.ForwardSelection: Specify the number of attributes to select from the ranked list (if generating a ranking).
setNumToSelect(int) - Method in class weka.attributeSelection.Ranker: Specify the number of attributes to select from the ranked list.
setNumToSelect(int) - Method in class weka.attributeSelection.RaceSearch: Specify the number of attributes to select from the ranked list (if generating a ranking).
setNumTrees(int) - Method in class weka.classifiers.trees.RandomForest: Set the value of numTrees.
setNumXValFolds(int) - Method in class weka.classifiers.meta.ThresholdSelector: Set the number of folds used for cross-validation.
setObject(Object) - Method in class weka.gui.beans.PredictionAppenderCustomizer: Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer: Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer: Set the TrainTestSplitMaker to be customized
setObject(Object) - Method in class weka.gui.beans.FilterCustomizer: Set the filter bean to be edited
setObject(Object) - Method in class weka.gui.beans.ClassifierCustomizer: Set the classifier object to be edited
setObject(Object) - Method in class weka.gui.beans.StripChartCustomizer: Set the StripChart object to be customized
setObject(Object) - Method in class weka.gui.beans.ClassAssignerCustomizer: Set the bean to be edited
setObject(Object) - Method in class weka.gui.beans.LoaderCustomizer: Set the loader to be customized
setOkButtonText(String) - Method in class weka.gui.GenericObjectEditor.GOEPanel: Allows customization of the action label on the dialog.
setOn(boolean) - Method in class weka.gui.visualize.ClassPanel: Enables the panel
setOnDemandDirectory(File) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.classifiers.meta.MetaCost: Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Sets the directory that will be searched for cost files when loading on demand.
setOnlineMode(SelectedTag) - Method in class evaluationMethods.OnlineEvaluation: Sets the online mode used.
setOnlyAlphabeticTokens(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if tokens are to be formed only from contiguous alphabetic character sequences.
setOptimizations(int) - Method in class weka.classifiers.rules.JRip
setOptimizeBins(boolean) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Switches bin optimisation on.
setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Set options.
setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute: Sets the options.
setOptions(String[]) - Method in interface weka.core.OptionHandler: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.clusterers.SimpleKMeans: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.MakeDensityBasedClusterer: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.EM: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.FarthestFirst: Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.Cobweb: Parses a given list of options.
setOptions(String[]) - Method in class weka.datagenerators.BIRCHCluster: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.datagenerators.RDG1: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.AttributeSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.NominalToBinary: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.ClassOrder: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.Discretize: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.instance.Resample: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.instance.SpreadSubsample: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RemoveType: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.StringToNominal: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RemoveUseless: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddCluster: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddExpression: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AddNoise: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.ClusterMembership: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NominalToBinary: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.SwapValues: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Remove: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Copy: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder: Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Add: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Discretize: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Resample: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.Randomize: Parses a list of options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveRange: Parses the options for this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.AveragingResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.PairedTTester: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.InstanceQuery: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.Experiment: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CSVResultListener: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.LearningRateResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RegressionSplitEvaluator: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CrossValidationResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RandomSplitResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DatabaseResultProducer: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.InfoGainAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ExhaustiveSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CfsSubsetEval: Parses and sets a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ReliefFAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ForwardSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.SVMAttributeEval: Parses a given list of options Valid options are:
setOptions(String[]) - Method in class weka.attributeSelection.RankSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GainRatioAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.PrincipalComponents: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.BestFirst: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.OneRAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GeneticSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.WrapperSubsetEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.Ranker: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RaceSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RandomSearch: Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ClassifierSubsetEval: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.Apriori: Parses a given list of options.
setOptions(String[]) - Method in class weka.associations.Tertius: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.CheckClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.Classifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.IteratedSingleClassifierEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.BVDecompose: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.SingleClassifierEnhancer: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.MultipleClassifiersCombiner: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.RandomizableClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.lazy.LWL: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.lazy.KStar: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.lazy.IBk: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Bagging: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.CVParameterSelection: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MetaCost: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiScheme: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.ThresholdSelector: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.FilteredClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiClassClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AdditiveRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Stacking: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiBoostAB: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Decorate: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AdaBoostM1: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.LogitBoost: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.RegressionByDiscretization: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.OrdinalClassClassifier: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.misc.FLR: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.misc.VFI: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.BayesNetK2: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.BayesNet: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.AODE: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.NaiveBayes: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.JRip: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.ConjunctiveRule: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.OneR: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.PART: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.DecisionTable: Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.rules.Ridor: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.NNge: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.trees.J48: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.ADTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomForest: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.REPTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.M5P: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.LMT: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomTree: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.m5.M5Base: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.LinearRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SimpleLogistic: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.LeastMedSq: Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.classifiers.functions.MultilayerPerceptron: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.VotedPerceptron: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SMO: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.Winnow: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SMOreg: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.Logistic: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.PaceRegression: Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.RBFNetwork: Parses a given list of options.
setOptions(String[]) - Method in class confidenceMachine.tcm.TCMBartsRMI: Parses a given list of options.
setOptions(String[]) - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Parses a given list of options.
setOptions(String[]) - Method in class coreComponents.SVMToArff: Parses a given list of options.
setOptions(String[]) - Method in class coreComponents.DataToArff: Parses a given list of options.
setOptions(String[]) - Method in class evaluationMethods.CreateROCCurve: Parses a given list of options.
setOptions(String[]) - Method in class evaluationMethods.CalculateLoss: Parses a given list of options.
setOptions(String[]) - Method in class evaluationMethods.CreateReliabilityCurve: Parses a given list of options.
setOptions(String[]) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Parses a given list of options.
setOptions(String[]) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Parses a given list of options.
setOptions(String[]) - Method in class probabilityMachine.VPMDistMetaLearner: Parses a given list of options.
setOptions(String[]) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Parses a given list of options.
setOptions(String[]) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Parses a given list of options.
setOptions(String[]) - Method in class probabilityMachine.vpm.VPMBartsRMI: Parses a given list of options.
setOptions(String[]) - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Parses a given list of options.
setOptions(String[]) - Method in class classifiers.PC_SMO: Parses a given list of options.
setOptions(String[]) - Method in class classifiers.AlphaProb_SMO: Parses a given list of options.
setOptions(String[]) - Method in class classifiers.AltDist_IBk: Parses a given list of options.
setOptions(String[]) - Method in class classifiers.usm.distance.USMWavDistance: Parses a given list of options.
setOptions(String[]) - Method in class classifiers.stbarts.BartsRMI: Parses a given list of options.
setOutput(PrintWriter) - Method in class weka.datagenerators.Generator: Sets the print writer.
setOutput(PrintWriter) - Method in class weka.datagenerators.ClusterGenerator: Sets the print writer.
setOutputFile(File) - Method in class weka.experiment.CSVResultListener: Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.CrossValidationResultProducer: Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.RandomSplitResultProducer: Set the value of OutputFile.
setOutputPValuesAndProbs(boolean) - Method in class evaluationMethods.OnlineEvaluation: Sets whether to output the probabilities or p-values are to be output by the classifier for each prediction in the online experiment.
setOutputWordCounts(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether output instances contain 0 or 1 indicating word presence, or word counts.
setP(double) - Method in class weka.classifiers.BVDecomposeSegCVSub: Set the proportion of instances that are common between two training sets used to train a classifier.
setPanelHeight(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the height of the visualization
setPanelWidth(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the width of the visualization
setParent(Edge) - Method in class weka.gui.treevisualizer.Node: Set the value of parent.
setPassword(String) - Method in class weka.experiment.DatabaseUtils: Set the database password
setPattern(int) - Method in class weka.datagenerators.BIRCHCluster: Sets the pattern type.
setPercent() - Method in class weka.gui.visualize.MatrixPanel: Calculates the percentage to resample
setPercent(double) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the percent the attributes (dimensions) of the data should be reduced to
setPercent(int) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets the size of noise data, as a percentage of the original set.
setPercentage(int) - Method in class weka.filters.unsupervised.instance.RemovePercentage: Sets the percentage of intances to select.
setPercentCompleted(int) - Method in class weka.gui.boundaryvisualizer.RemoteResult: Set the progress for this row so far
setPercentThreshold(int) - Method in class weka.attributeSelection.SVMAttributeEval: Set the threshold below which percentage elimination reverts to constant elimination.
setPercentToEliminatePerIteration(int) - Method in class weka.attributeSelection.SVMAttributeEval: Set the percentage of attributes to eliminate per iteration
setPixHeight(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the height of a pixel
setPixWidth(double) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the width of a pixel
setPlotCompanion(Plot2DCompanion) - Method in class weka.gui.visualize.Plot2D: Set a companion class.
setPlotList(FastVector) - Method in class weka.gui.visualize.LegendPanel: Set the list of plots to generate legend entries for
setPlotName(String) - Method in class weka.gui.visualize.PlotData2D: Set the name of this plot
setPlotTrainingData(boolean) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set whether to superimpose the training data plot
setPlus(int, double) - Method in class weka.classifiers.functions.pace.DoubleVector: Adds a value to an element
setPlus(int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix: Add a value to an element and reset the element
setPointValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction: Sets a particular point value
setPopulationSize(int) - Method in class weka.attributeSelection.GeneticSearch: set the population size
setPriors(Instances) - Method in class weka.classifiers.Evaluation: Sets the class prior probabilities
setPriors(Instances) - Method in class evaluationMethods.EstimatorEvaluation: Sets the class prior probabilities
setProduceLatex(boolean) - Method in class weka.experiment.PairedTTester: Set whether latex is output
setProperty(String, String) - Method in class weka.core.ProtectedProperties: Overrides a method to prevent the properties from being modified.
setPropertyArray(Object) - Method in class weka.experiment.Experiment: Sets the array of values to set the custom property to.
setPropertyArray(Object) - Method in class weka.experiment.RemoteExperiment: Sets the array of values to set the custom property to.
setPropertyPath(PropertyNode[]) - Method in class weka.experiment.Experiment: Sets the path of properties taken to get to the custom property to iterate over.
setPropertyPath(PropertyNode[]) - Method in class weka.experiment.RemoteExperiment: Sets the path of properties taken to get to the custom property to iterate over.
setPruningType(SelectedTag) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the pruning type
setQuery(String) - Method in class weka.experiment.InstanceQuery: Set the query to execute against the database
setRaceType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch: Set the race type
setRadiuses(String) - Method in class weka.datagenerators.BIRCHCluster: Sets the upper and lower boundary for the radius of the clusters.
setRandom(Random) - Method in class weka.datagenerators.BIRCHCluster: Sets the random generator.
setRandom(Random) - Method in class weka.datagenerators.RDG1: Sets the random generator.
setRandomizeData(boolean) - Method in class weka.experiment.RandomSplitResultProducer: Set to true if dataset is to be randomized
setRandomOrder(boolean) - Method in class weka.classifiers.bayes.BayesNetK2: Set random order flag
setRandomSeed(int) - Method in class weka.filters.supervised.instance.Resample: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.supervised.instance.SpreadSubsample: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Resample: Sets the random number seed.
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Randomize: Set the random number generator seed value.
setRandomSeed(int) - Method in class weka.classifiers.trees.ADTree: Sets random seed for a random walk.
setRandomSeed(int) - Method in class weka.classifiers.functions.SMO: Set the value of randomSeed.
setRandomSeed(int) - Method in class classifiers.PC_SMO: Set the value of randomSeed.
setRandomSeed(int) - Method in class classifiers.AlphaProb_SMO: Set the value of randomSeed.
setRandomSeed(long) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets the random seed of the random number generator
setRandomSeed(long) - Method in class weka.classifiers.functions.LeastMedSq: Set the seed for the random number generator
setRandomSeed(long) - Method in class weka.classifiers.functions.MultilayerPerceptron: This seeds the random number generator, that is used when a random number is needed for the network.
setRandomWidthFactor(double) - Method in class weka.classifiers.meta.MultiClassClassifier: Sets the multiplier when generating random codes.
setRangeCorrection(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector: Sets the confidence range correction mode used.
setRanges(String) - Method in class weka.core.Range: Sets the ranges from a string representation.
setRanking(boolean) - Method in class weka.attributeSelection.AttributeSelection: produce a ranking (if possible with the set search and evaluator)
setRawOutput(boolean) - Method in class weka.experiment.CrossValidationResultProducer: Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.RandomSplitResultProducer: Set to true if raw split evaluator output is to be saved
setReducedErrorPruning(boolean) - Method in class weka.classifiers.rules.PART: Set the value of reducedErrorPruning.
setReducedErrorPruning(boolean) - Method in class weka.classifiers.trees.J48: Set the value of reducedErrorPruning.
setRefer(String) - Method in class weka.gui.treevisualizer.Node: Set the value of refer.
setRefreshFreq(int) - Method in class weka.gui.beans.StripChart: Set how often (in x axis points) to refresh the display
setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.Rule: Set the value of regressionTree.
setRegressionTree(boolean) - Method in class weka.classifiers.trees.m5.RuleNode: Set the value of regressionTree.
setRelationName(String) - Method in class weka.core.Instances: Sets the relation's name.
setRelationName(String) - Method in class weka.datagenerators.Generator: Sets the relation name the dataset should have.
setRelationName(String) - Method in class weka.datagenerators.ClusterGenerator: Sets the relation name the dataset should have.
setRemoteHosts(Vector) - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Set a list of host names of machines to distribute processing to
setRemoveAllMissingCols(boolean) - Method in class weka.associations.Apriori: Remove columns containing all missing values.
setRepeatLiterals(boolean) - Method in class weka.associations.Tertius: Set the value of repeatLiterals.
setReplaceMissingValues(boolean) - Method in class weka.filters.unsupervised.attribute.RandomProjection: Sets either to use replace missing values filter or not
setReportFrequency(int) - Method in class weka.attributeSelection.GeneticSearch: set how often reports are generated
setReset(boolean) - Method in class weka.gui.beans.ChartEvent: Set the reset flag
setReset(boolean) - Method in class weka.classifiers.functions.MultilayerPerceptron: This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.
setResultKeyFromDialog() - Method in class weka.gui.experiment.ResultsPanel
setResultListener(ResultListener) - Method in interface weka.experiment.ResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.AveragingResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.Experiment: Sets the result listener where results will be sent.
setResultListener(ResultListener) - Method in class weka.experiment.LearningRateResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.CrossValidationResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.RandomSplitResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.DatabaseResultProducer: Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.RemoteExperiment: Sets the result listener where results will be sent.
setResultProducer(ResultProducer) - Method in class weka.experiment.AveragingResultProducer: Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.Experiment: Set the result producer used for the current experiment.
setResultProducer(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer: Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer: Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.RemoteExperiment: Set the result producer used for the current experiment.
setResultsetKeyColumns(Range) - Method in class weka.experiment.PairedTTester: Set the value of ResultsetKeyColumns.
setRhoa(double) - Method in class weka.classifiers.misc.FLR: Set rhoa
setRidge(double) - Method in class weka.classifiers.functions.LinearRegression: Set the value of Ridge.
setRidge(double) - Method in class weka.classifiers.functions.Logistic: Sets the ridge in the log-likelihood.
setRidge(double) - Method in class weka.classifiers.functions.RBFNetwork: Sets the ridge value for logistic or linear regression.
setRMIThreshold(double) - Method in class confidenceMachine.tcm.TCMBartsRMI: Sets the RMI threshold
setRMIThreshold(double) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Sets the RMI threshold
setRMIThreshold(double) - Method in class probabilityMachine.vpm.VPMBartsRMI: Sets the RMI threshold
setRocAnalysis(boolean) - Method in class weka.associations.Tertius: Set the value of rocAnalysis.
setROCString(String) - Method in class weka.gui.visualize.ThresholdVisualizePanel: Set the string with ROC area
setRoot(boolean) - Method in class weka.gui.treevisualizer.Node: Set the value of root.
setRow(int, double[]) - Method in class weka.core.Matrix: Sets a row of the matrix to the given row.
setRowDimension(int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Set the row dimenion of the matrix
setRowNumber(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the row number for this sub task
setRsource(String) - Method in class weka.gui.treevisualizer.Edge: Set the value of rsource.
setRtarget(String) - Method in class weka.gui.treevisualizer.Edge: Set the value of rtarget.
setRuleset(FastVector) - Method in class weka.classifiers.rules.RuleStats: Set the ruleset of the stats, overwriting the old one if any
setRunColumn(int) - Method in class weka.experiment.PairedTTester: Set the value of RunColumn.
setRunLower(int) - Method in class weka.experiment.Experiment: Set the lower run number for the experiment.
setRunLower(int) - Method in class weka.experiment.RemoteExperiment: Set the lower run number for the experiment.
setRunUpper(int) - Method in class weka.experiment.Experiment: Set the upper run number for the experiment.
setRunUpper(int) - Method in class weka.experiment.RemoteExperiment: Set the upper run number for the experiment.
setSampleSize(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the number of instances to sample for attribute estimation
setSampleSize(int) - Method in class weka.classifiers.functions.LeastMedSq: sets number of samples
setSampleSizePercent(double) - Method in class weka.filters.supervised.instance.Resample: Sets the size of the subsample, as a percentage of the original set.
setSampleSizePercent(double) - Method in class weka.filters.unsupervised.instance.Resample: Sets the size of the subsample, as a percentage of the original set.
setSaveInstanceData(boolean) - Method in class weka.clusterers.Cobweb: Set the value of saveInstances.
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.J48: Set whether instance data is to be saved.
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.ADTree: Sets whether the tree is to save instance data.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.M5P: Set whether to save instance data at each node in the tree for visualization purposes
setScoreType(SelectedTag) - Method in class weka.classifiers.bayes.BayesNet: Method declaration
setSearch(ASSearch) - Method in class weka.filters.supervised.attribute.AttributeSelection: Set as string holding the name of a search class
setSearch(ASSearch) - Method in class weka.attributeSelection.AttributeSelection: set the search method
setSearch(ASSearch) - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Sets the search method
setSearchPath(SelectedTag) - Method in class weka.classifiers.trees.ADTree: Sets the method of searching the tree for a new insertion.
setSearchPercent(double) - Method in class weka.attributeSelection.RandomSearch: set the percentage of the search space to consider
setSearchTermination(int) - Method in class weka.attributeSelection.BestFirst: Set the numnber of non-improving nodes to consider before terminating search.
setSecondValueIndex(String) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Sets index of the second value used.
setSecondValueIndex(String) - Method in class weka.filters.unsupervised.attribute.SwapValues: Sets index of the second value used.
setSeed(int) - Method in class weka.gui.beans.TrainTestSplitMaker: Set the random seed
setSeed(int) - Method in class weka.gui.beans.CrossValidationFoldMaker: Set the seed
setSeed(int) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Initializes a new random number generator using the supplied seed.
setSeed(int) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Set a seed for random number generation (if needed).
setSeed(int) - Method in interface weka.core.Randomizable: Set the seed for random number generation.
setSeed(int) - Method in class weka.clusterers.SimpleKMeans: Set the random number seed
setSeed(int) - Method in class weka.clusterers.EM: Set the random number seed
setSeed(int) - Method in class weka.clusterers.ClusterEvaluation: set the seed to use for cross validation
setSeed(int) - Method in class weka.clusterers.FarthestFirst: Set the random number seed
setSeed(int) - Method in class weka.datagenerators.BIRCHCluster: Sets the random number seed.
setSeed(int) - Method in class weka.datagenerators.RDG1: Sets the random number seed.
setSeed(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the random number seed for randomly sampling instances.
setSeed(int) - Method in class weka.attributeSelection.AttributeSelection: set the seed for use in cross validation
setSeed(int) - Method in class weka.attributeSelection.OneRAttributeEval: Set the random number seed for cross validation
setSeed(int) - Method in class weka.attributeSelection.GeneticSearch: set the seed for random number generation
setSeed(int) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the seed to use for cross validation
setSeed(int) - Method in class weka.classifiers.BVDecompose: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.RandomizableSingleClassifierEnhancer: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableMultipleClassifiersCombiner: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.BVDecomposeSegCVSub: Sets the random number seed
setSeed(int) - Method in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.RandomizableClassifier: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.meta.MultiScheme: Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.ThresholdSelector: Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.MultiClassClassifier: Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.Decorate: Set the seed for random number generator.
setSeed(int) - Method in class weka.classifiers.meta.CostSensitiveClassifier: Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.rules.PART: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.rules.Ridor
setSeed(int) - Method in class weka.classifiers.trees.J48: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.trees.RandomForest: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.trees.REPTree: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.trees.RandomTree: Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.evaluation.EvaluationUtils: Sets the seed for randomization during cross-validation
setSeed(int) - Method in class weka.classifiers.functions.VotedPerceptron: Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.functions.Winnow: Set the value of Seed.
setSeed(long) - Method in class weka.filters.supervised.attribute.ClassOrder: Set randomization seed
setSeed(long) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Sets the random number seed for shuffling the dataset.
setSeed(long) - Method in class weka.filters.unsupervised.instance.RemoveFolds: Sets the random number seed for shuffling the dataset.
setSeed(long) - Method in class weka.classifiers.rules.JRip
setSeed(long) - Method in class weka.classifiers.rules.ConjunctiveRule
setSelectedRange(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Set the value of m_SelectedRange.
setSelectionThreshold(double) - Method in class weka.attributeSelection.RaceSearch: Set the threshold by which the AttributeSelection module can discard attributes.
setSeparatingThreshold(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Sets the separating threshold value
setSeparatingThreshold(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Sets the separating threshold value
setSeperator(String) - Method in class weka.gui.HierarchyPropertyParser: Set the seperator between levels.
setSequentialAttIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: A Sequential Attribute index is all those Attributes that are set to the specified value placed in a sequential array.
setSequentialDataset(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: Sets both the Instance and Attribute indexes to a specified value
setSequentialInstanceIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes: A Sequential Instance index is all those Instances that are set to the specified value placed in a sequential array.
setShape(int) - Method in class weka.gui.treevisualizer.Node: Set the value of shape.
setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel: This will set the shapes for the instances.
setShapeSize(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set the shape sizes for the plot data
setShapeSize(int[]) - Method in class weka.gui.visualize.PlotData2D: Set the shape sizes for the plot data
setShapeType(FastVector) - Method in class weka.gui.visualize.PlotData2D: Set the shape type for the plot data
setShapeType(int[]) - Method in class weka.gui.visualize.PlotData2D: Set the shape type for the plot data
setShowRules(boolean) - Method in class weka.classifiers.misc.FLR: Set ShowRules flag
setShowStdDevs(boolean) - Method in class weka.experiment.PairedTTester: Set whether standard deviations are displayed or not.
setShrinkage(double) - Method in class weka.classifiers.meta.AdditiveRegression: Set the shrinkage parameter
setShrinkage(double) - Method in class weka.classifiers.meta.LogitBoost: Set the value of Shrinkage.
setShuffle(int) - Method in class weka.classifiers.rules.Ridor
setSigma(int) - Method in class weka.attributeSelection.ReliefFAttributeEval: Sets the sigma value.
setSignificanceLevel(double) - Method in class weka.experiment.PairedTTester: Set the value of SignificanceLevel.
setSignificanceLevel(double) - Method in class weka.attributeSelection.RaceSearch: Sets the significance level to use
setSignificanceLevel(double) - Method in class weka.associations.Apriori: Set the value of significanceLevel.
setSignificanceLevel(double) - Method in class evaluationMethods.OnlineEvaluation: Sets the significance level for a confidence classifier in an online experiment.
setSIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Set the shape for creating splits.
setSingle(String) - Method in class weka.gui.ResultHistoryPanel: Sets the single-click display to view the named result.
setSingleCompressedData(String) - Method in class classifiers.usm.distance.USMWavDistance: Set the single compressed wav file data
setSingleIndex(String) - Method in class weka.core.SingleIndex: Sets the index from a string representation.
setSize(int) - Method in class weka.classifiers.functions.pace.DoubleVector: Sets the size of the vector
setSize(int) - Method in class weka.classifiers.functions.pace.IntVector: Sets the size of the vector.
setSmoothing(boolean) - Method in class weka.classifiers.trees.m5.Rule: Smooth predictions
setSmoothingParameter(double) - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Sets the smoothing value used to avoid zero WordGivenClass probabilities
setSource(File) - Method in class weka.core.converters.CSVLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in interface weka.core.converters.Loader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.C45Loader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.SerializedInstancesLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.ArffLoader: Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File) - Method in class weka.core.converters.AbstractLoader: Default implementation throws an IOException.
setSource(InputStream) - Method in interface weka.core.converters.Loader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream) - Method in class weka.core.converters.SerializedInstancesLoader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream) - Method in class weka.core.converters.ArffLoader: Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream) - Method in class weka.core.converters.AbstractLoader: Default implementation throws an IOException.
setSource(Node) - Method in class weka.gui.treevisualizer.Edge: Set the value of source.
setSparseData(boolean) - Method in class weka.experiment.InstanceQuery: Sets whether data should be encoded as sparse instances
setSplitByDataSet(boolean) - Method in class weka.experiment.RemoteExperiment: Set whether sub experiments are to be created on the basis of data set.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.CrossValidationResultProducer: Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.RandomSplitResultProducer: Set the SplitEvaluator.
setSplitOnResiduals(boolean) - Method in class weka.classifiers.trees.LMT: Set the value of splitOnResiduals.
setSplitPoint(double) - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Split point to be used for selection on numeric attribute.
setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.C45Split: Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Sets split point to greatest value in given data smaller or equal to old split point.
setStartSet(String) - Method in class weka.attributeSelection.ExhaustiveSearch: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.ForwardSelection: Sets a starting set of attributes for the search.
setStartSet(String) - Method in interface weka.attributeSelection.StartSetHandler: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.BestFirst: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.GeneticSearch: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.Ranker: Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.RandomSearch: Sets a starting set of attributes for the search.
setStatic() - Method in class weka.gui.beans.BeanVisual: Set the static version of the icon
setStatus(int) - Method in class weka.gui.beans.IncrementalClassifierEvent: Set the status
setStatus(int) - Method in class weka.gui.beans.InstanceEvent: Set the status
setStatusMessage(String) - Method in class weka.experiment.TaskStatusInfo: Set the status message.
setStepSize(int) - Method in class weka.experiment.LearningRateResultProducer: Set the value of StepSize.
setSubtreeRaising(boolean) - Method in class weka.classifiers.trees.J48: Set the value of subtreeRaising.
setSuppressErrorMessage(boolean) - Method in class weka.classifiers.functions.SimpleLinearRegression: Turn off the error message that is reported when no useful attribute is found.
setTarget(Node) - Method in class weka.gui.treevisualizer.Edge: Set the value of target.
setTarget(Object) - Method in class weka.gui.PropertySheetPanel: Sets a new target object for customisation.
setTaskResult(Object) - Method in class weka.experiment.TaskStatusInfo: Set the returnable result for this task..
setTentativeInstance(Instance) - Method in interface coreComponents.NonExchangeableDistance: Sets the instances which we tentatively update the distance metric for.
setTentativeInstance(Instance) - Method in class classifiers.vdm.ValueDifferenceMetric: Sets the instances which we tentatively update the distance metric for.
setTestBaseFromDialog() - Method in class weka.gui.experiment.ResultsPanel
setText(String) - Method in class weka.gui.beans.BeanVisual: Set the label for the visual.
setTFTransform(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the word frequencies should be transformed into log(1+fij) where fij is the frequency of word i in document(instance) j.
setThreshold(double) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Sets the threshold for the max error when predicting a numeric class.
setThreshold(double) - Method in interface weka.attributeSelection.RankedOutputSearch: Sets a threshold by which attributes can be discarded from the ranking.
setThreshold(double) - Method in class weka.attributeSelection.ForwardSelection: Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double) - Method in class weka.attributeSelection.AttributeSelection: set the threshold by which to select features from a ranked list
setThreshold(double) - Method in class weka.attributeSelection.WrapperSubsetEval: Set the value of the threshold for repeating cross validation
setThreshold(double) - Method in class weka.attributeSelection.Ranker: Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double) - Method in class weka.attributeSelection.RaceSearch: Sets the threshold for comparisons
setThreshold(double) - Method in class weka.classifiers.functions.Winnow: Set the value of Threshold.
setThreshold(double) - Method in class weka.classifiers.functions.PaceRegression: Set threshold for the olsc estimator
setTimes(int, double) - Method in class weka.classifiers.functions.pace.DoubleVector: Multiplies a value to an element
setTimes(int, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix: Multiply a value with an element and reset the element
setToleranceParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval: Set the value of T for SMO
setToleranceParameter(double) - Method in class weka.classifiers.functions.SMO: Set the value of tolerance parameter.
setToleranceParameter(double) - Method in class weka.classifiers.functions.SMOreg: Set the value of tolerance parameter.
setToleranceParameter(double) - Method in class classifiers.PC_SMO: Set the value of tolerance parameter.
setToleranceParameter(double) - Method in class classifiers.AlphaProb_SMO: Set the value of tolerance parameter.
setTop(double) - Method in class weka.gui.treevisualizer.Node: Set the value of top.
setTrainingData(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the training data to use
setTrainingTime(int) - Method in class weka.classifiers.functions.MultilayerPerceptron: Set the number of training epochs to perform.
setTrainIterations(int) - Method in class weka.classifiers.BVDecompose: Sets the maximum number of boost iterations
setTrainPercent(double) - Method in class weka.experiment.RandomSplitResultProducer: Set the value of TrainPercent.
setTrainPercent(int) - Method in class weka.gui.beans.TrainTestSplitMaker: Set the percentage of data to be in the training portion of the split
setTrainPoolSize(int) - Method in class weka.classifiers.BVDecompose: Set the number of instances in the training pool.
setTrainSize(int) - Method in class weka.classifiers.BVDecomposeSegCVSub: Set the training size.
setTransformBackToOriginal(boolean) - Method in class weka.attributeSelection.PrincipalComponents: Sets whether the data should be transformed back to the original space
setTrimingThreshold(double) - Method in class weka.classifiers.functions.pace.ChisqMixture: Sets the triming thresholding value.
setTrimingThreshold(double) - Method in class weka.classifiers.functions.pace.NormalMixture: Sets the triming thresholding value.
setTrueNegative(double) - Method in class weka.classifiers.evaluation.TwoClassStats: Sets the number of negative instances predicted as negative
setTruePositive(double) - Method in class weka.classifiers.evaluation.TwoClassStats: Sets the number of positive instances predicted as positive
setType(int) - Method in class weka.classifiers.functions.neural.NeuralConnection
setUnpruned(boolean) - Method in class weka.classifiers.rules.PART: Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.J48: Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.Rule: Use unpruned tree/rules
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.M5Base: Use unpruned tree/rules
setupAttribLists() - Method in class weka.gui.visualize.MatrixPanel: Sets up the UI's attributes lists
setUpComboBoxes(Instances) - Method in class weka.gui.visualize.VisualizePanel
setUpComboBoxes(Instances) - Method in class weka.gui.visualize.ThresholdVisualizePanel: This overloads VisualizePanel's setUpComboBoxes to add ActionListeners to watch for when the X/Y Axis comboboxes are changed.
setUpdateIncrementalClassifier(boolean) - Method in class weka.gui.beans.Classifier
SetupModePanel - class weka.gui.experiment.SetupModePanel.: This panel switches between simple and advanced experiment setup panels.
SetupModePanel() - Constructor for class weka.gui.experiment.SetupModePanel: Creates the setup panel with no initial experiment.
SetupPanel - class weka.gui.experiment.SetupPanel.: This panel controls the configuration of an experiment.
SetupPanel() - Constructor for class weka.gui.experiment.SetupPanel: Creates the setup panel with no initial experiment.
SetupPanel(Experiment) - Constructor for class weka.gui.experiment.SetupPanel: Creates the setup panel with the supplied initial experiment.
setUpper(int) - Method in class weka.core.Range: Sets the value of "last".
setUpper(int) - Method in class weka.core.SingleIndex: Sets the value of "last".
setUpperBoundMinSupport(double) - Method in class weka.associations.Apriori: Set the value of upperBoundMinSupport.
setUpperSize(int) - Method in class weka.experiment.LearningRateResultProducer: Set the value of UpperSize.
setUpVisualizableInstances(Instances, ClusterEvaluation) - Static method in class weka.gui.explorer.ClustererPanel: Sets up the structure for the visualizable instances.
setUseADTree(boolean) - Method in class weka.classifiers.bayes.BayesNet: Method declaration
setUseBetterEncoding(boolean) - Method in class weka.filters.supervised.attribute.Discretize: Sets whether better encoding is to be used for MDL.
setUseCrossValidation(boolean) - Method in class weka.classifiers.functions.SimpleLogistic: Set the value of useCrossValidation.
setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Set the value of UseEqualFrequency.
setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize: Set the value of UseEqualFrequency.
setUseIBk(boolean) - Method in class weka.classifiers.rules.DecisionTable: Sets whether IBk should be used instead of the majority class
setUseKernelEstimator(boolean) - Method in class weka.classifiers.bayes.NaiveBayes: Sets if kernel estimator is to be used.
setUseKononenko(boolean) - Method in class weka.filters.supervised.attribute.Discretize: Sets whether Kononenko's MDL criterion is to be used.
setUseLaplace(boolean) - Method in class weka.classifiers.trees.J48: Set the value of useLaplace.
setUseMissing(boolean) - Method in class weka.filters.unsupervised.attribute.AddNoise: Sets the flag if missing values are treated as extra values.
setUsePropertyIterator(boolean) - Method in class weka.experiment.Experiment: Sets whether the custom property iterator should be used.
setUsePropertyIterator(boolean) - Method in class weka.experiment.RemoteExperiment: Sets whether the custom property iterator should be used.
setUsePruning(boolean) - Method in class weka.classifiers.rules.JRip
setUseRBF(boolean) - Method in class weka.classifiers.functions.SMO: Set if the RBF kernel is to be used.
setUseRBF(boolean) - Method in class weka.classifiers.functions.SMOreg: Set if the RBF kernel is to be used.
setUseRBF(boolean) - Method in class classifiers.PC_SMO: Set if the RBF kernel is to be used.
setUseRBF(boolean) - Method in class classifiers.AlphaProb_SMO: Set if the RBF kernel is to be used.
setUseResampling(boolean) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.AdaBoostM1: Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.LogitBoost: Set resampling mode
setUsername(String) - Method in class weka.experiment.DatabaseUtils: Set the database username
setUseStoplist(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets whether if the words that are on a stoplist are to be ignored (The stop list is in weka.core.StopWords).
setUseSupervisedDiscretization(boolean) - Method in class weka.classifiers.bayes.NaiveBayes: Set whether supervised discretization is to be used.
setUseSupervisedDiscretization(boolean) - Method in class weka.classifiers.bayes.NaiveBayesUpdateable: Set whether supervised discretization is to be used.
setUseTraining(boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval: Set if training data is to be used instead of hold out/test data
setUseTree(boolean) - Method in class weka.classifiers.trees.m5.Rule: Use an m5 tree rather than generate rules
setUseUnsmoothed(boolean) - Method in class weka.classifiers.trees.m5.M5Base: Use unsmoothed predictions
setValidationChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Set the validation chunk size
setValidationSetSize(int) - Method in class weka.classifiers.functions.MultilayerPerceptron: This will set the size of the validation set.
setValidationThreshold(int) - Method in class weka.classifiers.functions.MultilayerPerceptron: This sets the threshold to use for when validation testing is being done.
setValue(Attribute, double) - Method in class weka.core.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(Attribute, String) - Method in class weka.core.Instance: Sets a value of an nominal or string attribute to the given value.
setValue(double) - Method in class weka.classifiers.trees.adtree.PredictionNode: Sets the prediction value of the node.
setValue(int, double) - Method in class weka.core.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, double) - Method in class weka.core.BinarySparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, double) - Method in class weka.core.SparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, String) - Method in class weka.core.Instance: Sets a value of a nominal or string attribute to the given value.
setValue(Object) - Method in class weka.gui.GenericObjectEditor: Sets the current Object.
setValue(Object) - Method in class weka.gui.GenericArrayEditor: Sets the current object array.
setValue(Object) - Method in class weka.gui.CostMatrixEditor: Sets the value of the CostMatrix to be edited.
setValueIndex(int) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets index of the indicator value.
setValueIndices(String) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Sets indices of the indicator values.
setValueIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.MakeIndicator: Set which attributes are to be deleted (or kept if invert is true)
setValuesOutput(SelectedTag) - Method in class weka.associations.Tertius: Set the value of valuesOutput.
setValueSparse(int, double) - Method in class weka.core.Instance: Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.BinarySparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.SparseInstance: Sets a specific value in the instance to the given value (internal floating-point format).
setVarianceCovered(double) - Method in class weka.attributeSelection.PrincipalComponents: Sets the amount of variance to account for when retaining principal components
setVerbose(boolean) - Method in class weka.attributeSelection.ExhaustiveSearch: set whether or not to output new best subsets as the search proceeds
setVerbose(boolean) - Method in class weka.attributeSelection.RandomSearch: set whether or not to output new best subsets as the search proceeds
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainingSetProducer: Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.StripChart: Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.Classifier: Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.Filter: Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSink: Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.TextViewer: Describe setVisual method here.
setVisual(BeanVisual) - Method in class weka.gui.beans.PredictionAppender: Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.ClassAssigner
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSource: Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.GraphViewer: Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractEvaluator: Set the visual
setVisual(BeanVisual) - Method in interface weka.gui.beans.Visible: Set a new visual representation
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTestSetProducer: Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.DataVisualizer: Set the visual appearance of this bean
setVoteFlag(boolean) - Method in class weka.datagenerators.RDG1: Sets the vote flag.
setWeight(double) - Method in class weka.core.Instance: Sets the weight of an instance.
setWeightByConfidence(boolean) - Method in class weka.classifiers.misc.VFI: Set weighting by confidence
setWeightByDistance(boolean) - Method in class weka.attributeSelection.ReliefFAttributeEval: Set the nearest neighbour weighting method
setWeightingDimensions(boolean[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Set which dimensions to use when computing a weight for the next instance to generate
setWeightingDimensions(boolean[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Set the dimensions to be used in computing a weight for each instance generated
setWeightingKernel(int) - Method in class weka.classifiers.lazy.LWL: Sets the kernel weighting method to use.
setWeightingValues(double[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Set the values for the weighting dimensions to be used when computing the weight for the next instance to be generated
setWeightingValues(double[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator: Set the values of the dimensions (chosen via setWeightingDimensions) to be used when computing instance weights
setWeightThreshold(int) - Method in class weka.classifiers.meta.AdaBoostM1: Set weight threshold
setWeightThreshold(int) - Method in class weka.classifiers.meta.LogitBoost: Set weight thresholding
setWholeDataErr(boolean) - Method in class weka.classifiers.rules.Ridor
setWindowSize(int) - Method in class weka.classifiers.lazy.IBk: Sets the maximum number of instances allowed in the training pool.
setWindowSize(int) - Method in class classifiers.AltDist_IBk: Sets the maximum number of instances allowed in the training pool.
setWordsToKeep(int) - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Sets the number of words (per class if there is a class attribute assigned) to attempt to keep.
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Loader: Set the loader
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Classifier: Sets the algorithm (classifier) for this bean
setWrappedAlgorithm(Object) - Method in class weka.gui.beans.Filter: Set the filter to be wrapped by this bean
setWrappedAlgorithm(Object) - Method in interface weka.gui.beans.WekaWrapper: Set the algorithm.
setX(double) - Method in class weka.classifiers.functions.neural.NeuralConnection
setX(int) - Method in class weka.gui.beans.BeanInstance: Sets the x coordinate of this bean
setX(int) - Method in class weka.gui.visualize.AttributePanel: shows which bar is the current x attribute.
setXAttribute(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the x attribute index
setXAttribute(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the x axis fixed dimension
setXindex(int) - Method in class weka.gui.visualize.Plot2D: Set the index of the attribute to go on the x axis
setXindex(int) - Method in class weka.gui.visualize.PlotData2D: Set the x index of the data.
setXIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Set the index of the attribute for the x axis
setXLabelFreq(int) - Method in class weka.gui.beans.StripChart: Set the frequency for printing x label values
setXval(boolean) - Method in class weka.attributeSelection.AttributeSelection: do a cross validation
setXY_VisualizeIndexes(int, int) - Method in class weka.gui.explorer.ClassifierPanel: Set the default attributes to use on the x and y axis of a new visualization object.
setXY_VisualizeIndexes(int, int) - Method in class weka.gui.explorer.ClustererPanel: Set the default attributes to use on the x and y axis of a new visualization object.
setY(double) - Method in class weka.classifiers.functions.neural.NeuralConnection
setY(int) - Method in class weka.gui.beans.BeanInstance: Sets the y coordinate of this bean
setY(int) - Method in class weka.gui.visualize.AttributePanel: shows which bar is the current y attribute.
setYAttribute(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Set the y attribute index
setYAttribute(int) - Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Set the y axis fixed dimension
setYindex(int) - Method in class weka.gui.visualize.Plot2D: Set the index of the attribute to go on the y axis
setYindex(int) - Method in class weka.gui.visualize.PlotData2D: Set the y index of the data
setYIndex(int) - Method in class weka.gui.visualize.VisualizePanel: Set the index of the attribute for the y axis
SFEntropyGain() - Method in class weka.classifiers.Evaluation: Returns the total SF, which is the null model entropy minus the scheme entropy.
SFEntropyGain() - Method in class evaluationMethods.EstimatorEvaluation: Returns the total SF, which is the null model entropy minus the scheme entropy.
SFMeanEntropyGain() - Method in class weka.classifiers.Evaluation: Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanEntropyGain() - Method in class evaluationMethods.EstimatorEvaluation: Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanPriorEntropy() - Method in class weka.classifiers.Evaluation: Returns the entropy per instance for the null model
SFMeanPriorEntropy() - Method in class evaluationMethods.EstimatorEvaluation: Returns the entropy per instance for the null model
SFMeanSchemeEntropy() - Method in class weka.classifiers.Evaluation: Returns the entropy per instance for the scheme
SFMeanSchemeEntropy() - Method in class evaluationMethods.EstimatorEvaluation: Returns the entropy per instance for the scheme
SFPriorEntropy() - Method in class weka.classifiers.Evaluation: Returns the total entropy for the null model
SFPriorEntropy() - Method in class evaluationMethods.EstimatorEvaluation: Returns the total entropy for the null model
SFSchemeEntropy() - Method in class weka.classifiers.Evaluation: Returns the total entropy for the scheme
SFSchemeEntropy() - Method in class evaluationMethods.EstimatorEvaluation: Returns the total entropy for the scheme
shift(int, int) - Method in class weka.classifiers.functions.pace.IntVector: Shifts an element to another position.
shift(int, int, Instance) - Method in class weka.classifiers.trees.j48.Distribution: Shifts given instance from one bag to another one.
shiftRange(int, int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution: Shifts all instances in given range from one bag to another one.
shiftToEnd(int) - Method in class weka.classifiers.functions.pace.IntVector: Shifts an element to the end of the vector.
SHORT - Static variable in class weka.experiment.DatabaseUtils
show(Component, int, int) - Method in class weka.gui.GenericObjectEditor.JTreePopupMenu: Displays the menu, making sure it will fit on the screen.
showChart() - Method in class weka.gui.beans.StripChart: Popup the chart panel
showDialog() - Method in class weka.gui.ListSelectorDialog: Pops up the modal dialog and waits for cancel or a selection.
showDialog() - Method in class weka.gui.PropertySelectorDialog: Pops up the modal dialog and waits for cancel or a selection.
showPropertyDialog() - Method in class weka.gui.PropertyPanel: Displays the property edit dialog for the panel.
showResults() - Method in class weka.gui.beans.TextViewer: Popup a component to display the selected text
showResults() - Method in class weka.gui.beans.GraphViewer: Popup a result list from which the user can select a graph to view
showRules() - Method in class weka.classifiers.misc.FLR: Returns the induced set of Fuzzy Lattice Rules
showRulesTipText() - Method in class weka.classifiers.misc.FLR: Returns the tip text for this property
showTree() - Method in class weka.gui.HierarchyPropertyParser: Show the whole tree in text format
shrinkageTipText() - Method in class weka.classifiers.meta.AdditiveRegression: Returns the tip text for this property
shrinkageTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
shuffleTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
sigLevel - Variable in class weka.experiment.PairedStats: The significance level for comparisons
sigmaTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
SigmoidUnit - class weka.classifiers.functions.neural.SigmoidUnit.: This can be used by the neuralnode to perform all it's computations (as a sigmoid unit).
SigmoidUnit() - Constructor for class weka.classifiers.functions.neural.SigmoidUnit
sign() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the signs of all elements in terms of -1, 0 and +1.
significanceLevelTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
significanceLevelTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
SIGNIFICANT - Static variable in class weka.associations.Tertius
SimpleCLI - class weka.gui.SimpleCLI.: Creates a very simple command line for invoking the main method of classes.
SimpleCLI() - Constructor for class weka.gui.SimpleCLI: Constructor
SimpleKMeans - class weka.clusterers.SimpleKMeans.: Simple k means clustering class.
SimpleKMeans() - Constructor for class weka.clusterers.SimpleKMeans
SimpleLinearRegression - class weka.classifiers.functions.SimpleLinearRegression.: Class for learning a simple linear regression model.
SimpleLinearRegression() - Constructor for class weka.classifiers.functions.SimpleLinearRegression
SimpleLinkedList - class weka.associations.tertius.SimpleLinkedList.
SimpleLinkedList.LinkedListInverseIterator - class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator.
SimpleLinkedList.LinkedListInverseIterator() - Constructor for class weka.associations.tertius.SimpleLinkedList.LinkedListInverseIterator
SimpleLinkedList.LinkedListIterator - class weka.associations.tertius.SimpleLinkedList.LinkedListIterator.
SimpleLinkedList.LinkedListIterator() - Constructor for class weka.associations.tertius.SimpleLinkedList.LinkedListIterator
SimpleLinkedList() - Constructor for class weka.associations.tertius.SimpleLinkedList
SimpleLogistic - class weka.classifiers.functions.SimpleLogistic.: Class for building a logistic regression model using LogitBoost.
SimpleLogistic() - Constructor for class weka.classifiers.functions.SimpleLogistic: Constructor for creating SimpleLogistic object with standard options.
SimpleLogistic(int, boolean, boolean) - Constructor for class weka.classifiers.functions.SimpleLogistic: Constructor for creating SimpleLogistic object.
SimpleSetupPanel - class weka.gui.experiment.SimpleSetupPanel.: This panel controls the configuration of an experiment.
SimpleSetupPanel() - Constructor for class weka.gui.experiment.SimpleSetupPanel: Creates the setup panel with no initial experiment.
SimpleSetupPanel(Experiment) - Constructor for class weka.gui.experiment.SimpleSetupPanel: Creates the setup panel with the supplied initial experiment.
SINE - Static variable in class weka.datagenerators.BIRCHCluster
SingleClassifierEnhancer - class weka.classifiers.SingleClassifierEnhancer.: Abstract utility class for handling settings common to meta classifiers that use a single base learner.
SingleClassifierEnhancer() - Constructor for class weka.classifiers.SingleClassifierEnhancer
SingleIndex - class weka.core.SingleIndex.: Class representing a single cardinal number.
SingleIndex() - Constructor for class weka.core.SingleIndex: Default constructor.
SingleIndex(String) - Constructor for class weka.core.SingleIndex: Constructor to set initial index.
singletons(Instances) - Static method in class weka.associations.ItemSet: Converts the header info of the given set of instances into a set of item sets (singletons).
SINGULAR_DUMMY - Static variable in interface weka.gui.graphvisualizer.GraphConstants: SINGULAR_DUMMY node - node with only one outgoing edge i.e.
size() - Method in class weka.core.FastVector: Returns the vector's current size.
size() - Method in class weka.core.Queue: Gets queue's size.
size() - Method in class weka.associations.tertius.SimpleLinkedList
size() - Method in class weka.classifiers.CostMatrix: Gets the size of the matrix.
size() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable: Returns the number of keys in this hashtable.
size() - Method in class weka.classifiers.rules.Rule: The size of the rule.
size() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of classes.
size() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Returns the size of the point set.
size() - Method in class weka.classifiers.functions.pace.DoubleVector: Gets the size of the vector.
size() - Method in class weka.classifiers.functions.pace.IntVector: Gets the size of the vector.
sm(double, double) - Static method in class weka.core.Utils: Tests if a is smaller than b.
SMALL - Static variable in class weka.core.Utils: The small deviation allowed in double comparisons
SMO - class weka.classifiers.functions.SMO.: Implements John C.
SMO() - Constructor for class weka.classifiers.functions.SMO
smoothingParameterTipText() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Returns the tip text for this property
SMOreg - class weka.classifiers.functions.SMOreg.: Implements Alex J.Smola and Bernhard Scholkopf sequential minimal optimization algorithm for training a support vector regression using polynomial or RBF kernels.
SMOreg() - Constructor for class weka.classifiers.functions.SMOreg
smOrEq(double, double) - Static method in class weka.core.Utils: Tests if a is smaller or equal to b.
SMOset - class weka.classifiers.functions.supportVector.SMOset.: Stores a set of integer of a given size.
SMOset(int) - Constructor for class weka.classifiers.functions.supportVector.SMOset: Creates a new set of the given size.
solve(double[]) - Method in class weka.core.Matrix: Solve A*X = B using backward substitution.
solveTriangle(Matrix, double[], boolean, boolean[]) - Static method in class weka.core.Optimization: Solve the linear equation of TX=B where T is a triangle matrix It can be solved using back/forward substitution, with O(N^2) complexity
sort() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Sorts the point values of the discrete function.
sort() - Method in class weka.classifiers.functions.pace.DoubleVector: Sorts the array in place
sort() - Method in class weka.classifiers.functions.pace.IntVector: Sorts the elements in place
sort(Attribute) - Method in class weka.core.Instances: Sorts the instances based on an attribute.
sort(Comparator) - Method in class weka.associations.tertius.SimpleLinkedList
sort(double[]) - Static method in class weka.core.Utils: Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(int) - Method in class weka.core.Instances: Sorts the instances based on an attribute.
sort(int[]) - Static method in class weka.core.Utils: Sorts a given array of integers in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sortIndexedDoubles(double[]) - Static method in class coreComponents.GeneralUtils: Sorts and indexed array of doubles returns array from lowest to highest
sortIndexedDoubles(DoubleWithIndex[]) - Static method in class coreComponents.GeneralUtils: Sorts and indexed array of doubles returns array from lowest to highest
sortWithIndex() - Method in class weka.classifiers.functions.pace.DoubleVector: Sorts the array in place with index returned
sortWithIndex(int, int, IntVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Sorts the array in place with index changed
Sourcable - interface weka.classifiers.Sourcable.: Interface for classifiers that can be converted to Java source.
sourceClass(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit: Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Method not in use
SOUTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
sparseDataTipText() - Method in class weka.experiment.InstanceQuery: Returns the tip text for this property
sparseIndices() - Method in class weka.classifiers.functions.SMO: Returns the indices in sparse format.
sparseIndices() - Method in class classifiers.PC_SMO: Returns the indices in sparse format.
sparseIndices() - Method in class classifiers.AlphaProb_SMO: Returns the indices in sparse format.
SparseInstance - class weka.core.SparseInstance.: Class for storing an instance as a sparse vector.
SparseInstance(double, double[]) - Constructor for class weka.core.SparseInstance: Constructor that generates a sparse instance from the given parameters.
SparseInstance(double, double[], int[], int) - Constructor for class weka.core.SparseInstance: Constructor that inititalizes instance variable with given values.
SparseInstance(Instance) - Constructor for class weka.core.SparseInstance: Constructor that generates a sparse instance from the given instance.
SparseInstance(int) - Constructor for class weka.core.SparseInstance: Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
SparseInstance(SparseInstance) - Constructor for class weka.core.SparseInstance: Constructor that copies the info from the given instance.
SparseToNonSparse - class weka.filters.unsupervised.instance.SparseToNonSparse.: A filter that converts all incoming sparse instances into non-sparse format.
SparseToNonSparse() - Constructor for class weka.filters.unsupervised.instance.SparseToNonSparse
sparseWeights() - Method in class weka.classifiers.functions.SMO: Returns the weights in sparse format.
sparseWeights() - Method in class classifiers.PC_SMO: Returns the weights in sparse format.
sparseWeights() - Method in class classifiers.AlphaProb_SMO: Returns the weights in sparse format.
SpecialFunctions - class weka.core.SpecialFunctions.: Class implementing some mathematical functions.
SpecialFunctions() - Constructor for class weka.core.SpecialFunctions
sphere - Variable in class weka.classifiers.lazy.kstar.KStarWrapper: used/reused to hold the sphere size
split() - Method in class weka.classifiers.trees.m5.RuleNode: Finds an attribute and split point for this node
split(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Splits the given set of instances into subsets.
splitAtt() - Method in class weka.classifiers.trees.m5.RuleNode: Get the index of the splitting attribute for this node
splitAttr() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the attribute used in this split
splitAttr() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Returns the attribute used in this split
splitAttr() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the attribute used in this split
SplitCriterion - class weka.classifiers.trees.j48.SplitCriterion.: Abstract class for computing splitting criteria with respect to distributions of class values.
SplitCriterion() - Constructor for class weka.classifiers.trees.j48.SplitCriterion
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit: This method is a straightforward implementation of the information gain criterion for the given distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit: This method is a straightforward implementation of the gain ratio criterion for the given distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit: Computes entropy for given distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion: Computes result of splitting criterion for given distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit: Computes entropy of test distribution with respect to training distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions.
splitCritValue(Distribution, Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions and given default distribution.
splitCritValue(Distribution, Distribution, int) - Method in class weka.classifiers.trees.j48.SplitCriterion: Computes result of splitting criterion for given training and test distributions and given number of classes.
splitCritValue(Distribution, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit: This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit: This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit: This method computes the gain ratio in the same way C4.5 does.
splitEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit: Computes entropy after splitting without considering the class values.
SplitEvaluate - interface weka.classifiers.trees.m5.SplitEvaluate.: Interface for objects that determine a split point on an attribute
SplitEvaluator - interface weka.experiment.SplitEvaluator.: Interface to objects able to generate a fixed set of results for a particular split of a dataset.
splitEvaluatorTipText() - Method in class weka.experiment.CrossValidationResultProducer: Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
splitOnResidualsTipText() - Method in class weka.classifiers.trees.LMT: Returns the tip text for this property
splitOptions(String) - Static method in class weka.core.Utils: Split up a string containing options into an array of strings, one for each option.
splitPointTipText() - Method in class weka.filters.unsupervised.instance.RemoveWithValues: Returns the tip text for this property
Splitter - class weka.classifiers.trees.adtree.Splitter.: Abstract class representing a splitter node in an alternating tree.
Splitter() - Constructor for class weka.classifiers.trees.adtree.Splitter
splitVal() - Method in class weka.classifiers.trees.m5.RuleNode: Get the split point for this node
splitValue() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo: Returns the split value
splitValue() - Method in interface weka.classifiers.trees.m5.SplitEvaluate: Returns the split value
splitValue() - Method in class weka.classifiers.trees.m5.YongSplitInfo: Returns the split value
SpreadSubsample - class weka.filters.supervised.instance.SpreadSubsample.: Produces a random subsample of a dataset.
SpreadSubsample() - Constructor for class weka.filters.supervised.instance.SpreadSubsample
sqrt() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the square-root of all the elements in the vector
square() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the squared vector
square(double) - Static method in class weka.classifiers.functions.pace.Maths: Returns the square of a value
stableSort(double[]) - Static method in class weka.core.Utils: Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
Stacking - class weka.classifiers.meta.Stacking.: Implements stacking.
Stacking() - Constructor for class weka.classifiers.meta.Stacking
StackingC - class weka.classifiers.meta.StackingC.: Implements StackingC (more efficient version of stacking).
StackingC() - Constructor for class weka.classifiers.meta.StackingC: The constructor.
Standardize - class weka.filters.unsupervised.attribute.Standardize.: Standardizes all numeric attributes in the given dataset to have zero mean and unit variance.
Standardize() - Constructor for class weka.filters.unsupervised.attribute.Standardize
start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Start the plotting thread
start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed: Start processing
startLoading() - Method in class weka.gui.beans.Loader: Start loading data
StartSetHandler - interface weka.attributeSelection.StartSetHandler.: Interface for search methods capable of doing something sensible given a starting set of attributes.
startSetTipText() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.ForwardSelection: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.BestFirst: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.GeneticSearch: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.RandomSearch: Returns the tip text for this property
Statistics - class weka.core.Statistics.: Class implementing some distributions, tests, etc.
Statistics() - Constructor for class weka.core.Statistics
Stats - class weka.experiment.Stats.: A class to store simple statistics
Stats - class weka.classifiers.trees.j48.Stats.: Class implementing a statistical routine needed by J48 to compute its error estimate.
Stats() - Constructor for class weka.experiment.Stats
Stats() - Constructor for class weka.classifiers.trees.j48.Stats
statusMessage(String) - Method in class weka.gui.LogPanel: Sends the supplied message to the status line.
statusMessage(String) - Method in class weka.gui.SysErrLog: Sends the supplied message to the status line.
statusMessage(String) - Method in interface weka.gui.Logger: Sends the supplied message to the status line.
stdDev - Variable in class weka.experiment.Stats: The std deviation of values at the last calculateDerived() call
STEP_FIELD_NAME - Static variable in class weka.experiment.LearningRateResultProducer
steplsqr(PaceMatrix, IntVector, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Stepwise least squares QR-decomposition of the problem A x = b
stepSizeTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
stop() - Method in class weka.gui.beans.TrainingSetMaker: Stop any action
stop() - Method in class weka.gui.beans.AbstractTrainingSetProducer: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.TrainTestSplitMaker: Stop processing
stop() - Method in class weka.gui.beans.StripChart: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.Classifier: Stop any classifier action
stop() - Method in class weka.gui.beans.Filter: Stop all action if possible
stop() - Method in interface weka.gui.beans.BeanCommon: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.IncrementalClassifierEvaluator: Stop all action
stop() - Method in class weka.gui.beans.AbstractDataSink: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.CSVDataSink
stop() - Method in class weka.gui.beans.PredictionAppender
stop() - Method in class weka.gui.beans.ClassAssigner
stop() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator: Try and stop any action
stop() - Method in class weka.gui.beans.TestSetMaker
stop() - Method in class weka.gui.beans.AbstractEvaluator: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTestSetProducer: Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.CrossValidationFoldMaker: Stop any action
stopPlotting() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Stop the plotting thread
Stopwords - class weka.core.Stopwords.: Class that can test whether a given string is a stop word.
Stopwords() - Constructor for class weka.core.Stopwords
store(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache: Stores the specified values in the cahce table for easy retrieval.
StratifiedRemoveFolds - class weka.filters.supervised.instance.StratifiedRemoveFolds.: This filter takes a dataset and outputs folds suitable for cross validation.
StratifiedRemoveFolds() - Constructor for class weka.filters.supervised.instance.StratifiedRemoveFolds
stratify(Instances, int, Random) - Static method in class weka.classifiers.rules.RuleStats: Stratify the given data into the given number of bags based on the class values.
stratify(int) - Method in class weka.core.Instances: Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).
StreamableFilter - interface weka.filters.StreamableFilter.: Interface for filters can work with a stream of instances.
STRING - Static variable in class weka.core.Attribute: Constant set for attributes with string values.
STRING - Static variable in class weka.experiment.DatabaseUtils
stringFreeStructure() - Method in class weka.core.Instances: Create a copy of the structure, but "cleanse" string types (i.e.
stringSize(FontMetrics) - Method in class weka.gui.treevisualizer.Node: This will return the width and height of the rectangle that the text will fit into.
stringSize(FontMetrics) - Method in class weka.gui.treevisualizer.Edge: This will calculate how large a rectangle using the FontMetrics passed that the lines of the label will take up
StringToNominal - class weka.filters.unsupervised.attribute.StringToNominal.: Converts a string attribute (i.e.
StringToNominal() - Constructor for class weka.filters.unsupervised.attribute.StringToNominal
StringToWordVector - class weka.filters.unsupervised.attribute.StringToWordVector.: Converts String attributes into a set of attributes representing word occurrence information from the text contained in the strings.
StringToWordVector() - Constructor for class weka.filters.unsupervised.attribute.StringToWordVector: Default constructor.
StringToWordVector(int) - Constructor for class weka.filters.unsupervised.attribute.StringToWordVector: Constructor that allows specification of the target number of words in the output.
stringValue(Attribute) - Method in class weka.core.Instance: Returns the string value of a nominal, string, or date attribute for the instance.
stringValue(int) - Method in class weka.core.Instance: Returns the string value of a nominal, string, or date attribute for the instance.
StripChart - class weka.gui.beans.StripChart.: Bean that can display a horizontally scrolling strip chart.
StripChart() - Constructor for class weka.gui.beans.StripChart
StripChartBeanInfo - class weka.gui.beans.StripChartBeanInfo.: Bean info class for the strip chart bean
StripChartBeanInfo() - Constructor for class weka.gui.beans.StripChartBeanInfo
StripChartCustomizer - class weka.gui.beans.StripChartCustomizer.: GUI Customizer for the strip chart bean
StripChartCustomizer() - Constructor for class weka.gui.beans.StripChartCustomizer
sub(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution: Subtracts given instance from given bag.
subsetDL(double, double, double) - Static method in class weka.classifiers.rules.RuleStats: Subset description length:
S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95
subsetEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Returns the estimate of optimal subset selection.
SubsetEvaluator - class weka.attributeSelection.SubsetEvaluator.: Abstract attribute subset evaluator.
SubsetEvaluator() - Constructor for class weka.attributeSelection.SubsetEvaluator
subsumes(Rule) - Method in class weka.associations.tertius.Rule: Test if this rule subsumes another rule.
subsumptionTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
subtract(Distribution) - Method in class weka.classifiers.trees.j48.Distribution: Subtracts the given distribution from this one.
subtract(double) - Method in class weka.experiment.Stats: Removes a value to the observed values (no checking is done that the value being removed was actually added).
subtract(double, double) - Method in class weka.experiment.Stats: Subtracts a value that has been seen n times from the observed values
subtract(double, double) - Method in class weka.experiment.PairedStats: Removes an observed pair of values.
subtract(ItemSet) - Method in class weka.associations.ItemSet: Subtracts an item set from another one.
subtreeRaisingTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
subvector(int, int) - Method in class weka.classifiers.functions.pace.DoubleVector: Returns a subvector.
subvector(int, int) - Method in class weka.classifiers.functions.pace.IntVector: Returns a subvector.
subvector(IntVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Returns a subvector.
subvector(IntVector) - Method in class weka.classifiers.functions.pace.IntVector: Returns a subvector as indexed by an IntVector.
sum - Variable in class weka.experiment.Stats: The sum of values seen
sum() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the sum of all elements in the vector.
sum(double[]) - Static method in class weka.core.Utils: Computes the sum of the elements of an array of doubles.
sum(int[]) - Static method in class weka.core.Utils: Computes the sum of the elements of an array of integers.
sum2() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns the squared sum of all elements in the vector.
sum2(boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Squared sum of columns or rows of a matrix
sum2(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Returns ||u-v||^2
sum2(int, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Squared sum of a column or row in a matrix
Summarizable - interface weka.core.Summarizable.: Interface to something that provides a short textual summary (as opposed to toString() which is usually a fairly complete description) of itself.
sumOfWeights() - Method in class weka.core.Instances: Computes the sum of all the instances' weights.
sumSq - Variable in class weka.experiment.Stats: The sum of values squared seen
SupervisedFilter - interface weka.filters.SupervisedFilter.: Interface for filters that make use of a class attribute.
support() - Method in class weka.associations.ItemSet: Outputs the support for an item set.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution: Contructs the set of support points for mixture estimation.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.ChisqMixture: Contructs the set of support points for mixture estimation.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.NormalMixture: Contructs the set of support points for mixture estimation.
supportsCustomEditor() - Method in class weka.gui.FileEditor: Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.GenericObjectEditor: Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.GenericArrayEditor: Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.CostMatrixEditor: Indicates whether the cost matrix can be edited in a GUI, which it can.
SVMAttributeEval - class weka.attributeSelection.SVMAttributeEval.: Class for Evaluating attributes individually by using the SVM classifier.
SVMAttributeEval() - Constructor for class weka.attributeSelection.SVMAttributeEval: Constructor
SVMToArff - class coreComponents.SVMToArff.: Here is a boring and pointless application for converting the primitive SVM data format to the nice WEKA arff format.
SVMToArff() - Constructor for class coreComponents.SVMToArff
swap(int, int) - Method in class weka.core.FastVector: Swaps two elements in the vector.
swap(int, int) - Method in class weka.classifiers.functions.pace.DoubleVector: Swaps the values stored at i and j
swap(int, int) - Method in class weka.classifiers.functions.pace.IntVector: Swaps the values stored at i and j
SwapValues - class weka.filters.unsupervised.attribute.SwapValues.: Swaps two values of a nominal attribute.
SwapValues() - Constructor for class weka.filters.unsupervised.attribute.SwapValues
switchToAdvanced(Experiment) - Method in class weka.gui.experiment.SetupModePanel: Switches to the advanced setup mode.
switchToSimple(Experiment) - Method in class weka.gui.experiment.SetupModePanel: Switches to the simple setup mode only if allowed to.
symmetricalUncertainty(double[][]) - Static method in class weka.core.ContingencyTables: Calculates the symmetrical uncertainty for base 2.
SymmetricalUncertAttributeEval - class weka.attributeSelection.SymmetricalUncertAttributeEval.: Class for Evaluating attributes individually by measuring symmetrical uncertainty with respect to the class.
SymmetricalUncertAttributeEval() - Constructor for class weka.attributeSelection.SymmetricalUncertAttributeEval: Constructor
synopsis() - Method in class weka.core.Option: Returns the option's synopsis.
SysErrLog - class weka.gui.SysErrLog.: This Logger just sends messages to System.err.
SysErrLog() - Constructor for class weka.gui.SysErrLog

T

tableExists(String) - Method in class weka.experiment.DatabaseUtils: Checks that a given table exists.
Tag - class weka.core.Tag.: A Tag simply associates a numeric ID with a String description.
Tag(int, String) - Constructor for class weka.core.Tag: Creates a new Tag instance.
TAGS_ATTRIBUTETYPE - Static variable in class weka.filters.unsupervised.attribute.RemoveType: Tag allowing selection of attribute type to delete
TAGS_DSTRS_TYPE - Static variable in class weka.filters.unsupervised.attribute.RandomProjection
TAGS_ESTIMATOR - Static variable in class weka.classifiers.functions.PaceRegression
TAGS_EVAL - Static variable in class weka.classifiers.meta.ThresholdSelector
TAGS_FILTER - Static variable in class weka.classifiers.functions.SMO
TAGS_FILTER - Static variable in class weka.classifiers.functions.SMOreg
TAGS_FILTER - Static variable in class classifiers.PC_SMO
TAGS_FILTER - Static variable in class classifiers.AlphaProb_SMO
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.meta.MetaCost
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
TAGS_METHOD - Static variable in class weka.classifiers.meta.MultiClassClassifier
TAGS_MISSING - Static variable in class weka.classifiers.lazy.KStar: Define possible missing value handling methods
TAGS_ONLINE_MODE - Static variable in class evaluationMethods.OnlineEvaluation
TAGS_OPTIMIZE - Static variable in class weka.classifiers.meta.ThresholdSelector
TAGS_PRUNETYPE - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
TAGS_RANGE - Static variable in class weka.classifiers.meta.ThresholdSelector
TAGS_SCORE_TYPE - Static variable in class weka.classifiers.bayes.BayesNet
TAGS_SEARCHPATH - Static variable in class weka.classifiers.trees.ADTree
TAGS_SELECTION - Static variable in class weka.attributeSelection.BestFirst
TAGS_SELECTION - Static variable in class weka.attributeSelection.RaceSearch
TAGS_SELECTION - Static variable in class weka.associations.Apriori
TAGS_SELECTION - Static variable in class weka.classifiers.functions.LinearRegression
TAGS_WEIGHTING - Static variable in class weka.classifiers.lazy.IBk
TAGS_WEIGHTING - Static variable in class classifiers.AltDist_IBk
Task - interface weka.experiment.Task.: Interface to something that can be remotely executed as a task.
taskFinished() - Method in interface weka.gui.TaskLogger: Tells the task logger that a task has completed
taskFinished() - Method in class weka.gui.LogPanel: Record a task ending
taskFinished() - Method in class weka.gui.WekaTaskMonitor: Tells the panel that a task has completed
TaskLogger - interface weka.gui.TaskLogger.: Interface for objects that display log and display information on running tasks.
taskStarted() - Method in interface weka.gui.TaskLogger: Tells the task logger that a new task has been started
taskStarted() - Method in class weka.gui.LogPanel: Record the starting of a new task
taskStarted() - Method in class weka.gui.WekaTaskMonitor: Tells the panel that a new task has been started
TaskStatusInfo - class weka.experiment.TaskStatusInfo.: A class holding information for tasks being executed on RemoteEngines.
TaskStatusInfo() - Constructor for class weka.experiment.TaskStatusInfo
tauVal(double[][]) - Static method in class weka.core.ContingencyTables: Computes Goodman and Kruskal's tau-value for a contingency table.
TAXONOMY_CHOICE - Static variable in class probabilityMachine.vpm.VPMKNearestNeighbours
TCMBartsRMI - class confidenceMachine.tcm.TCMBartsRMI.: The TCM Barts RMI algorithm
TCMBartsRMI() - Constructor for class confidenceMachine.tcm.TCMBartsRMI: The amazing TCM Barts RMI classifier
TCMBartsRMI(double) - Constructor for class confidenceMachine.tcm.TCMBartsRMI: The amazing TCM Barts RMI classifier
TCMKNearestNeighbours - class confidenceMachine.tcm.TCMKNearestNeighbours.: The TCM K-Nearest Neighbours algorithm.
TCMKNearestNeighbours() - Constructor for class confidenceMachine.tcm.TCMKNearestNeighbours: The amazing TCM K Nearest Neighbours classifier
TCMKNearestNeighbours(int) - Constructor for class confidenceMachine.tcm.TCMKNearestNeighbours: The amazing TCM K Nearest Neighbours classifier
Tertius - class weka.associations.Tertius.: Class implementing a Tertius-type algorithm.
Tertius() - Constructor for class weka.associations.Tertius: Constructor that sets the options to the default values.
Test - class weka.datagenerators.Test.: Class to represent a test.

The string representation of the test can be supplied in standard notation or for a subset of types of attributes in Prolog notation.
Following examples for all possible tests that can be represented by this class, given in standard notation.

Examples of tests for numeric attributes:
B >= 2.333
B < 4.56

Examples of tests for nominal attributes with more then 2 values:
A = rain
A != rain

Examples of tests for nominal attribute with exactly 2 values:
A = false
A = true

The Prolog notation is only supplied for numeric attributes and for nominal attributes that have the values "true" and "false".

Following examples for the Prolog notation provided.

Examples of tests for numeric attributes:
The same as for standard notation above.

Examples of tests for nominal attributes with values "true"and "false":
A
not(A)

(Other nominal attributes are not supported by the Prolog notation.)
test(String[]) - Static method in class weka.core.Instances: Method for testing this class.
testCV(int, int) - Method in class weka.core.Instances: Creates the test set for one fold of a cross-validation on the dataset.
testEigen(Matrix, double[], boolean) - Method in class weka.core.Matrix: Test eigenvectors and eigenvalues.
TestSetEvent - class weka.gui.beans.TestSetEvent.: Event encapsulating a test set
TestSetEvent(Object, Instances) - Constructor for class weka.gui.beans.TestSetEvent
TestSetListener - interface weka.gui.beans.TestSetListener.: Interface to something that can accpet test set events
TestSetMaker - class weka.gui.beans.TestSetMaker.: Bean that accepts data sets and produces test sets
TestSetMaker() - Constructor for class weka.gui.beans.TestSetMaker
TestSetMakerBeanInfo - class weka.gui.beans.TestSetMakerBeanInfo.: Bean info class for the test set maker bean.
TestSetMakerBeanInfo() - Constructor for class weka.gui.beans.TestSetMakerBeanInfo
TestSetProducer - interface weka.gui.beans.TestSetProducer.: Interface to something that can produce test sets
TextEvent - class weka.gui.beans.TextEvent.: Event that encapsulates some textual information
TextEvent(Object, String, String) - Constructor for class weka.gui.beans.TextEvent: Creates a new TextEvent instance.
TextListener - interface weka.gui.beans.TextListener.: Interface to something that can process a TextEvent
TextViewer - class weka.gui.beans.TextViewer.: Bean that collects and displays pieces of text
TextViewer() - Constructor for class weka.gui.beans.TextViewer
TextViewerBeanInfo - class weka.gui.beans.TextViewerBeanInfo.: Bean info class for the text viewer
TextViewerBeanInfo() - Constructor for class weka.gui.beans.TextViewerBeanInfo
TFTransformTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property
theoryDL(int) - Method in class weka.classifiers.rules.RuleStats: The description length of the theory for a given rule.
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
ThresholdCurve - class weka.classifiers.evaluation.ThresholdCurve.: Generates points illustrating prediction tradeoffs that can be obtained by varying the threshold value between classes.
ThresholdCurve() - Constructor for class weka.classifiers.evaluation.ThresholdCurve
ThresholdSelector - class weka.classifiers.meta.ThresholdSelector.: Class for selecting a threshold on a probability output by a distribution classifier.
ThresholdSelector() - Constructor for class weka.classifiers.meta.ThresholdSelector
thresholdTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.ForwardSelection: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
thresholdTipText() - Method in class weka.classifiers.functions.Winnow: Returns the tip text for this property
thresholdTipText() - Method in class weka.classifiers.functions.PaceRegression: Returns the tip text for this property
ThresholdVisualizePanel - class weka.gui.visualize.ThresholdVisualizePanel.: This panel is a VisualizePanel, with the added ablility to display the area under the ROC curve if an ROC curve is chosen.
ThresholdVisualizePanel() - Constructor for class weka.gui.visualize.ThresholdVisualizePanel
times(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Multiplies a scalar
times(double) - Method in class weka.classifiers.functions.pace.Matrix: Multiply a matrix by a scalar, C = s*A
times(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Multiplies another DoubleVector element by element
times(int, int, int, PaceMatrix, int) - Method in class weka.classifiers.functions.pace.PaceMatrix: Multiplication between a row (or part of a row) of the first matrix and a column (or part or a column) of the second matrix.
times(Matrix) - Method in class weka.classifiers.functions.pace.Matrix: Linear algebraic matrix multiplication, A * B
timesEquals(double) - Method in class weka.classifiers.functions.pace.DiscreteFunction: All function values are multiplied by a double
timesEquals(double) - Method in class weka.classifiers.functions.pace.DoubleVector: Multiply a vector by a scalar in place, u = s * u
timesEquals(double) - Method in class weka.classifiers.functions.pace.Matrix: Multiply a matrix by a scalar in place, A = s*A
timesEquals(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector: Multiplies another DoubleVector element by element in place
TimeSeriesDelta - class weka.filters.unsupervised.attribute.TimeSeriesDelta.: An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
TimeSeriesDelta() - Constructor for class weka.filters.unsupervised.attribute.TimeSeriesDelta
TimeSeriesTranslate - class weka.filters.unsupervised.attribute.TimeSeriesTranslate.: An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute attribute values of some previous (or future) instance.
TimeSeriesTranslate() - Constructor for class weka.filters.unsupervised.attribute.TimeSeriesTranslate
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
TO_BE_RUN - Static variable in class weka.experiment.TaskStatusInfo
toArray() - Method in class weka.core.FastVector: Returns all the elements of this vector as an array
toCalibrationHistogramString() - Method in class evaluationMethods.EstimatorEvaluation: Outputs the probability calibration histogram of the data
toCalibrationHistogramString() - Method in class evaluationMethods.OnlineEvaluation: Outputs the probability calibration histogram of the data
toClassDetailsString() - Method in class weka.classifiers.Evaluation
toClassDetailsString() - Method in class evaluationMethods.EstimatorEvaluation
toClassDetailsString(String) - Method in class weka.classifiers.Evaluation: Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
toClassDetailsString(String) - Method in class evaluationMethods.EstimatorEvaluation: Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
toCumulativeMarginDistributionString() - Method in class weka.classifiers.Evaluation: Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
toCumulativeMarginDistributionString() - Method in class evaluationMethods.EstimatorEvaluation: Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
toDoubleArray() - Method in class weka.core.Instance: Returns the values of each attribute as an array of doubles.
toDoubleArray() - Method in class weka.core.BinarySparseInstance: Returns the values of each attribute as an array of doubles.
toDoubleArray() - Method in class weka.core.SparseInstance: Returns the values of each attribute as an array of doubles.
toGraph() - Method in class weka.classifiers.trees.RandomTree: Outputs the decision tree as a graph
toGraph(StringBuffer, int) - Method in class weka.classifiers.trees.RandomTree: Outputs one node for graph.
tokenize(String) - Method in class weka.gui.HierarchyPropertyParser: Tokenize the given string based on the seperator and put the tokens into an array of strings
toleranceParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval: Returns a tip text for this property suitable for display in the GUI
toleranceParameterTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
toleranceParameterTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
toleranceParameterTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
toleranceParameterTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
toMatrixString() - Method in class weka.classifiers.Evaluation: Calls toMatrixString() with a default title.
toMatrixString() - Method in class evaluationMethods.EstimatorEvaluation: Calls toMatrixString() with a default title.
toMatrixString(String) - Method in class weka.classifiers.Evaluation: Outputs the performance statistics as a classification confusion matrix.
toMatrixString(String) - Method in class evaluationMethods.EstimatorEvaluation: Outputs the performance statistics as a classification confusion matrix.
toOfflineConfidenceCalibrationHistogramString() - Method in class evaluationMethods.EstimatorEvaluation: Outputs the confidence calibration histogram of the data, in the offline learning setting
topOfTree() - Method in class weka.classifiers.trees.m5.Rule: Returns the top of the tree.
toPrologString() - Method in class weka.datagenerators.Test: Returns the test represented by a string in Prolog notation.
toResultsString() - Method in class weka.attributeSelection.AttributeSelection: get a description of the attribute selection
toSource(String) - Method in interface weka.classifiers.Sourcable: Returns a string that describes the classifier as source.
toSource(String) - Method in class weka.classifiers.meta.AdaBoostM1: Returns the boosted model as Java source code.
toSource(String) - Method in class weka.classifiers.meta.LogitBoost: Returns the boosted model as Java source code.
toSource(String) - Method in class weka.classifiers.trees.J48: Returns tree as an if-then statement.
toSource(String) - Method in class weka.classifiers.trees.DecisionStump: Returns the decision tree as Java source code.
toSource(String) - Method in class weka.classifiers.trees.REPTree: Returns the tree as if-then statements.
toSource(String) - Method in class weka.classifiers.trees.j48.ClassifierTree: Returns source code for the tree as an if-then statement.
toString() - Method in class weka.gui.graphvisualizer.GraphEdge
toString() - Method in class weka.core.Instance: Returns the description of one instance.
toString() - Method in class weka.core.BinarySparseInstance: Returns the description of one instance in sparse format.
toString() - Method in class weka.core.Attribute: Returns a description of this attribute in ARFF format.
toString() - Method in class weka.core.SparseInstance: Returns the description of one instance in sparse format.
toString() - Method in class weka.core.Instances: Returns the dataset as a string in ARFF format.
toString() - Method in class weka.core.AttributeStats: Returns a human readable representation of this AttributeStats instance.
toString() - Method in class weka.core.Range: Constructs a representation of the current range.
toString() - Method in class weka.core.Matrix: Converts a matrix to a string
toString() - Method in class weka.core.Queue: Produces textual description of queue.
toString() - Method in class weka.core.SingleIndex: Constructs a representation of the current range.
toString() - Method in class weka.clusterers.SimpleKMeans: return a string describing this clusterer
toString() - Method in class weka.clusterers.MakeDensityBasedClusterer: Returns a description of the clusterer.
toString() - Method in class weka.clusterers.EM: Outputs the generated clusters into a string.
toString() - Method in class weka.clusterers.FarthestFirst: return a string describing this clusterer
toString() - Method in class weka.clusterers.Cobweb: Returns a description of the clusterer as a string.
toString() - Method in class weka.datagenerators.Test: Returns the test represented by a string.
toString() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.AveragingResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.Experiment: Gets a string representation of the experiment configuration.
toString() - Method in class weka.experiment.Stats: Returns a string summarising the stats so far.
toString() - Method in class weka.experiment.ClassifierSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.PropertyNode: Returns a string description of this property.
toString() - Method in class weka.experiment.LearningRateResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.PairedStats: Returns statistics on the paired comparison.
toString() - Method in class weka.experiment.RegressionSplitEvaluator: Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CrossValidationResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.RandomSplitResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DatabaseResultProducer: Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.RemoteExperiment: Overides toString in Experiment
toString() - Method in class weka.estimators.DiscreteEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KKConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.NNConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DKConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.PoissonEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.MahalanobisEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.KernelEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.NDConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.NormalEstimator: Display a representation of this estimator
toString() - Method in class weka.estimators.DNConditionalEstimator: Display a representation of this estimator
toString() - Method in class weka.attributeSelection.InfoGainAttributeEval: Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.ExhaustiveSearch: prints a description of the search
toString() - Method in class weka.attributeSelection.CfsSubsetEval: returns a string describing CFS
toString() - Method in class weka.attributeSelection.ReliefFAttributeEval: Return a description of the ReliefF attribute evaluator.
toString() - Method in class weka.attributeSelection.ForwardSelection: returns a description of the search.
toString() - Method in class weka.attributeSelection.SVMAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.RankSearch: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.ChiSquaredAttributeEval: Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.GainRatioAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.ConsistencySubsetEval: returns a description of the evaluator
toString() - Method in class weka.attributeSelection.PrincipalComponents: Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.BestFirst: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.BestFirst.Link2
toString() - Method in class weka.attributeSelection.OneRAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.GeneticSearch: returns a description of the search
toString() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Return a description of the evaluator
toString() - Method in class weka.attributeSelection.WrapperSubsetEval: Returns a string describing the wrapper
toString() - Method in class weka.attributeSelection.Ranker: returns a description of the search as a String
toString() - Method in class weka.attributeSelection.RaceSearch
toString() - Method in class weka.attributeSelection.RandomSearch: prints a description of the search
toString() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns a string describing classifierSubsetEval
toString() - Method in class weka.associations.Apriori: Outputs the size of all the generated sets of itemsets and the rules.
toString() - Method in class weka.associations.Tertius: Outputs the best rules found with their confirmation value and number of counter-instances.
toString() - Method in class weka.associations.tertius.Literal
toString() - Method in class weka.associations.tertius.Rule: Retrun a String for this rule.
toString() - Method in class weka.associations.tertius.Head: Gives a String representation of this set of literals as a disjunction.
toString() - Method in class weka.associations.tertius.SimpleLinkedList
toString() - Method in class weka.associations.tertius.Predicate
toString() - Method in class weka.associations.tertius.LiteralSet: Gives a String representation for this set of literals.
toString() - Method in class weka.associations.tertius.AttributeValueLiteral
toString() - Method in class weka.associations.tertius.Body: Gives a String representation of this set of literals as a conjunction.
toString() - Method in class weka.classifiers.BVDecompose: Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.BVDecomposeSegCVSub: Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.lazy.LBR: Returns a description of the classifier.
toString() - Method in class weka.classifiers.lazy.LWL: Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.IB1: Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.KStar: Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.IBk: Returns a description of this classifier.
toString() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.Bagging: Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.CVParameterSelection: Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.meta.MetaCost: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.MultiScheme: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Grading: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.ClassificationViaRegression: Prints the classifiers.
toString() - Method in class weka.classifiers.meta.ThresholdSelector: Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.meta.FilteredClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.MultiClassClassifier: Prints the classifiers.
toString() - Method in class weka.classifiers.meta.AdditiveRegression: Returns textual description of the classifier.
toString() - Method in class weka.classifiers.meta.Vote: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Stacking: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.MultiBoostAB: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.AttributeSelectedClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Decorate: Returns description of the Decorate classifier.
toString() - Method in class weka.classifiers.meta.AdaBoostM1: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.RandomCommittee: Returns description of the committee.
toString() - Method in class weka.classifiers.meta.StackingC: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.LogitBoost: Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.CostSensitiveClassifier: Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.RegressionByDiscretization: Returns a description of the classifier.
toString() - Method in class weka.classifiers.meta.OrdinalClassClassifier: Prints the classifiers.
toString() - Method in class weka.classifiers.misc.HyperPipes: Returns a description of this classifier.
toString() - Method in class weka.classifiers.misc.FLR: Returns a description of the classifier.
toString() - Method in class weka.classifiers.misc.VFI: Returns a description of this classifier.
toString() - Method in class weka.classifiers.bayes.BayesNet: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.AODE: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.NaiveBayesSimple: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.NaiveBayesMultinomial
toString() - Method in class weka.classifiers.bayes.NaiveBayes: Returns a description of the classifier.
toString() - Method in class weka.classifiers.bayes.DiscreteEstimatorBayes: Display a representation of this estimator
toString() - Method in class weka.classifiers.bayes.ComplementNaiveBayes: Prints out the internal model built by the classifier.
toString() - Method in class weka.classifiers.rules.ZeroR: Returns a description of the classifier.
toString() - Method in class weka.classifiers.rules.JRip: Prints the all the rules of the rule learner.
toString() - Method in class weka.classifiers.rules.ConjunctiveRule: Prints this rule
toString() - Method in class weka.classifiers.rules.OneR: Returns a description of the classifier
toString() - Method in class weka.classifiers.rules.Prism: Prints a description of the classifier.
toString() - Method in class weka.classifiers.rules.PART: Returns a description of the classifier
toString() - Method in class weka.classifiers.rules.DecisionTable: Returns a description of the classifier.
toString() - Method in class weka.classifiers.rules.DecisionTable.Link: Returns string representation.
toString() - Method in class weka.classifiers.rules.Ridor: Prints the all the rules of the rule learner.
toString() - Method in class weka.classifiers.rules.NNge: Returns a description of this classifier.
toString() - Method in class weka.classifiers.rules.part.MakeDecList: Outputs the classifier into a string.
toString() - Method in class weka.classifiers.rules.part.ClassifierDecList: Prints rules.
toString() - Method in class weka.classifiers.trees.J48: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.ADTree: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.RandomForest: Outputs a description of this classifier.
toString() - Method in class weka.classifiers.trees.DecisionStump: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.UserClassifier
toString() - Method in class weka.classifiers.trees.Id3: Prints the decision tree using the private toString method from below.
toString() - Method in class weka.classifiers.trees.REPTree: Outputs the decision tree.
toString() - Method in class weka.classifiers.trees.LMT: Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.RandomTree: Outputs the decision tree.
toString() - Method in class weka.classifiers.trees.m5.Rule: Return a description of the m5 tree or rule
toString() - Method in class weka.classifiers.trees.m5.PreConstructedLinearModel: Returns a textual description of this linear model
toString() - Method in class weka.classifiers.trees.m5.Impurity: Converts an Impurity object to a string
toString() - Method in class weka.classifiers.trees.m5.RuleNode: print the linear model at this node
toString() - Method in class weka.classifiers.trees.m5.M5Base: Returns a description of the classifier
toString() - Method in class weka.classifiers.trees.m5.Values: Converts the stats to a string
toString() - Method in class weka.classifiers.trees.j48.ClassifierTree: Prints tree structure.
toString() - Method in class weka.classifiers.trees.lmt.LMTNode: Returns a description of the logistic model tree (tree structure and logistic models)
toString() - Method in class weka.classifiers.trees.lmt.LogisticBase: Returns a description of the logistic model (i.e., attributes and coefficients).
toString() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Calls toString() with a default title.
toString() - Method in class weka.classifiers.evaluation.NumericPrediction: Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.TwoClassStats: Returns a string containing the various performance measures for the current object
toString() - Method in class weka.classifiers.functions.LinearRegression: Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.functions.SimpleLogistic: Returns a description of the logistic model (attributes/coefficients).
toString() - Method in class weka.classifiers.functions.SimpleLinearRegression: Returns a description of this classifier as a string
toString() - Method in class weka.classifiers.functions.LeastMedSq: Returns a string representing the best LinearRegression classifier found.
toString() - Method in class weka.classifiers.functions.MultilayerPerceptron
toString() - Method in class weka.classifiers.functions.VotedPerceptron: Returns textual description of classifier.
toString() - Method in class weka.classifiers.functions.SMO: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.Winnow: Returns textual description of the classifier.
toString() - Method in class weka.classifiers.functions.SMOreg: Prints out the classifier.
toString() - Method in class weka.classifiers.functions.Logistic: Gets a string describing the classifier.
toString() - Method in class weka.classifiers.functions.PaceRegression: Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.functions.RBFNetwork: Returns a description of this classifier as a String
toString() - Method in class weka.classifiers.functions.pace.MixtureDistribution: Converts to a string
toString() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Converts the discrete function to string.
toString() - Method in class weka.classifiers.functions.pace.DoubleVector: Convert the DoubleVecor to a string
toString() - Method in class weka.classifiers.functions.pace.PaceMatrix: Converts matrix to string
toString() - Method in class weka.classifiers.functions.pace.ChisqMixture: Converts to a string
toString() - Method in class weka.classifiers.functions.pace.IntVector: Converts the IntVecor to a string
toString() - Method in class weka.classifiers.functions.pace.NormalMixture: Converts to a string
toString() - Method in class confidenceMachine.tcm.TCMBartsRMI: Returns a description of this classifier.
toString() - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Returns a description of this classifier.
toString() - Method in class coreComponents.PatternCounter
toString() - Method in class coreComponents.PatternCounter.PatternObject
toString() - Method in class coreComponents.EuclideanDistanceMetric: Documents the content of an EuclideanDistance object in a string.
toString() - Method in class coreComponents.DistanceMetric: Creates a debugging string detailing the information about the distance metric
toString() - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Returns a description of this classifier.
toString() - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Returns a description of this classifier.
toString() - Method in class probabilityMachine.VPMDistMetaLearner: Returns a description of this classifier.
toString() - Method in class probabilityMachine.vpm.VPMNaiveBayes: Returns a description of this classifier.
toString() - Method in class probabilityMachine.vpm.VPMBartsRMI2: Returns a description of this classifier.
toString() - Method in class probabilityMachine.vpm.VPMBartsRMI: Returns a description of this classifier.
toString() - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Returns a description of this classifier.
toString() - Method in class classifiers.PC_SMO: Prints out the classifier.
toString() - Method in class classifiers.AlphaProb_SMO: Prints out the classifier.
toString() - Method in class classifiers.AlphaProb_SMO.BinarySMO: Prints out the classifier.
toString() - Method in class classifiers.AltDist_IBk: Returns a description of this classifier.
toString() - Method in class classifiers.vdm.ValueDifferenceMetric
toString() - Method in class classifiers.usm.distance.USMDistanceFunction: Converts a DistanceFunction object to a string
toString() - Method in class classifiers.usm.distance.USMComplexityCache: Converts a USMComplexityCache object to a string
toString(Attribute) - Method in class weka.core.Instance: Returns the description of one value of the instance as a string.
toString(Instances) - Method in class weka.associations.ItemSet: Returns the contents of an item set as a string.
toString(Instances) - Method in class weka.classifiers.trees.m5.YongSplitInfo: Converts the spliting information to string
toString(Instances, int) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey: Convert a hash entry to a string
toString(Instances, int) - Method in class weka.classifiers.rules.DecisionTable.hashKey: Convert a hash entry to a string
toString(int) - Method in class weka.core.Instance: Returns the description of one value of the instance as a string.
toString(int, boolean) - Method in class weka.classifiers.functions.pace.DoubleVector: Convert the DoubleVecor to a string
toString(int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix: Converts matrix to string
toString(int, boolean) - Method in class weka.classifiers.functions.pace.IntVector: Convert the IntVecor to a string
toString(String) - Method in class weka.classifiers.evaluation.ConfusionMatrix: Outputs the performance statistics as a classification confusion matrix.
toString(String, String) - Method in class weka.classifiers.rules.ConjunctiveRule: Prints this rule with the specified class label
toSummaryString() - Method in class weka.core.Instances: Generates a string summarizing the set of instances.
toSummaryString() - Method in interface weka.core.Summarizable: Returns a string that summarizes the object.
toSummaryString() - Method in class weka.classifiers.Evaluation: Calls toSummaryString() with no title and no complexity stats
toSummaryString() - Method in class weka.classifiers.meta.CVParameterSelection
toSummaryString() - Method in class weka.classifiers.misc.FLR: Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.rules.PART: Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.trees.J48: Returns a superconcise version of the model
toSummaryString() - Method in class evaluationMethods.EstimatorEvaluation: Calls toSummaryString() with no title and no complexity stats
toSummaryString(boolean) - Method in class weka.classifiers.Evaluation: Calls toSummaryString() with a default title.
toSummaryString(boolean) - Method in class evaluationMethods.EstimatorEvaluation: Calls toSummaryString() with a default title.
toSummaryString(String, boolean) - Method in class weka.classifiers.Evaluation: Outputs the performance statistics in summary form.
toSummaryString(String, boolean) - Method in class evaluationMethods.EstimatorEvaluation: Outputs the performance statistics in summary form.
total() - Method in class weka.classifiers.trees.j48.Distribution: Returns total number of (possibly fractional) instances.
total() - Method in class weka.classifiers.evaluation.ConfusionMatrix: Gets the number of predictions that were made (actually the sum of the weights of predictions where the class value was known).
totalCost() - Method in class weka.classifiers.Evaluation: Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
totalCost() - Method in class evaluationMethods.EstimatorEvaluation: Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
totalCount - Variable in class weka.core.AttributeStats: The total number of values (i.e.
toXMLBIF03() - Method in class weka.classifiers.bayes.BayesNet: Returns a description of the classifier in XML BIF 0.3 format.
TP_RATE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
trace() - Method in class weka.classifiers.functions.pace.Matrix: Matrix trace.
trainCV(int, int) - Method in class weka.core.Instances: Creates the training set for one fold of a cross-validation on the dataset.
trainCV(int, int, Random) - Method in class weka.core.Instances: Creates the training set for one fold of a cross-validation on the dataset.
TrainingSetEvent - class weka.gui.beans.TrainingSetEvent.: Event encapsulating a training set
TrainingSetEvent(Object, Instances) - Constructor for class weka.gui.beans.TrainingSetEvent: Creates a new TrainingSetEvent
TrainingSetListener - interface weka.gui.beans.TrainingSetListener.: Interface to something that can accept and process training set events
TrainingSetMaker - class weka.gui.beans.TrainingSetMaker.: Bean that accepts a data sets and produces a training set
TrainingSetMaker() - Constructor for class weka.gui.beans.TrainingSetMaker
TrainingSetMakerBeanInfo - class weka.gui.beans.TrainingSetMakerBeanInfo.: Bean info class for the training set maker bean
TrainingSetMakerBeanInfo() - Constructor for class weka.gui.beans.TrainingSetMakerBeanInfo
TrainingSetProducer - interface weka.gui.beans.TrainingSetProducer.: Interface to something that can produce a training set
trainingTimeTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
trainPercentTipText() - Method in class weka.gui.beans.TrainTestSplitMaker: Tip text info for this property
trainPercentTipText() - Method in class weka.experiment.RandomSplitResultProducer: Returns the tip text for this property
TrainTestSplitMaker - class weka.gui.beans.TrainTestSplitMaker.: Bean that accepts data sets, training sets, test sets and produces both a training and test set by randomly spliting the data
TrainTestSplitMaker() - Constructor for class weka.gui.beans.TrainTestSplitMaker
TrainTestSplitMakerBeanInfo - class weka.gui.beans.TrainTestSplitMakerBeanInfo.: Bean info class for the train test split maker bean
TrainTestSplitMakerBeanInfo() - Constructor for class weka.gui.beans.TrainTestSplitMakerBeanInfo
TrainTestSplitMakerCustomizer - class weka.gui.beans.TrainTestSplitMakerCustomizer.: GUI customizer for the train test split maker bean
TrainTestSplitMakerCustomizer() - Constructor for class weka.gui.beans.TrainTestSplitMakerCustomizer
transformBackToOriginalTipText() - Method in class weka.attributeSelection.PrincipalComponents: Returns the tip text for this property
transformedData() - Method in class weka.attributeSelection.PrincipalComponents: Gets the transformed training data.
transformedData() - Method in interface weka.attributeSelection.AttributeTransformer: Returns the transformed data
transformedHeader() - Method in class weka.attributeSelection.PrincipalComponents: Returns just the header for the transformed data (ie.
transformedHeader() - Method in interface weka.attributeSelection.AttributeTransformer: Returns just the header for the transformed data (ie.
transpose() - Method in class weka.core.Matrix: Returns the transpose of a matrix.
transpose() - Method in class weka.classifiers.functions.pace.Matrix: Matrix transpose.
transProb() - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute: Calculates the transformation probability of the attribute indexed "m_AttrIndex" in test instance "m_Test" to the same attribute in the train instance "m_Train".
transProb() - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute: Calculates the probability of the indexed nominal attribute of the test instance transforming into the indexed nominal attribute of the training instance.
TREE - Static variable in interface weka.core.Drawable
TreeBuild - class weka.gui.treevisualizer.TreeBuild.: This class will parse a dotty file and construct a tree structure from it with Edge's and Node's
TreeBuild() - Constructor for class weka.gui.treevisualizer.TreeBuild: Upon construction this will only setup the color table for quick reference of a color.
TreeDisplayEvent - class weka.gui.treevisualizer.TreeDisplayEvent.: An event containing the user selection from the tree display
TreeDisplayEvent(int, String) - Constructor for class weka.gui.treevisualizer.TreeDisplayEvent: Constructs an event with the specified command and what the command is applied to.
TreeDisplayListener - interface weka.gui.treevisualizer.TreeDisplayListener.: Interface implemented by classes that wish to recieve user selection events from a tree displayer.
treeErrors() - Method in class weka.classifiers.trees.lmt.LMTNode: Updates the numIncorrectTree field for all nodes.
treeToString(int) - Method in class weka.classifiers.trees.m5.RuleNode: Recursively builds a textual description of the tree
TreeVisualizer - class weka.gui.treevisualizer.TreeVisualizer.: Class for displaying a Node structure in Swing.
TreeVisualizer(TreeDisplayListener, Node, NodePlace) - Constructor for class weka.gui.treevisualizer.TreeVisualizer: Constructs Displayer with the specified Node as the top of the tree, and uses the NodePlacer to place the Nodes.
TreeVisualizer(TreeDisplayListener, String, NodePlace) - Constructor for class weka.gui.treevisualizer.TreeVisualizer: Constructs Displayer to display a tree provided in a dot format.
TRIANGLEDOWN_SHAPE - Static variable in class weka.gui.visualize.Plot2D
TRIANGLEUP_SHAPE - Static variable in class weka.gui.visualize.Plot2D
trim(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture: Trims the small values of the estaimte
trim(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture: Trims the small values of the estaimte
trimToSize() - Method in class weka.core.FastVector: Sets the vector's capacity to its size.
TRUE_NEG_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
TRUE_POS_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
trueNegativeRate(int) - Method in class weka.classifiers.Evaluation: Calculate the true negative rate with respect to a particular class.
trueNegativeRate(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate the true negative rate with respect to a particular class.
truePositiveRate(int) - Method in class weka.classifiers.Evaluation: Calculate the true positive rate with respect to a particular class.
truePositiveRate(int) - Method in class evaluationMethods.EstimatorEvaluation: Calculate the true positive rate with respect to a particular class.
turnChecksOff() - Method in class weka.classifiers.functions.LinearRegression: Turns off checks for missing values, etc.
turnChecksOff() - Method in class weka.classifiers.functions.SMO: Turns off checks for missing values, etc.
turnChecksOff() - Method in class weka.classifiers.functions.SMOreg: Turns off checks for missing values, etc.
turnChecksOff() - Method in class classifiers.PC_SMO: Turns off checks for missing values, etc.
turnChecksOff() - Method in class classifiers.AlphaProb_SMO: Turns off checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.functions.LinearRegression: Turns on checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.functions.SMO: Turns on checks for missing values, etc.
turnChecksOn() - Method in class weka.classifiers.functions.SMOreg: Turns on checks for missing values, etc.
turnChecksOn() - Method in class classifiers.PC_SMO: Turns on checks for missing values, etc.
turnChecksOn() - Method in class classifiers.AlphaProb_SMO: Turns on checks for missing values, etc.
TwoClassStats - class weka.classifiers.evaluation.TwoClassStats.: Encapsulates performance functions for two-class problems.
TwoClassStats(double, double, double, double) - Constructor for class weka.classifiers.evaluation.TwoClassStats: Creates the TwoClassStats with the given initial performance values.
TwoWayNominalSplit - class weka.classifiers.trees.adtree.TwoWayNominalSplit.: Class representing a two-way split on a nominal attribute, of the form: either 'is some_value' or 'is not some_value'.
TwoWayNominalSplit(int, int) - Constructor for class weka.classifiers.trees.adtree.TwoWayNominalSplit: Creates a new two-way nominal splitter.
TwoWayNumericSplit - class weka.classifiers.trees.adtree.TwoWayNumericSplit.: Class representing a two-way split on a numeric attribute, of the form: either 'is < some_value' or 'is >= some_value'.
TwoWayNumericSplit(int, double) - Constructor for class weka.classifiers.trees.adtree.TwoWayNumericSplit: Creates a new two-way numeric splitter.
type() - Method in class weka.core.Attribute: Returns the attribute's type as an integer.
typeName(int) - Static method in class weka.experiment.DatabaseUtils: Returns the name associated with a SQL type.

U

uminus() - Method in class weka.classifiers.functions.pace.Matrix: Unary minus
UnassignedClassException - exception weka.core.UnassignedClassException.: Exception that is raised when trying to use some data that has no class assigned to it, but a class is needed to perform the operation.
UnassignedClassException() - Constructor for class weka.core.UnassignedClassException: Creates a new UnassignedClassException with no message.
UnassignedClassException(String) - Constructor for class weka.core.UnassignedClassException: Creates a new UnassignedClassException.
UnassignedDatasetException - exception weka.core.UnassignedDatasetException.: Exception that is raised when trying to use something that has no reference to a dataset, when one is required.
UnassignedDatasetException() - Constructor for class weka.core.UnassignedDatasetException: Creates a new UnassignedDatasetException with no message.
UnassignedDatasetException(String) - Constructor for class weka.core.UnassignedDatasetException: Creates a new UnassignedDatasetException.
unclassified() - Method in class weka.classifiers.Evaluation: Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
unclassified() - Method in class evaluationMethods.EstimatorEvaluation: Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
UNCONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection: This unit is not connected to any others.
undefinedDistribution - Static variable in class weka.classifiers.functions.pace.Maths: Distribution type: undefined
undo() - Method in class weka.gui.explorer.PreprocessPanel: Reverts to the last backed up version of the dataset.
unique() - Method in class weka.classifiers.functions.pace.DiscreteFunction: Makes each individual point value unique
uniqueCount - Variable in class weka.core.AttributeStats: The number of values that only appear once
unpivoting(IntVector, int) - Method in class weka.classifiers.functions.pace.DoubleVector: Returns a vector from the pivoting indices.
unprunedTipText() - Method in class weka.classifiers.rules.PART: Returns the tip text for this property
unprunedTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
unsorted() - Method in class weka.classifiers.functions.pace.DoubleVector: Returns true if vector not sorted
UnsupervisedAttributeEvaluator - class weka.attributeSelection.UnsupervisedAttributeEvaluator.: Abstract unsupervised attribute evaluator.
UnsupervisedAttributeEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedAttributeEvaluator
UnsupervisedFilter - interface weka.filters.UnsupervisedFilter.: Interface for filters that do not need a class attribute.
UnsupervisedSubsetEvaluator - class weka.attributeSelection.UnsupervisedSubsetEvaluator.: Abstract unsupervised attribute subset evaluator.
UnsupervisedSubsetEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedSubsetEvaluator
UnsupportedAttributeTypeException - exception weka.core.UnsupportedAttributeTypeException.: Exception that is raised by an object that is unable to process some of the attribute types it has been passed.
UnsupportedAttributeTypeException() - Constructor for class weka.core.UnsupportedAttributeTypeException: Creates a new UnsupportedAttributeTypeException with no message.
UnsupportedAttributeTypeException(String) - Constructor for class weka.core.UnsupportedAttributeTypeException: Creates a new UnsupportedAttributeTypeException.
UnsupportedClassTypeException - exception weka.core.UnsupportedClassTypeException.: Exception that is raised by an object that is unable to process the class type of the data it has been passed.
UnsupportedClassTypeException() - Constructor for class weka.core.UnsupportedClassTypeException: Creates a new UnsupportedClassTypeException with no message.
UnsupportedClassTypeException(String) - Constructor for class weka.core.UnsupportedClassTypeException: Creates a new UnsupportedClassTypeException.
update(double) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
upDate(Instances) - Method in class weka.associations.tertius.Rule: Update the number of counter-instances of this rule in the dataset.
upDate(Instances) - Method in class weka.associations.tertius.LiteralSet: Update the number of counter-instances of this set in the dataset.
UpdateableClassifier - interface weka.classifiers.UpdateableClassifier.: Interface to incremental classification models that can learn using one instance at a time.
updateChildPropertySheet() - Method in class weka.gui.GenericObjectEditor.GOEPanel: Updates the child property sheet, and creates if needed
updateClassifier(Instance) - Method in interface weka.classifiers.UpdateableClassifier: Updates a classifier using the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.lazy.LWL: Adds the supplied instance to the training set
updateClassifier(Instance) - Method in class weka.classifiers.lazy.IB1: Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.lazy.KStar: Adds the supplied instance to the training set
updateClassifier(Instance) - Method in class weka.classifiers.lazy.IBk: Adds the supplied instance to the training set
updateClassifier(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.misc.HyperPipes: Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.BayesNet: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.NaiveBayes: Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.rules.NNge: Updates the classifier using the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.functions.Winnow: Updates the classifier with a new learning example
updateClassifier(Instance) - Method in class confidenceMachine.tcm.TCMKNearestNeighbours: Updates the classifier.
updateClassifier(Instance) - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Updates the classifier.
updateClassifier(Instance) - Method in class classifiers.AltDist_IBk: Adds the supplied instance to the training set
updateConfidenceClassifierStats(double[]) - Method in class evaluationMethods.OnlineEvaluation: Updates the confidence statistics.
upDateCounter(Instance) - Method in class weka.associations.ItemSet: Updates counter of item set with respect to given transaction.
upDateCounters(FastVector, Instances) - Static method in class weka.associations.ItemSet: Updates counters for a set of item sets and a set of instances.
updateDistributionClassifierStats(double[]) - Method in class evaluationMethods.OnlineEvaluation: Updates the distribution classifier statistics.
updateInstanceCache(USMComplexityCache) - Method in class classifiers.usm.distance.USMDistanceFunction: Updates the instance complexity cache
updatePattern(Instance) - Method in class coreComponents.PatternCounter.PatternObject
updatePriors(Instance) - Method in class weka.classifiers.Evaluation: Updates the class prior probabilities (when incrementally training)
updatePriors(Instance) - Method in class evaluationMethods.EstimatorEvaluation: Updates the class prior probabilities (when incrementally training)
updateRanges(Instance) - Method in class coreComponents.EuclideanDistanceMetric: Update the ranges of the distance metric with a new training instance.
updateRanges(Instance) - Method in class coreComponents.DistanceMetric: Update the ranges of the distance metric with a new training instance.
updateRanges(Instance) - Method in class classifiers.vdm.ValueDifferenceMetric
updateRanges(Instance) - Method in class classifiers.usm.distance.USMWavDistance: Update the ranges of the distance metric with a new training instance.
updateResult(String) - Method in class weka.gui.ResultHistoryPanel: Tells any component currently displaying the named result that the contents of the result text in the StringBuffer have been updated.
updateStandardStatistics(Instance, double[], double) - Method in class evaluationMethods.OnlineEvaluation: A method used to update the standard statistics based on some probabilities and p-values output by a classifier.
updateVennProbabilityClassifierStats(Matrix) - Method in class evaluationMethods.OnlineEvaluation: Updates the Venn probability statistics.
updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this function to update the weight values at this unit.
updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralNode: Call this function to update the weight values at this unit.
updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.SigmoidUnit: This function will calculate what the change in weights should be and also update them.
updateWeights(NeuralNode, double, double) - Method in interface weka.classifiers.functions.neural.NeuralMethod: This function will calculate what the change in weights should be and also update them.
updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.LinearUnit: This function will calculate what the change in weights should be and also update them.
upperBoundMinSupportTipText() - Method in class weka.associations.Apriori: Returns the tip text for this property
upperNumericBoundIsOpen() - Method in class weka.core.Attribute: Returns whether the upper numeric bound of the attribute is open.
upperSizeTipText() - Method in class weka.experiment.LearningRateResultProducer: Returns the tip text for this property
useADTreeTipText() - Method in class weka.classifiers.bayes.BayesNet
useBetterEncodingTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
useCrossValidationTipText() - Method in class weka.classifiers.functions.SimpleLogistic: Returns the tip text for this property
useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer: Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.AttributeSummarizer: Use the default appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.StripChart: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.Classifier: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.Filter: Use the default visual appearance
useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSink: Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.TextViewer: Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.PredictionAppender: Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.ClassAssigner
useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSource: Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.GraphViewer: Use the default visual appearance
useDefaultVisual() - Method in class weka.gui.beans.AbstractEvaluator: Use the default images for an evaluator
useDefaultVisual() - Method in interface weka.gui.beans.Visible: Use the default visual representation
useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.AbstractTestSetProducer: Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.DataVisualizer: Use the default appearance for this bean
useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Returns the tip text for this property
useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.Discretize: Returns the tip text for this property
useFilter(Instances, Filter) - Static method in class weka.filters.Filter: Filters an entire set of instances through a filter and returns the new set.
useIBkTipText() - Method in class weka.classifiers.rules.DecisionTable: Returns the tip text for this property
useKernelEstimatorTipText() - Method in class weka.classifiers.bayes.NaiveBayes: Returns the tip text for this property
useKononenkoTipText() - Method in class weka.filters.supervised.attribute.Discretize: Returns the tip text for this property
useLaplaceTipText() - Method in class weka.classifiers.trees.J48: Returns the tip text for this property
useMissingTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise: Returns the tip text for this property
usePruningTipText() - Method in class weka.classifiers.rules.JRip: Returns the tip text for this property
useRBFTipText() - Method in class weka.classifiers.functions.SMO: Returns the tip text for this property
useRBFTipText() - Method in class weka.classifiers.functions.SMOreg: Returns the tip text for this property
useRBFTipText() - Method in class classifiers.PC_SMO: Returns the tip text for this property
useRBFTipText() - Method in class classifiers.AlphaProb_SMO: Returns the tip text for this property
UserClassifier - class weka.classifiers.trees.UserClassifier.: Class for generating an user defined decision tree.
UserClassifier() - Constructor for class weka.classifiers.trees.UserClassifier: Constructor
userCommand(TreeDisplayEvent) - Method in interface weka.gui.treevisualizer.TreeDisplayListener: Gets called when the user selects something, in the tree display.
userCommand(TreeDisplayEvent) - Method in class weka.classifiers.trees.UserClassifier: Receives user choices from the tree view, and then deals with these choices.
userDataEvent(VisualizePanelEvent) - Method in interface weka.gui.visualize.VisualizePanelListener: This method receives an object containing the shapes, instances inside and outside these shapes and the attributes these shapes were created in.
userDataEvent(VisualizePanelEvent) - Method in class weka.classifiers.trees.UserClassifier: This receives shapes from the data view.
useResamplingTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
useResamplingTipText() - Method in class weka.classifiers.meta.AdaBoostM1: Returns the tip text for this property
useResamplingTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
UserRequestAcceptor - interface weka.gui.beans.UserRequestAcceptor.: Interface to something that can accept requests from a user to perform some action
useStoplistTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property.
useSupervisedDiscretizationTipText() - Method in class weka.classifiers.bayes.NaiveBayes: Returns the tip text for this property
useTrainingTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval: Returns the tip text for this property
USMComplexityCache - class classifiers.usm.distance.USMComplexityCache.: Class for cacheing the Kolmogorov complexity esitmates for a particular instance!
USMComplexityCache(Instance, double, double, Object, Attribute) - Constructor for class classifiers.usm.distance.USMComplexityCache: This is the constructor for the caching of the complexity esitmated needed to compute the distances between instances.
USMDistanceFunction - class classifiers.usm.distance.USMDistanceFunction.: Abstract class to implement Vitanyi's wonderful Universal Similarity Metric (USM) distance function.
USMDistanceFunction(Instances) - Constructor for class classifiers.usm.distance.USMDistanceFunction: Constructs a distance function object.
USMStringDistance - class classifiers.usm.distance.USMStringDistance.: Class to implement Vitanyi's wonderful Universal Similarity Metric (USM) distance function for simple strings! Does exactly what it says on the tin.
USMStringDistance(Instances) - Constructor for class classifiers.usm.distance.USMStringDistance: Simple constructor, just initialise with the data that is being used to check that it is in the correct format!
USMWavDistance - class classifiers.usm.distance.USMWavDistance.: Class to implement Vitanyi's wonderful Universal Similarity Metric (USM) distance function for wav files! Does exactly what it says on the tin.
USMWavDistance(Instances) - Constructor for class classifiers.usm.distance.USMWavDistance: Simple constructor, just initialise with the data that is being used to check that it is in the correct format!
Utils - class weka.core.Utils.: Class implementing some simple utility methods.
Utils() - Constructor for class weka.core.Utils

V

validationChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
validationSetSizeTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
validationThresholdTipText() - Method in class weka.classifiers.functions.MultilayerPerceptron
value - Variable in class weka.experiment.PropertyNode: The current property value
value - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry: scale factor or stop parameter
value(Attribute) - Method in class weka.core.Instance: Returns an instance's attribute value in internal format.
value(int) - Method in class weka.core.Instance: Returns an instance's attribute value in internal format.
value(int) - Method in class weka.core.BinarySparseInstance: Returns an instance's attribute value in internal format.
value(int) - Method in class weka.core.Attribute: Returns a value of a nominal or string attribute.
value(int) - Method in class weka.core.SparseInstance: Returns an instance's attribute value in internal format.
ValueDifferenceMetric - class classifiers.vdm.ValueDifferenceMetric.: Implementation of the Value Difference Metric used in the PEBLS nearest neighbour algorithm detailed in the paper: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features 1993, S.
ValueDifferenceMetric() - Constructor for class classifiers.vdm.ValueDifferenceMetric: Default constructor
ValueDifferenceMetric(Instances) - Constructor for class classifiers.vdm.ValueDifferenceMetric: More complicated constructor
valueIndicesTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Values - class weka.classifiers.trees.m5.Values.: Stores some statistics.
Values(int, int, int, Instances) - Constructor for class weka.classifiers.trees.m5.Values: Constructs an object which stores some statistics of the instances such as sum, squared sum, variance, standard deviation
valuesOutputTipText() - Method in class weka.associations.Tertius: Returns the tip text for this property.
valueSparse(int) - Method in class weka.core.Instance: Returns an instance's attribute value in internal format.
valueSparse(int) - Method in class weka.core.BinarySparseInstance: Returns an instance's attribute value in internal format.
valuesToString() - Method in class weka.associations.tertius.Rule: Return a String giving the confirmation and optimistic estimate of this rule.
variance(Attribute) - Method in class weka.core.Instances: Computes the variance for a numeric attribute.
variance(double[]) - Static method in class weka.core.Utils: Computes the variance for an array of doubles.
variance(int) - Method in class weka.core.Instances: Computes the variance for a numeric attribute.
varianceCoveredTipText() - Method in class weka.attributeSelection.PrincipalComponents: Returns the tip text for this property
VaryNode - class weka.classifiers.bayes.VaryNode.: Part of ADTree implementation.
VaryNode(int) - Constructor for class weka.classifiers.bayes.VaryNode: Creates new VaryNode
VennProbabilityClassifier - class probabilityMachine.VennProbabilityClassifier.: Abstract classification model that produces (for each test instance) a valid probability estimation of the membership in each class.
VennProbabilityClassifier() - Constructor for class probabilityMachine.VennProbabilityClassifier
vennProbsForInstance(Instance) - Method in class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: Returns the Venn probability matrix for a given test instance.
vennProbsForInstance(Instance) - Method in class probabilityMachine.VPMSimpleTypeDistMetaLearner: Returns the Venn probability matrix for a given test instance.
vennProbsForInstance(Instance) - Method in class probabilityMachine.VPMDistMetaLearner: Returns the Venn probability matrix for a given test instance.
vennProbsForInstance(Instance) - Method in class probabilityMachine.VennProbabilityClassifier: Predicts the Venn probabilities for each instance.
vennProbsForInstance(Instance) - Method in class probabilityMachine.vpm.VPMNaiveBayes: Returns the Venn probability matrix for a given test instance.
vennProbsForInstance(Instance) - Method in class probabilityMachine.vpm.VPMBartsRMI2: Returns the Venn probability matrix for a given test instance.
vennProbsForInstance(Instance) - Method in class probabilityMachine.vpm.VPMBartsRMI: Returns the Venn probability matrix for a given test instance.
vennProbsForInstance(Instance) - Method in class probabilityMachine.vpm.VPMKNearestNeighbours: Returns the Venn probability matrix for a given test instance.
verboseTipText() - Method in class weka.attributeSelection.ExhaustiveSearch: Returns the tip text for this property
verboseTipText() - Method in class weka.attributeSelection.RandomSearch: Returns the tip text for this property
VFI - class weka.classifiers.misc.VFI.: Class implementing the voting feature interval classifier.
VFI() - Constructor for class weka.classifiers.misc.VFI
Visible - interface weka.gui.beans.Visible.: Interface to something that has a visible (via BeanVisual) reprentation
VisualizePanel - class weka.gui.visualize.VisualizePanel.: This panel allows the user to visualize a dataset (and if provided) a classifier's/clusterer's predictions in two dimensions.
VisualizePanel() - Constructor for class weka.gui.visualize.VisualizePanel: Constructor
VisualizePanel(VisualizePanelListener) - Constructor for class weka.gui.visualize.VisualizePanel: This constructor allows a VisualizePanelListener to be set.
VisualizePanelEvent - class weka.gui.visualize.VisualizePanelEvent.: This event Is fired to a listeners 'userDataEvent' function when The user on the VisualizePanel clicks submit.
VisualizePanelEvent(FastVector, Instances, Instances, int, int) - Constructor for class weka.gui.visualize.VisualizePanelEvent: This constructor creates the event with all the parameters set.
VisualizePanelListener - interface weka.gui.visualize.VisualizePanelListener.: Interface implemented by a class that is interested in receiving submited shapes from a visualize panel.
VisualizeUtils - class weka.gui.visualize.VisualizeUtils.: This class contains utility routines for visualization
VisualizeUtils() - Constructor for class weka.gui.visualize.VisualizeUtils
VLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
Vote - class weka.classifiers.meta.Vote.: Class for combining classifiers using unweighted average of probability estimates (classification) or numeric predictions (regression).
Vote() - Constructor for class weka.classifiers.meta.Vote
VotedPerceptron - class weka.classifiers.functions.VotedPerceptron.: Implements the voted perceptron algorithm by Freund and Schapire.
VotedPerceptron() - Constructor for class weka.classifiers.functions.VotedPerceptron
VPMBartsRMI - class probabilityMachine.vpm.VPMBartsRMI.: The VPM version of the BartsRMI algorithm.
VPMBartsRMI() - Constructor for class probabilityMachine.vpm.VPMBartsRMI: The amazing VPMBartsRMI classifier
VPMBartsRMI(double, int) - Constructor for class probabilityMachine.vpm.VPMBartsRMI: The amazing VPMBartsRMI classifier, used to show how to adapt a classifer to the VPM meta learning framework!
VPMBartsRMI2 - class probabilityMachine.vpm.VPMBartsRMI2.: The second VPM version of the BartsRMI algorithm.
VPMBartsRMI2() - Constructor for class probabilityMachine.vpm.VPMBartsRMI2: The amazing VPMBartsRMI classifier
VPMBartsRMI2(double, int) - Constructor for class probabilityMachine.vpm.VPMBartsRMI2: The amazing VPMBartsRMI2 classifier, used to show how to adapt a classifer to the VPM meta learning framework!
VPMDistMetaLearner - class probabilityMachine.VPMDistMetaLearner.: The first implementation of a VPM Distribution Classifier Meta Learner.
VPMDistMetaLearner() - Constructor for class probabilityMachine.VPMDistMetaLearner: The amazing VPMBartsRMI classifier
VPMDistMetaLearner(int) - Constructor for class probabilityMachine.VPMDistMetaLearner: The amazing VPMDistMetaLearner classifier, used to show how to adapt a classifer to the VPM meta learning framework!
VPMKNearestNeighbours - class probabilityMachine.vpm.VPMKNearestNeighbours.: The VPM K-Nearest Neighbours algorithm.
VPMKNearestNeighbours() - Constructor for class probabilityMachine.vpm.VPMKNearestNeighbours: The amazing VPM K Nearest Neighbours classifier
VPMKNearestNeighbours(int) - Constructor for class probabilityMachine.vpm.VPMKNearestNeighbours: The amazing VPM K Nearest Neighbours classifier
VPMMDLEntropyTypeDistMetaLearner - class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner.: The first implementation of a VPM Distribution Classifier Meta Learner.
VPMMDLEntropyTypeDistMetaLearner() - Constructor for class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: The amazing VPMBartsRMI classifier
VPMMDLEntropyTypeDistMetaLearner(int) - Constructor for class probabilityMachine.VPMMDLEntropyTypeDistMetaLearner: The amazing VPMMDLEntropyTypeDistMetaLearner classifier, used to show how to adapt a classifer to the VPM meta learning framework!
VPMNaiveBayes - class probabilityMachine.vpm.VPMNaiveBayes.: The first implementation of a VPM of Naive Bayes.
VPMNaiveBayes() - Constructor for class probabilityMachine.vpm.VPMNaiveBayes: The amazing VPMBartsRMI classifier
VPMNaiveBayes(int) - Constructor for class probabilityMachine.vpm.VPMNaiveBayes: The amazing VPMNaiveBayes classifier, used to show how to adapt a classifer to the VPM meta learning framework!
VPMSimpleTypeDistMetaLearner - class probabilityMachine.VPMSimpleTypeDistMetaLearner.: The first implementation of a VPM Distribution Classifier Meta Learner.
VPMSimpleTypeDistMetaLearner() - Constructor for class probabilityMachine.VPMSimpleTypeDistMetaLearner: The amazing VPMBartsRMI classifier
VPMSimpleTypeDistMetaLearner(int) - Constructor for class probabilityMachine.VPMSimpleTypeDistMetaLearner: The amazing VPMSimpleTypeDistMetaLearner classifier, used to show how to adapt a classifer to the VPM meta learning framework!

W

WEIGHT_INVERSE - Static variable in class weka.classifiers.lazy.IBk
WEIGHT_INVERSE - Static variable in class classifiers.AltDist_IBk
WEIGHT_NONE - Static variable in class weka.classifiers.lazy.IBk
WEIGHT_NONE - Static variable in class classifiers.AltDist_IBk
WEIGHT_SIMILARITY - Static variable in class weka.classifiers.lazy.IBk
WEIGHT_SIMILARITY - Static variable in class classifiers.AltDist_IBk
weight() - Method in class weka.core.Instance: Returns the instance's weight.
weight() - Method in class weka.core.Attribute: Returns the attribute's weight.
weight() - Method in class weka.classifiers.evaluation.NominalPrediction: Gets the weight assigned to this prediction.
weight() - Method in interface weka.classifiers.evaluation.Prediction: Gets the weight assigned to this prediction.
weight() - Method in class weka.classifiers.evaluation.NumericPrediction: Gets the weight assigned to this prediction.
weight(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList: Returns the weight a rule assigns to an instance.
weightByConfidenceTipText() - Method in class weka.classifiers.misc.VFI: Returns the tip text for this property
weightByDistanceTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval: Returns the tip text for this property
WeightedInstancesHandler - interface weka.core.WeightedInstancesHandler.: Interface to something that makes use of the information provided by instance weights.
weightingKernelTipText() - Method in class weka.classifiers.lazy.LWL: Returns the tip text for this property
weights(Instance) - Method in class weka.classifiers.trees.j48.C45Split: Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.BinC45Split: Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.NoSplit: Always returns null because there is only one subset.
weights(Instance) - Method in class weka.classifiers.trees.lmt.ResidualSplit: Method not in use
weightThresholdTipText() - Method in class weka.classifiers.meta.AdaBoostM1: Returns the tip text for this property
weightThresholdTipText() - Method in class weka.classifiers.meta.LogitBoost: Returns the tip text for this property
weightValue(int) - Method in class weka.classifiers.functions.neural.NeuralConnection: Call this to get the weight value on a particular connection.
weightValue(int) - Method in class weka.classifiers.functions.neural.NeuralNode: Call this to get the weight value on a particular connection.
weka.associations - package weka.associations
weka.associations.tertius - package weka.associations.tertius
weka.attributeSelection - package weka.attributeSelection
weka.classifiers - package weka.classifiers
weka.classifiers.bayes - package weka.classifiers.bayes
weka.classifiers.evaluation - package weka.classifiers.evaluation
weka.classifiers.functions - package weka.classifiers.functions
weka.classifiers.functions.neural - package weka.classifiers.functions.neural
weka.classifiers.functions.pace - package weka.classifiers.functions.pace
weka.classifiers.functions.supportVector - package weka.classifiers.functions.supportVector
weka.classifiers.lazy - package weka.classifiers.lazy
weka.classifiers.lazy.kstar - package weka.classifiers.lazy.kstar
weka.classifiers.meta - package weka.classifiers.meta
weka.classifiers.misc - package weka.classifiers.misc
weka.classifiers.rules - package weka.classifiers.rules
weka.classifiers.rules.part - package weka.classifiers.rules.part
weka.classifiers.trees - package weka.classifiers.trees
weka.classifiers.trees.adtree - package weka.classifiers.trees.adtree
weka.classifiers.trees.j48 - package weka.classifiers.trees.j48
weka.classifiers.trees.lmt - package weka.classifiers.trees.lmt
weka.classifiers.trees.m5 - package weka.classifiers.trees.m5
weka.clusterers - package weka.clusterers
weka.core - package weka.core
weka.core.converters - package weka.core.converters
weka.datagenerators - package weka.datagenerators
weka.estimators - package weka.estimators
weka.experiment - package weka.experiment
weka.filters - package weka.filters
weka.filters.supervised.attribute - package weka.filters.supervised.attribute
weka.filters.supervised.instance - package weka.filters.supervised.instance
weka.filters.unsupervised.attribute - package weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance - package weka.filters.unsupervised.instance
weka.gui - package weka.gui
weka.gui.beans - package weka.gui.beans
weka.gui.boundaryvisualizer - package weka.gui.boundaryvisualizer
weka.gui.experiment - package weka.gui.experiment
weka.gui.explorer - package weka.gui.explorer
weka.gui.graphvisualizer - package weka.gui.graphvisualizer
weka.gui.streams - package weka.gui.streams
weka.gui.treevisualizer - package weka.gui.treevisualizer
weka.gui.visualize - package weka.gui.visualize
WekaException - exception weka.core.WekaException.: Class for Weka-specific exceptions.
WekaException() - Constructor for class weka.core.WekaException: Creates a new WekaException with no message.
WekaException(String) - Constructor for class weka.core.WekaException: Creates a new WekaException.
WekaTaskMonitor - class weka.gui.WekaTaskMonitor.: This panel records the number of weka tasks running and displays a simple bird animation while their are active tasks
WekaTaskMonitor() - Constructor for class weka.gui.WekaTaskMonitor: Constructor
WekaWrapper - interface weka.gui.beans.WekaWrapper.: Interface to something that can wrap around a class of Weka algorithms (classifiers, filters etc).
WEST_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.C45Split: Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.BinC45Split: Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel: Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.NoSplit: Always returns 0 because only there is only one subset.
whichSubset(Instance) - Method in class weka.classifiers.trees.lmt.ResidualSplit
wholeDataErrTipText() - Method in class weka.classifiers.rules.Ridor: Returns the tip text for this property
width() - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
width() - Method in class weka.classifiers.functions.pace.FloatingPointFormat
width() - Method in class weka.classifiers.functions.pace.ExponentialFormat
windowSizeTipText() - Method in class weka.classifiers.lazy.IBk: Returns the tip text for this property
Winnow - class weka.classifiers.functions.Winnow.: Implements Winnow and Balanced Winnow algorithms by N.
Winnow() - Constructor for class weka.classifiers.functions.Winnow
WITHIN_BATCH - Static variable in class weka.gui.beans.IncrementalClassifierEvent
wordsToKeepTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector: Returns the tip text for this property
WrapperSubsetEval - class weka.attributeSelection.WrapperSubsetEval.: Wrapper attribute subset evaluator.
WrapperSubsetEval() - Constructor for class weka.attributeSelection.WrapperSubsetEval: Constructor.
write(Writer) - Method in class weka.core.Matrix: Writes out a matrix.
writeDOT(String, String, FastVector, FastVector) - Static method in class weka.gui.graphvisualizer.DotParser: This method saves a graph in a file in DOT format.
writeXMLBIF03(String, String, FastVector, FastVector) - Static method in class weka.gui.graphvisualizer.BIFParser: This method writes a graph in XMLBIF ver.

X

X_SHAPE - Static variable in class weka.gui.visualize.Plot2D
xLabelFreqTipText() - Method in class weka.gui.beans.StripChart: GUI Tip text
xlogx(int) - Static method in class weka.core.Utils: Returns c*log2(c) for a given integer value c.
xStats - Variable in class weka.experiment.PairedStats: The stats associated with the data in column 1
XVALTAGS_SELECTION - Static variable in class weka.attributeSelection.RaceSearch
xySum - Variable in class weka.experiment.PairedStats: The sum of the products

Y

YongSplitInfo - class weka.classifiers.trees.m5.YongSplitInfo.: Stores split information.
YongSplitInfo(int, int, int) - Constructor for class weka.classifiers.trees.m5.YongSplitInfo: Constructs an object which contains the split information
yStats - Variable in class weka.experiment.PairedStats: The stats associated with the data in column 2

Z

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

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