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

A

A - Variable in class weka.classifiers.functions.pace.Matrix
Array for internal storage of elements.
ACCEPT - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
States that the user has accepted the tree.
ADDING - Static variable in class weka.gui.beans.KnowledgeFlow
 
ADD_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
 
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.ADTree.
Class for generating an alternating decision tree.
ADTree() - Constructor for class weka.classifiers.trees.adtree.ADTree
 
AIC - Static variable in interface weka.classifiers.bayes.Scoreable
 
ALGORITHM_SIMPLE - Static variable in class weka.clusterers.MPCKMeans
Define possible algorithms
ALGORITHM_SIMPLE - Static variable in class weka.clusterers.PCKMeans
Define possible algorithms
ALGORITHM_SIMPLE - Static variable in class weka.clusterers.PCSoftKMeans
Define possible algorithms
ALGORITHM_SIMPLE - Static variable in class weka.clusterers.SeededKMeans
Define possible algorithms
ALGORITHM_SPHERICAL - Static variable in class weka.clusterers.MPCKMeans
 
ALGORITHM_SPHERICAL - Static variable in class weka.clusterers.PCKMeans
 
ALGORITHM_SPHERICAL - Static variable in class weka.clusterers.PCSoftKMeans
 
ALGORITHM_SPHERICAL - Static variable in class weka.clusterers.SeededKMeans
 
APPROACH_DEFL - Static variable in class weka.attributeSelection.MatlabICA
 
APPROACH_SYMM - Static variable in class weka.attributeSelection.MatlabICA
 
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
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
 
AVAILABLE - Static variable in class weka.experiment.RemoteExperiment
 
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
 
ActiveDecorate - class weka.classifiers.meta.ActiveDecorate.
Active-DECORATE is a version of DECORATE that allows for selective sampling of training examples.
ActiveDecorate() - Constructor for class weka.classifiers.meta.ActiveDecorate
 
ActiveFeatureAcquirer - interface weka.classifiers.ActiveFeatureAcquirer.
Interface to permit a classifier to perform active feature aquisition.
ActiveFeatureAcquisitionCVResultProducer - class weka.experiment.ActiveFeatureAcquisitionCVResultProducer.
Does an N-fold cross-validation, but generates a learning curve by also varying the number of training examples.
ActiveFeatureAcquisitionCVResultProducer() - Constructor for class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
ActiveLearner - interface weka.classifiers.ActiveLearner.
Interface to permit a classifier to perform selective sampling.
ActiveLearningClusterer - interface weka.clusterers.ActiveLearningClusterer.
 
ActiveLearningCurveCVResultProducer - class weka.experiment.ActiveLearningCurveCVResultProducer.
Does an N-fold cross-validation, but generates a learning curve by also varying the number of training examples.
ActiveLearningCurveCVResultProducer() - Constructor for class weka.experiment.ActiveLearningCurveCVResultProducer
 
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
 
Add - class weka.filters.unsupervised.attribute.Add.
An instance filter that adds a new attribute to the dataset.
Add() - Constructor for class weka.filters.unsupervised.attribute.Add
 
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
 
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
 
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
 
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)
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.
AffineMetric - class weka.deduping.metrics.AffineMetric.
A measure of distance between two strings based on affine distance.
AffineMetric() - Constructor for class weka.deduping.metrics.AffineMetric
A default constructor that assigns the name of this distance
AffineProbMetric - class weka.deduping.metrics.AffineProbMetric.
AffineProbMetric class implements a probabilistic model string edit distance with affine-cost gaps
AffineProbMetric() - Constructor for class weka.deduping.metrics.AffineProbMetric
set up an instance of AffineProbMetric
AlgVector - class weka.clusterers.AlgVector.
Class for performing operations on an algebraic vector of floating-point values.
AlgVector(int) - Constructor for class weka.clusterers.AlgVector
Constructs a vector and initializes it with default values.
AlgVector(double[]) - Constructor for class weka.clusterers.AlgVector
Constructs a vector using a given array.
AlgVector(Instances, Random) - Constructor for class weka.clusterers.AlgVector
Constructs a vector using a given data format.
AlgVector(Instance) - Constructor for class weka.clusterers.AlgVector
Constructs a vector using an instance.
AlgVector - class weka.core.AlgVector.
Class for performing operations on an algebraic vector of floating-point values.
AlgVector(int) - Constructor for class weka.core.AlgVector
Constructs a vector and initializes it with default values.
AlgVector(double[]) - Constructor for class weka.core.AlgVector
Constructs a vector using a given array.
AlgVector(Instances, Random) - Constructor for class weka.core.AlgVector
Constructs a vector using a given data format.
AlgVector(Instance) - Constructor for class weka.core.AlgVector
Constructs a vector using an instance.
AlgorithmListPanel - class weka.gui.experiment.AlgorithmListPanel.
This panel controls setting a list of algorithms for an experiment to iterate over.
AlgorithmListPanel(Experiment) - Constructor for class weka.gui.experiment.AlgorithmListPanel
Creates the algorithm list panel with the given experiment.
AlgorithmListPanel() - Constructor for class weka.gui.experiment.AlgorithmListPanel
Create the algorithm list panel initially disabled.
AlgorithmListPanel.ObjectCellRenderer - class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer.
 
AlgorithmListPanel.ObjectCellRenderer() - Constructor for class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
 
AllFilter - class weka.filters.AllFilter.
A simple instance filter that passes all instances directly through.
AllFilter() - Constructor for class weka.filters.AllFilter
 
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.
ArffLoader - class weka.core.converters.ArffLoader.
Reads a source that is in arff text format.
ArffLoader() - Constructor for class weka.core.converters.ArffLoader
 
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
 
AttrEvalMetricLearner - class weka.core.metrics.AttrEvalMetricLearner.
AttrEvalMetricLearner - sets the weights of a metric using scores from an attribute evaluator
AttrEvalMetricLearner() - Constructor for class weka.core.metrics.AttrEvalMetricLearner
Create a new attribute evaluator metric learner
Attribute - class weka.core.Attribute.
Class for handling an attribute.
Attribute(String) - Constructor for class weka.core.Attribute
Constructor for a numeric attribute.
Attribute(String, ProtectedProperties) - Constructor for class weka.core.Attribute
Constructor for a numeric attribute, where metadata is supplied.
Attribute(String, String) - Constructor for class weka.core.Attribute
Constructor for a date attribute.
Attribute(String, String, ProtectedProperties) - Constructor for class weka.core.Attribute
Constructor for a date attribute, where metadata is supplied.
Attribute(String, FastVector) - Constructor for class weka.core.Attribute
Constructor for nominal attributes and string attributes.
Attribute(String, FastVector, ProtectedProperties) - Constructor for class weka.core.Attribute
Constructor for nominal attributes and string attributes, where metadata is supplied.
AttributeEvaluator - class weka.attributeSelection.AttributeEvaluator.
Abstract attribute evaluator.
AttributeEvaluator() - Constructor for class weka.attributeSelection.AttributeEvaluator
 
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.
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.
AttributePanel.AttributeSpacing - class weka.gui.visualize.AttributePanel.AttributeSpacing.
inner inner class used for plotting the points into a bar for a particular attribute.
AttributePanel.AttributeSpacing(Attribute, int) - Constructor for class weka.gui.visualize.AttributePanel.AttributeSpacing
This constructs the bar with the specified attribute and sets its index to be used for selecting by the mouse.
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.attributeSelection.AttributeSelection.
Attribute selection class.
AttributeSelection() - Constructor for class weka.attributeSelection.AttributeSelection
constructor.
AttributeSelection - class weka.filters.supervised.attribute.AttributeSelection.
Filter for doing attribute selection.
AttributeSelection() - Constructor for class weka.filters.supervised.attribute.AttributeSelection
Constructor
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() - Constructor for class weka.gui.AttributeSelectionPanel
Creates the attribute selection panel with no initial instances.
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.explorer.AttributeSelectionPanel
Creates the classifier panel
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
 
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.
AttributeTransformer - interface weka.attributeSelection.AttributeTransformer.
Abstract attribute transformer.
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
 
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
 
ablateFeatures(Instances) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Replace non local features with missing values
abortExperiment() - Method in class weka.experiment.RemoteExperiment
Set the abort flag
absDev(int, Instances) - Static method in class weka.classifiers.trees.m5.Rule
Returns the absolute deviation value of the supplied attribute index.
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.ClassifierPerformanceEvaluator
Accept a classifier to be evaluated
acceptClassifier(IncrementalClassifierEvent) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Accepts and processes a classifier encapsulated in an incremental classifier event
acceptClassifier(IncrementalClassifierEvent) - Method in interface weka.gui.beans.IncrementalClassifierListener
Accept the event
acceptDataPoint(ChartEvent) - Method in interface weka.gui.beans.ChartListener
 
acceptDataPoint(ChartEvent) - Method in class weka.gui.beans.StripChart
Accept a data point (encapsulated in a chart event) to plot
acceptDataPoint(double[]) - Method in class weka.gui.beans.StripChart
Accept a data point to plot
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Subclass must implement
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker
Accept a data set
acceptDataSet(DataSetEvent) - Method in interface weka.gui.beans.DataSourceListener
 
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.DataVisualizer
Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.Filter
Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TestSetMaker
Accepts and processes a data set event
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TextViewer
Accept a data set for displaying as text
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Accept a data set
acceptDataSet(DataSetEvent) - Method in class weka.gui.beans.TrainingSetMaker
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 class weka.gui.beans.ClassAssigner
 
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.Classifier
Accepts an instance for incremental processing.
acceptInstance(InstanceEvent) - Method in class weka.gui.beans.Filter
Accept an instance for processing by StreamableFilters only
acceptInstance(InstanceEvent) - Method in interface weka.gui.beans.InstanceListener
Accept and process an instance event
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.AveragingResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.CSVResultListener
Just prints out each result as it is received.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseResultListener
Submit the result to the appropriate table of the database
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.InstancesResultListener
Collects each instance and adjusts the header information.
acceptResult(ResultProducer, Object[], Object[]) - Method in class weka.experiment.LearningRateResultProducer
Accepts results from a ResultProducer.
acceptResult(ResultProducer, Object[], Object[]) - Method in interface weka.experiment.ResultListener
Accepts results from a ResultProducer.
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Classifier
Accepts a test set for a batch trained classifier
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker
Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.DataVisualizer
Accept a test set
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.Filter
Accept a test set
acceptTestSet(TestSetEvent) - Method in interface weka.gui.beans.TestSetListener
Accept and process a test set event
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.TextViewer
Accept a test set for displaying as text
acceptTestSet(TestSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Accept a test set
acceptText(TextEvent) - Method in interface weka.gui.beans.TextListener
Accept and process a text event
acceptText(TextEvent) - Method in class weka.gui.beans.TextViewer
Accept some text
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Classifier
Accepts a training set and builds batch classifier
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.DataVisualizer
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.Filter
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.TextViewer
Accept a training set for displaying as text
acceptTrainingSet(TrainingSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Accept a training set
acceptTrainingSet(TrainingSetEvent) - Method in interface weka.gui.beans.TrainingSetListener
Accept and process a training set
accumulateConstraintCoeffs(double[]) - Method in class weka.clusterers.assigners.LPAssigner
Accumulate contribution from constraints
accumulateDistortionCoeffs(double[]) - Method in class weka.clusterers.assigners.LPAssigner
go through all instances and all clusters and accumulate the distortion contributions
accumulateStatistics() - Method in class weka.deduping.BasicDeduper
Add the current state of things to statistics
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.experiment.AlgorithmListPanel
Handle actions when buttons get pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.DatasetListPanel
Handle actions when buttons get pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Handles the various button clicking type activities.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.HostListPanel
Handle actions when text is entered into the host field or the delete button is pressed.
actionPerformed(ActionEvent) - Method in class weka.gui.experiment.RunPanel
Controls starting and stopping the experiment.
actionPerformed(ActionEvent) - Method in class weka.gui.streams.InstanceLoader
 
actionPerformed(ActionEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the action associated with the ActionEvent.
activePhaseOne(int) - Method in class weka.clusterers.MPCKMeans
Phase 1 code for active learning
activePhaseOne(int) - Method in class weka.clusterers.PCKMeans
Phase 1 code for active learning
activePhaseTwoRandom(int) - Method in class weka.clusterers.MPCKMeans
Phase 2 code for active learning, random
activePhaseTwoRandom(int) - Method in class weka.clusterers.PCKMeans
Phase 2 code for active learning, random
activePhaseTwoRoundRobin(int) - Method in class weka.clusterers.MPCKMeans
Phase 2 code for active learning, with round robin
activePhaseTwoRoundRobin(int) - Method in class weka.clusterers.PCKMeans
Phase 2 code for active learning, with round robin
activeScore - Variable in class weka.clusterers.InstancePair
----- DEPRECATED: ACTIVE SCORE NO LONGER USED IN PCKMEANS!!!! -----
actual() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets the actual class value.
actual() - Method in class weka.classifiers.evaluation.NumericPrediction
Gets the actual class value.
actual() - Method in interface weka.classifiers.evaluation.Prediction
Gets the actual class value.
actualNumBags() - Method in class weka.classifiers.trees.j48.Distribution
Returns number of non-empty bags of distribution.
actualNumClasses() - Method in class weka.classifiers.trees.j48.Distribution
Returns number of classes actually occuring in distribution.
actualNumClasses(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns number of classes actually occuring in given bag.
acuityTipText() - Method in class weka.clusterers.Cobweb
Returns the tip text for this property
add(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution
Adds given instance to given bag.
add(int, double[]) - Method in class weka.classifiers.trees.j48.Distribution
Adds counts to given bag.
add(AlgVector) - Method in class weka.clusterers.AlgVector
Returns the sum of this vector with another.
add(Object) - Method in class weka.clusterers.Cluster
Adds an object to the cluster with default weight 1
add(Object, double) - Method in class weka.clusterers.Cluster
Adds an object to the cluster with specified weight
add(AlgVector) - Method in class weka.core.AlgVector
Returns the sum of this vector with another.
add(Instance) - Method in class weka.core.Instances
Adds one instance to the end of the set.
add(Instance, double) - Method in class weka.core.Instances
Adds one instance to the end of the set, with given weight.
add(Matrix) - Method in class weka.core.Matrix
Returns the sum of this matrix with another.
add(TextSource.DataRow) - Method in class weka.datagenerators.TextSource.Table
 
add(HashMapVector) - Method in class weka.deduping.metrics.HashMapVector
Destructively add the given vector to the current vector
add(double, double) - Method in class weka.experiment.PairedStats
Add an observed pair of values.
add(double) - Method in class weka.experiment.Stats
Adds a value to the observed values
add(double, double) - Method in class weka.experiment.Stats
Adds a value that has been seen n times to the observed values
add(String) - Method in class weka.gui.HierarchyPropertyParser
Add the given item of property to the tree
addActionListener(ActionListener) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Register a listener to be notified when plotting completes
addActionListener(ActionListener) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Add a listener interested in kowing about editor status changes
addActionListener(ActionListener) - Method in class weka.gui.visualize.VisualizePanel
Add a listener for this visualize panel
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
addAttribute(Attribute) - Method in class weka.datagenerators.TextSource.Table
 
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
addCVParameter(String) - Method in class weka.classifiers.meta.CVParameterSelection
Adds a scheme parameter to the list of parameters to be set by cross-validation
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(Splitter, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode
Adds a child to this node.
addChild(Edge) - Method in class weka.gui.treevisualizer.Node
Set the value of children.
addChildrenToTree(DefaultMutableTreeNode, HierarchyPropertyParser) - Method in class weka.gui.GenericObjectEditor
Recursively builds a JTree from an object heirarchy.
addClassNoise(Instances, Instances, int) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.AbstractDataSource
Add a listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassAssigner
 
addDataSourceListener(DataSourceListener) - Method in interface weka.gui.beans.DataSource
Add a data source listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Filter
Add a data source listener
addDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Loader
Add a listener
addDistinct(double, int) - Method in class weka.core.AttributeStats
Updates the counters for one more observed distinct value.
addElement(double) - Method in class weka.classifiers.functions.pace.DoubleVector
Adds an element into the vector
addElement(Object) - Method in class weka.core.FastVector
Adds an element to this vector.
addElement(int, int, double) - Method in class weka.core.Matrix
Add a value to an element.
addErrs(double, double, float) - Static method in class weka.classifiers.trees.j48.Stats
Computes estimated extra error for given total number of instances and error using normal approximation to binomial distribution (and continuity correction).
addFeatureMiss(Instances, Instances, int) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
addFeatureNoise(Instances, Instances, int) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
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
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.
addInstance(Instance) - Method in class weka.clusterers.Cobweb
Adds an instance to the Cobweb tree.
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.AbstractDataSource
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.ClassAssigner
 
addInstanceListener(InstanceListener) - Method in interface weka.gui.beans.DataSource
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.Filter
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.beans.Loader
Add an instance listener
addInstanceListener(InstanceListener) - Method in class weka.gui.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,
addInstances(Instances, Instances) - Method in class weka.classifiers.meta.ActiveDecorate
Add new instances to the given set of instances.
addInstances(Instances, Instances) - Method in class weka.classifiers.meta.Crate
Add new instances to the given set of instances.
addInstances(Instances, Instances) - Method in class weka.classifiers.meta.DEC
 
addInstances(Instances, Instances) - Method in class weka.classifiers.meta.Decorate
Add new instances to the given set of instances.
addInstances(Instances, Instances) - Method in class weka.classifiers.meta.Fable
Add new instances to the given set of instances.
addInstances(Instances, Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
 
addInstances(SoftClassifiedInstances) - Method in class weka.core.SoftClassifiedInstances
Add another set of instances to this set
addInstances(Instances, Instances) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Add new instances to the given set of instances.
addInstances(Instances, Instances) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Add new instances to the given set of instances.
addLooslyInstance(Instance) - Method in class weka.core.KDTree
Adds one instance to KDTree loosly.
addMLAndCLTransitiveClosure(int[]) - Method in class weka.clusterers.MPCKMeans
adding other inferred ML and CL links to m_ConstraintsHash, from m_NeighborSets
addMLAndCLTransitiveClosure(int[]) - Method in class weka.clusterers.PCKMeans
adding other inferred ML and CL links to m_ConstraintsHash, from m_NeighborSets
addMLAndCLTransitiveClosure(int[]) - Method in class weka.clusterers.PCSoftKMeans
adding other inferred ML and CL links to m_ConstraintsHash, from m_NeighborSets
addMissing(Instances, int, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Add missing values to a dataset.
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
addPairPenalties(InstancePair, int, double[]) - Method in class weka.clusterers.assigners.LPAssigner
accumulate penalties associated with a given constraint
addPlot(PlotData2D) - Method in class weka.gui.visualize.Plot2D
Add a plot to the list of plots to display
addPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Adds a plot.
addPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel
Set a new plot to the visualize panel
addPrediction(NominalPrediction) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Includes a prediction in the confusion matrix.
addPredictions(FastVector) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Includes a whole bunch of predictions in the confusion matrix.
addPropertyChangeListener(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.GenericArrayEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericObjectEditor
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.PropertySheetPanel
Adds a PropertyChangeListener.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SetInstancesPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.BeanVisual
Add a listener for property change events
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassAssignerCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassifierCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.FilterCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.LoaderCustomizer
Add a property change listener
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.StripChartCustomizer
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.experiment.SetupModePanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SetupPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.experiment.SimpleSetupPanel
Adds a PropertyChangeListener who will be notified of value changes.
addPropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel
Adds a PropertyChangeListener who will be notified of value changes.
addRange(int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution
Adds all instances in given range to given bag.
addReference(Instance) - Method in class weka.classifiers.trees.adtree.ReferenceInstances
Adds one instance reference to the end of the set.
addRemoteExperimentListener(RemoteExperimentListener) - Method in class weka.experiment.RemoteExperiment
Add an object to the list of those interested in recieving update information from the RemoteExperiment
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.ClassPanel
Adds a component that will need to be repainted if the user changes the colour of a label.
addRepaintNotify(Component) - Method in class weka.gui.visualize.LegendPanel
Adds a component that will need to be repainted if the user changes the colour of a label.
addResult(String, StringBuffer) - Method in class weka.gui.ResultHistoryPanel
Adds a new result to the result list.
addScaled(HashMapVector, double) - Method in class weka.deduping.metrics.HashMapVector
Destructively add a scaled version of the given vector to the current vector
addStringValue(String) - Method in class weka.core.Attribute
Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addStringValue(Attribute, int) - Method in class weka.core.Attribute
Adds a string value to the list of valid strings for attributes of type STRING and returns the index of the string.
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTestSetProducer
Add a listener for test sets
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Add a test set listener
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.ClassAssigner
 
addTestSetListener(TestSetListener) - Method in class weka.gui.beans.Filter
Add a test set listener
addTestSetListener(TestSetListener) - Method in interface weka.gui.beans.TestSetProducer
Add a listener for test set events
addTextListener(TextListener) - Method in class weka.gui.beans.Classifier
Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Add a text listener
addTextListener(TextListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
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.AbstractTrainAndTestSetProducer
Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.ClassAssigner
 
addTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.Filter
Add a training set listener
addTrainingSetListener(TrainingSetListener) - Method in 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.
addUniquePair(TreeSet, TrainingPair) - Method in class weka.core.metrics.HardPairwiseSelector
Add a pair to the set so that there are no collisions
addUniquePair(TreeSet, StringPair) - Method in class weka.deduping.PairwiseSelector
Add a pair to a TreeSet so that there are no collisions, and no values are erased
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 interface weka.estimators.ConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DKConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.DNConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.DiscreteEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in interface weka.estimators.Estimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.KDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.KKConditionalEstimator
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.MahalanobisEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.NDConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double, double) - Method in class weka.estimators.NNConditionalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.NormalEstimator
Add a new data value to the current estimator.
addValue(double, double) - Method in class weka.estimators.PoissonEstimator
Add a new data value to the current estimator.
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.
advanceCounters() - Method in class weka.experiment.Experiment
Increments iteration counters appropriately.
advanceCounters() - Method in class weka.experiment.RemoteExperiment
overides the one in Experiment
allocateInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
This will allocate more space for input connection information if the arrays for this have been filled up.
allocateInputs() - Method in class weka.classifiers.functions.neural.NeuralNode
This will allocate more space for input connection information if the arrays for this have been filled up.
allocateOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
Allocates more space for output connection information if the arrays have been filled up.
alphaTipText() - Method in class weka.classifiers.bayes.BayesNet
 
appendElements(FastVector) - Method in class weka.core.FastVector
Appends all elements of the supplied vector to this vector.
applyCostMatrix(Instances, Random) - Method in class weka.classifiers.CostMatrix
Applies the cost matrix to a set of instances.
applyFilter() - Method in class weka.gui.explorer.PreprocessPanel
Passes the dataset through the filter that has been configured for use.
applyNodeFilter(Instance) - Method in class weka.classifiers.trees.m5.RuleNode
Apply the attribute filter at this node to a set of supplied instances
arffFileName - Variable in class weka.experiment.Grapher
Name of original file of experimental result data in arff format
arffFileName - Variable in class weka.experiment.NoiseGrapher
Name of original file of experimental result data in arff format
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
askOracle(int, int) - Method in class weka.clusterers.MPCKMeans
Query: oracle replies on link
askOracle(int, int) - Method in class weka.clusterers.PCKMeans
 
assign() - Method in class weka.clusterers.assigners.LPAssigner
The main method
assign() - Method in class weka.clusterers.assigners.MPCKMeansAssigner
The main method
assign() - Method in class weka.clusterers.assigners.RMNAssigner
The main method
assign() - Method in class weka.clusterers.assigners.RandomAssigner
The main method
assign() - Method in class weka.clusterers.assigners.SimpleAssigner
The main method
assign() - Method in class weka.clusterers.assigners.SortedAssigner
The main method
assignAllInstancesToClusters() - Method in class weka.clusterers.MPCKMeans
Classifies the instances using the current clustering, moves must-linked points together (Xing's approach)
assignClusterToInstance(Instance) - Method in class weka.clusterers.SeededKMeans
Classifies the instance using the current clustering
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.m5.RuleNode
Assigns a unique identifier to each node in the tree
assignInstanceToCluster(Instance) - Method in class weka.clusterers.MPCKMeans
Classifies the instance using the current clustering, without considering constraints
assignInstanceToCluster(Instance) - Method in class weka.clusterers.PCKMeans
Classifies the instance using the current clustering, without considering constraints
assignInstanceToCluster(Instance) - Method in class weka.clusterers.PCSoftKMeans
Classifies the instance using the current clustering, without considering constraints
assignInstanceToClusterWithConstraints(int) - Method in class weka.clusterers.MPCKMeans
Classifies the instance using the current clustering, considering constraints
assignInstanceToClusterWithConstraints(int) - Method in class weka.clusterers.PCKMeans
Classifies the instance using the current clustering considering constraints, updates cluster assignments
assignInstanceToClustersWithConstraints(int) - Method in class weka.clusterers.PCSoftKMeans
Classifies the instance using the current clustering considering constraints, updates cluster assignment probs
attIndex() - Method in class weka.classifiers.trees.j48.BinC45Split
Returns index of attribute for which split was generated.
attIndex() - Method in class weka.classifiers.trees.j48.C45Split
Returns index of attribute for which split was generated.
attIndexSetTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the tip text for this property
attrSplit(int, Instances) - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Finds the best splitting point for an attribute in the instances
attrSplit(int, Instances) - Method in interface weka.classifiers.trees.m5.SplitEvaluate
Finds the best splitting point for an attribute in the instances
attrSplit(int, Instances) - Method in class weka.classifiers.trees.m5.YongSplitInfo
Finds the best splitting point for an attribute in the instances
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.
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
attributeEvaluatorTipText() - Method in class weka.attributeSelection.RankSearch
Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.Add
Returns the tip text for this property
attributeIndexTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
 
attributeIndicesTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Copy
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Returns the tip text for this property
attributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.Remove
Returns the tip text for this property
attributeNameTipText() - Method in class weka.filters.unsupervised.attribute.Add
Returns the tip text for this property
attributeSelectionChange(AttributePanelEvent) - Method in interface weka.gui.visualize.AttributePanelListener
Called when the user clicks on an attribute bar
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(int) - Method in class weka.core.Instances
Calculates summary statistics on the values that appear in this set of instances for a specified attribute.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the string describing the attributes the split depends on.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the string describing the attributes the split depends on.
attributeString(Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the string describing the attributes the split depends on.
attributeToDoubleArray(int) - Method in class weka.core.Instances
Gets the value of all instances in this dataset for a particular attribute.
attributeTypeTipText() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns the tip text for this property
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.neural.NeuralNetwork
 
availableHost(int) - Method in class weka.experiment.RemoteExperiment
Pushes a host back onto the queue of available hosts and attempts to launch a waiting experiment (if any).
avgCost() - Method in class weka.classifiers.EnsembleEvaluation
Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
avgCost() - Method in class weka.classifiers.Evaluation
Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
avgProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the average transformation probability

B

BATCH - Static variable in class weka.core.converters.AbstractLoader
 
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
BAYES - Static variable in interface weka.classifiers.bayes.Scoreable
score types
BEAN_EXECUTING - Static variable in class weka.gui.beans.BeanInstance
 
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
 
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
 
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
Bagging - class weka.classifiers.meta.Bagging.
Class for bagging a classifier.
Bagging() - Constructor for class weka.classifiers.meta.Bagging
 
BarHillelMetric - class weka.core.metrics.BarHillelMetric.
Class for performing RCA according to Bar-Hillel's algorithm.
BarHillelMetric(int) - Constructor for class weka.core.metrics.BarHillelMetric
Create a new metric.
BarHillelMetric() - Constructor for class weka.core.metrics.BarHillelMetric
Create a default new metric
BarHillelMetric(int[]) - Constructor for class weka.core.metrics.BarHillelMetric
Creates a new metric which takes specified attributes.
BarHillelMetricMatlab - class weka.core.metrics.BarHillelMetricMatlab.
Class for performing RCA according to Bar-Hillel's algorithm.
BarHillelMetricMatlab(int) - Constructor for class weka.core.metrics.BarHillelMetricMatlab
Create a new metric.
BarHillelMetricMatlab() - Constructor for class weka.core.metrics.BarHillelMetricMatlab
Create a default new metric
BarHillelMetricMatlab(int[]) - Constructor for class weka.core.metrics.BarHillelMetricMatlab
Creates a new metric which takes specified attributes.
BasicDeduper - class weka.deduping.BasicDeduper.
A basic deduper class that takes a set of objects and identifies disjoint subsets of duplicates
BasicDeduper() - Constructor for class weka.deduping.BasicDeduper
 
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
BatchLoader - interface weka.core.converters.BatchLoader.
Marker interface for a loader that can retrieve instances in batch mode
BayesNet - class weka.classifiers.bayes.BayesNet.
Base class for a Bayes Network classifier.
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
 
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() - Constructor for class weka.attributeSelection.BestFirst
Constructor
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
 
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.
BinarySparseInstance - class weka.core.BinarySparseInstance.
Class for storing a binary-data-only instance as a sparse vector.
BinarySparseInstance(Instance) - Constructor for class weka.core.BinarySparseInstance
Constructor that generates a sparse instance from the given instance.
BinarySparseInstance(SparseInstance) - Constructor for class weka.core.BinarySparseInstance
Constructor that copies the info from the given instance.
BinarySparseInstance(double, double[]) - Constructor for class weka.core.BinarySparseInstance
Constructor that generates a sparse instance from the given parameters.
BinarySparseInstance(double, int[], int) - Constructor for class weka.core.BinarySparseInstance
Constructor that inititalizes instance variable with given values.
BinarySparseInstance(int) - Constructor for class weka.core.BinarySparseInstance
Constructor of an instance that sets weight to one, all values to 1, and the reference to the dataset to null.
Blocking - class weka.deduping.blocking.Blocking.
This class takes a set of records, amalgamates them into single strings and creates an inverted index for that collection.
Blocking() - Constructor for class weka.deduping.blocking.Blocking
Construct a vector space from a given set of examples
BoundaryPanel - class weka.gui.boundaryvisualizer.BoundaryPanel.
BoundaryPanel.
BoundaryPanel(int, int) - Constructor for class weka.gui.boundaryvisualizer.BoundaryPanel
Creates a new BoundaryPanel instance.
BoundaryVisualizer - class weka.gui.boundaryvisualizer.BoundaryVisualizer.
BoundaryVisualizer.
BoundaryVisualizer() - Constructor for class weka.gui.boundaryvisualizer.BoundaryVisualizer
Creates a new BoundaryVisualizer instance.
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.
backward(String, String) - Method in class weka.deduping.metrics.AffineProbMetric
Calculate the backward matrices
batchFilterFile(Filter, String[]) - Static method in class weka.filters.Filter
Method for testing filters ability to process multiple batches.
batchFinished() - Method in class weka.filters.Filter
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.AttributeSelection
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.ClassOrder
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.Discretize
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.attribute.NominalToBinary
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.Resample
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.SpreadSubsample
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AddCluster
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.AddNoise
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Discretize
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Normalize
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.RemoveType
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.Standardize
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Signifies that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.attribute.UnitVector
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.Randomize
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.RemoveRange
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.filters.unsupervised.instance.Resample
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.gui.streams.InstanceJoiner
Signify that this batch of input to the filter is finished.
batchFinished() - Method in class weka.gui.streams.InstanceSavePanel
 
batchFinished() - Method in class weka.gui.streams.InstanceTable
 
batchFinished() - Method in class weka.gui.streams.InstanceViewer
 
bestCnt - Variable in class weka.classifiers.lazy.LBR
 
bestInstancesForActiveLearning(int) - Method in interface weka.clusterers.ActiveLearningClusterer
Returns the list of best instances for active learning
bestInstancesForActiveLearning(int) - Method in class weka.clusterers.MPCKMeans
Dummy: not implemented for MPCKMeans
bestInstancesForActiveLearning(int) - Method in class weka.clusterers.PCKMeans
Dummy: not implemented for PCKMeans
bestInstancesForActiveLearning(int) - Method in class weka.clusterers.PCSoftKMeans
Dummy: not implemented for PCSoftKMeans
bestInstancesForActiveLearning(int) - Method in class weka.clusterers.SeededKMeans
Returns the indices of the best numActive instances for active learning
bestPairsForActiveLearning(int) - Method in interface weka.clusterers.ActiveLearningClusterer
Returns the list of best pairs for active learning
bestPairsForActiveLearning(int) - Method in class weka.clusterers.MPCKMeans
Returns the indices of the best numActive instances for active learning
bestPairsForActiveLearning(int) - Method in class weka.clusterers.PCKMeans
Returns the indices of the best numActive instances for active learning
bestPairsForActiveLearning(int) - Method in class weka.clusterers.PCSoftKMeans
Dummy: not implemented for PCSoftKMeans
bestPairsForActiveLearning(int) - Method in class weka.clusterers.SeededKMeans
 
biasTipText() - Method in class weka.classifiers.misc.VFI
Returns the tip text for this property
big - Static variable in class weka.core.Statistics
 
biginv - Static variable in class weka.core.Statistics
 
binValueTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property
binarizeNumericAttributesTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the tip text for this property
binarizeNumericAttributesTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the tip text for this property
binomP(double, double, double) - Method in class weka.classifiers.lazy.LBR
Significance test binomp:
binomialStandardError(double, int) - Static method in class weka.core.Statistics
Computes standard error for observed values of a binomial random variable.
binsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
binsTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Returns the tip text for this property
blank - Variable in class weka.deduping.metrics.AffineProbMetric
A handy constant for insertions/deletions, we treat them as substitution with a null character
blocker(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork
A function used to stop the code that called buildclassifier from continuing on before the user has finished the decision tree.
boost(Instances) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
boost() - Method in class weka.classifiers.trees.adtree.ADTree
Performs a single boosting iteration, using two-class optimized method.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the index of the branch that an instance applies to.
branchInstanceGoesDown(Instance) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the index of the branch that an instance applies to.
bufferInput(Instance) - Method in class weka.filters.Filter
Adds the supplied input instance to the inputformat dataset for later processing.
build(String, String) - Method in class weka.gui.HierarchyPropertyParser
Build a tree from the given property with the given delimitor
buildAssociations(Instances) - Method in class weka.associations.Apriori
Method that generates all large itemsets with a minimum support, and from these all association rules with a minimum confidence.
buildAssociations(Instances) - Method in class weka.associations.Associator
Generates an associator.
buildAttributeMatrix(Instances, int[]) - Method in class weka.core.metrics.BarHillelMetric
 
buildAttributeMatrix(Instances, int[]) - Method in class weka.core.metrics.BarHillelMetricMatlab
 
buildAttributeMatrix(Instances, HashMap) - Method in class weka.core.metrics.XingMetric
 
buildClassifier(Instances) - Method in class weka.classifiers.Classifier
Generates a classifier.
buildClassifier(SoftClassifiedInstances) - Method in interface weka.classifiers.SoftClassifier
Generates a classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.BayesNet
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayes
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.NaiveBayesSimple
Generates the classifier.
buildClassifier(SoftClassifiedInstances) - Method in class weka.classifiers.bayes.NaiveBayesSimpleSoft
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.bayes.SemiSupEM
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.LeastMedSq
Build lms regression
buildClassifier(Instances) - Method in class weka.classifiers.functions.LinearRegression
Builds a regression model for the given data.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Logistic
Builds the model.
buildClassifier(Instances) - Method in class weka.classifiers.functions.SMO
Method for building the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.functions.UnivariateLinearRegression
 
buildClassifier(Instances) - Method in class weka.classifiers.functions.VotedPerceptron
Builds the ensemble of perceptrons.
buildClassifier(Instances) - Method in class weka.classifiers.functions.Winnow
Builds the classifier
buildClassifier(Instances) - Method in class weka.classifiers.functions.neural.NeuralNetwork
Call this function to build and train a neural network for the training data provided.
buildClassifier(Instances) - Method in class weka.classifiers.functions.pace.PaceRegression
Builds a pace regression model for the given data.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.IB1
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.IBk
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.LBR
For lazy learning, building classifier is only to prepare their inputs until classification time.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.LWR
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.lazy.kstar.KStar
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.ActiveDecorate
Build Decorate classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.AdaBoostM1
Boosting method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.AdditiveRegression
Build the classifier on the supplied data
buildClassifier(Instances) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Build the classifier on the dimensionally reduced data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Bagging
Bagging method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.CVParameterSelection
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.ClassificationViaRegression
Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Crate
Build Crate classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.DEC
DEC method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Decorate
Build Decorate classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.DistributionMetaClassifier
Builds a classifier for a set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.meta.Fable
Build Decorate classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.FilteredClassifier
Build the classifier on the filtered data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.LogitBoost
Builds the boosted classifier
buildClassifier(Instances) - Method in class weka.classifiers.meta.MetaCost
Builds the model of the base learner.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiBoostAB
Boosting method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiClassClassifier
Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.MultiScheme
Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
buildClassifier(Instances) - Method in class weka.classifiers.meta.OrdinalClassClassifier
Builds the classifiers.
buildClassifier(Instances) - Method in class weka.classifiers.meta.QBag
QBag method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.QBoost
Boosting method.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.RegressionByDiscretization
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
SemiSupDecorate method.
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.TestEnsembleClassifier
 
buildClassifier(Instances) - Method in class weka.classifiers.meta.ThresholdSelector
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.misc.HyperPipes
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.misc.Prototype
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.misc.PrototypeMetric
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.misc.VFI
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.ConjunctiveRule
Builds a single rule learner with REP dealing with nominal classes or numeric classes.
buildClassifier(Instances) - Method in class weka.classifiers.rules.DecisionTable
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.JRip
Builds Ripper in the order of class frequencies.
buildClassifier(Instances) - Method in class weka.classifiers.rules.OneR
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.Prism
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.Ridor
Builds a ripple-down manner rule learner.
buildClassifier(Instances) - Method in class weka.classifiers.rules.ZeroR
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.rules.part.MakeDecList
Builds dec list.
buildClassifier(Instances) - Method in class weka.classifiers.rules.part.PART
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.sparse.IBkMetric
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Generates the classifier.
buildClassifier(SoftClassifiedInstances) - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparseSoft
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.sparse.SVMlight
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.DecisionStump
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.Id3
Builds Id3 decision tree classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.REPTree
Builds classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomForest
Builds a classifier for a set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.RandomTree
Builds classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.UserClassifier
Call this function to build a decision tree for the training data provided.
buildClassifier(Instances) - Method in class weka.classifiers.trees.adtree.ADTree
Builds a classifier for a set of instances.
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.C45Split
Creates a C4.5-type split on the given data.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Builds the classifier split model for the given set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
Method for building a classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.J48
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.NoSplit
Creates a "no-split"-split for a given set of instances.
buildClassifier(Instances) - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
Method for building a pruneable classifier tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.M5Base
Generates the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.Rule
Generates a single rule or m5 model tree.
buildClassifier(Instances) - Method in class weka.classifiers.trees.m5.RuleNode
Build this node (find an attribute and split point)
buildClassifierUsingResampling(Instances) - Method in class weka.classifiers.meta.AdaBoostM1
Boosting method.
buildClassifierUsingResampling(Instances) - Method in class weka.classifiers.meta.MultiBoostAB
Boosting method.
buildClassifierUsingResampling(Instances) - Method in class weka.classifiers.meta.QBoost
Boosting method.
buildClassifierWithWeights(Instances) - Method in class weka.classifiers.meta.AdaBoostM1
Boosting method.
buildClassifierWithWeights(Instances) - Method in class weka.classifiers.meta.MultiBoostAB
Boosting method.
buildClassifierWithWeights(Instances) - Method in class weka.classifiers.meta.QBoost
Boosting method.
buildClusterer(Instances) - Method in class weka.clusterers.Clusterer
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.Cobweb
Builds the clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.DistributionMetaClusterer
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, int) - Method in class weka.clusterers.HAC
Cluster given instances to form the specified number of clusters.
buildClusterer(Instances, Instances, int, int) - Method in class weka.clusterers.HAC
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, Instances, int, int, int) - Method in class weka.clusterers.HAC
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances) - Method in class weka.clusterers.HAC
Cluster given instances.
buildClusterer(Instances, Instances, int, int, int) - Method in class weka.clusterers.MPCKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, int) - Method in class weka.clusterers.MPCKMeans
Cluster given instances to form the specified number of clusters.
buildClusterer(ArrayList, Instances, Instances, int, int) - Method in class weka.clusterers.MPCKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, Instances, int, int) - Method in class weka.clusterers.MPCKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances) - Method in class weka.clusterers.MPCKMeans
Generates a clusterer.
buildClusterer(Instances, Instances, int, int, int) - Method in class weka.clusterers.PCKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, int) - Method in class weka.clusterers.PCKMeans
Cluster given instances to form the specified number of clusters.
buildClusterer(ArrayList, Instances, Instances, int) - Method in class weka.clusterers.PCKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, Instances, int, int) - Method in class weka.clusterers.PCKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds -- NOT USED FOR PCKMeans!!!
buildClusterer(Instances) - Method in class weka.clusterers.PCKMeans
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.PCSoftKMeans
Generates a clusterer.
buildClusterer(Instances, Instances, int, int, int) - Method in class weka.clusterers.PCSoftKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, int) - Method in class weka.clusterers.PCSoftKMeans
Cluster given instances to form the specified number of clusters.
buildClusterer(ArrayList, Instances, Instances, int) - Method in class weka.clusterers.PCSoftKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, Instances, int, int) - Method in class weka.clusterers.PCSoftKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds -- NOT USED FOR PCSoftKMeans!!!
buildClusterer(Instances, int) - Method in class weka.clusterers.SeededKMeans
Cluster given instances to form the specified number of clusters.
buildClusterer(Instances, Instances, int, int, int) - Method in class weka.clusterers.SeededKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, Instances, int, Instances, int) - Method in class weka.clusterers.SeededKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances, Instances, int, int) - Method in class weka.clusterers.SeededKMeans
Clusters unlabeledData and labeledData (with labels removed), using labeledData as seeds
buildClusterer(Instances) - Method in class weka.clusterers.SeededKMeans
Generates a clusterer.
buildClusterer(Instances) - Method in interface weka.clusterers.SemiSupClusterer
Generates the clustering.
buildClusterer(Instances, Instances, int, int, int) - Method in interface weka.clusterers.SemiSupClusterer
Generates the clustering using labeled seeds
buildClusterer(Instances) - Method in class weka.clusterers.SimpleKMeans
Generates a clusterer.
buildClusterer(Instances) - Method in class weka.clusterers.XMeans
Generates the X-Means 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
buildDeduper(Instances, Instances) - Method in class weka.deduping.BasicDeduper
Given training data, build the metrics required by the deduper
buildDeduper(Instances, Instances) - Method in class weka.deduping.Deduper
Given training data, build the metrics required by the deduper
buildEvaluator(Instances) - Method in class weka.attributeSelection.ASEvaluation
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.CfsSubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Initializes a chi-squared attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.ConsistencySubsetEval
Generates a attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.GainRatioAttributeEval
Initializes a gain ratio attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.InfoGainAttributeEval
Initializes an information gain attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.MatlabICA
Initializes independent components and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.MatlabNMF
Initializes NMF.
buildEvaluator(Instances) - Method in class weka.attributeSelection.MatlabPCA
Initializes principal components and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.OneRAttributeEval
Initializes an information gain attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.PrincipalComponents
Initializes principal components and performs the analysis
buildEvaluator(Instances) - Method in class weka.attributeSelection.ReliefFAttributeEval
Initializes a ReliefF attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.SVMAttributeEval
Initializes the evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Initializes a symmetrical uncertainty attribute evaluator.
buildEvaluator(Instances) - Method in class weka.attributeSelection.WrapperSubsetEval
Generates a attribute evaluator.
buildGenerator(Instances) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Build the data generator
buildGenerator(Instances) - Method in class weka.gui.boundaryvisualizer.EMDataGenerator
Builds the data generator
buildGenerator(Instances) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Initialize the generator using the supplied instances
buildIndex(Instances) - Method in class weka.deduping.blocking.Blocking
Given a list of strings, build the vector space
buildInstanceMetric(int[]) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Generates a new ClassifierInstanceMetric that computes similarity between records using the specified attributes.
buildInstanceMetric(int[]) - Method in class weka.deduping.metrics.InstanceMetric
Generates a new InstanceMetric based on specified attributes.
buildInstanceMetric(int[]) - Method in class weka.deduping.metrics.SumInstanceMetric
Generates a new SumInstanceMetric based on specified attributes.
buildKDTree(Instances) - Method in class weka.core.KDTree
Builds the KDTree.
buildKDTree(Instances, double[][]) - Method in class weka.core.KDTree
Builds the KDTree.
buildMetric(int) - Method in class weka.core.metrics.BarHillelMetric
Generates a new Metric.
buildMetric(int, String[]) - Method in class weka.core.metrics.BarHillelMetric
Generates a new Metric.
buildMetric(Instances) - Method in class weka.core.metrics.BarHillelMetric
Create a new metric for operating on specified instances
buildMetric(int) - Method in class weka.core.metrics.BarHillelMetricMatlab
Generates a new Metric.
buildMetric(int, String[]) - Method in class weka.core.metrics.BarHillelMetricMatlab
Generates a new Metric.
buildMetric(Instances) - Method in class weka.core.metrics.BarHillelMetricMatlab
Create a new metric for operating on specified instances
buildMetric(int) - Method in class weka.core.metrics.KL
Generates a new Metric.
buildMetric(int, String[]) - Method in class weka.core.metrics.KL
Generates a new Metric.
buildMetric(Instances) - Method in class weka.core.metrics.KL
Create a new metric for operating on specified instances
buildMetric(int) - Method in class weka.core.metrics.Metric
Generates a new Metric with a specified number of attributes.
buildMetric(int, String[]) - Method in class weka.core.metrics.Metric
Generates a new Metric.
buildMetric(Instances) - Method in class weka.core.metrics.Metric
Create a new metric for operating on specified instances
buildMetric(int) - Method in class weka.core.metrics.WeightedDotP
Generates a new Metric.
buildMetric(int, String[]) - Method in class weka.core.metrics.WeightedDotP
Generates a new Metric.
buildMetric(Instances) - Method in class weka.core.metrics.WeightedDotP
Create a new metric for operating on specified instances
buildMetric(int) - Method in class weka.core.metrics.WeightedEuclidean
Generates a new Metric.
buildMetric(int, String[]) - Method in class weka.core.metrics.WeightedEuclidean
Generates a new Metric.
buildMetric(Instances) - Method in class weka.core.metrics.WeightedEuclidean
Create a new metric for operating on specified instances
buildMetric(int) - Method in class weka.core.metrics.WeightedMahalanobis
Generates a new Metric.
buildMetric(int, String[]) - Method in class weka.core.metrics.WeightedMahalanobis
Generates a new Metric.
buildMetric(Instances) - Method in class weka.core.metrics.WeightedMahalanobis
Create a new metric for operating on specified instances
buildMetric(int) - Method in class weka.core.metrics.XingMetric
Generates a new Metric.
buildMetric(int, String[]) - Method in class weka.core.metrics.XingMetric
Generates a new Metric.
buildMetric(Instances) - Method in class weka.core.metrics.XingMetric
Create a new metric for operating on specified instances
buildMetric(List) - Method in interface weka.deduping.metrics.DataDependentStringMetric
Given a list of strings, build a metric
buildMetric(List) - Method in class weka.deduping.metrics.JaccardMetric
Given a list of strings, build the vector space
buildMetric(List) - Method in class weka.deduping.metrics.KernelVSMetric
Given a list of strings, build the vector space
buildMetric(List) - Method in class weka.deduping.metrics.VectorSpaceMetric
Given a list of strings, build the vector space
buildRule(Instances) - Method in class weka.classifiers.rules.part.ClassifierDecList
Method for building a pruned partial tree.
buildRule(Instances, Instances) - Method in class weka.classifiers.rules.part.PruneableDecList
Method for building a pruned partial tree.
buildStructure() - Method in class weka.classifiers.bayes.BayesNet
buildStructure determines the network structure/graph of the network.
buildStructure() - 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.
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.
buildTree(int[][], double[][], Instances, double, double[], Instances, double, double, int, int) - Method in class weka.classifiers.trees.REPTree.Tree
Recursively generates a tree.
buildTree(int[][], double[][], Instances, double[], Instances, double, boolean, int[]) - Method in class weka.classifiers.trees.RandomTree
Recursively generates a tree.
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

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.
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
CANNOT_LINK - Static variable in class weka.clusterers.InstancePair
cannot-link
CLASSIFY_CHILD - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Asks for another learning scheme to classify this node.
COMPLETE_LINK - Static variable in class weka.clusterers.HAC
 
CONFIDENCE - Static variable in class weka.associations.Apriori
Metric types.
CONF_INF - Static variable in class weka.experiment.Grapher
errorBar value for error bars using 95% confidence intervals
CONF_INF - Static variable in class weka.experiment.NoiseGrapher
errorBar value for error bars using 95% confidence intervals
CONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This flag is set once the unit has a connection.
CONNECTING - Static variable in class weka.gui.beans.KnowledgeFlow
 
CONNECTIONS - Static variable in class weka.gui.beans.BeanConnection
The list of connections
CONNECTION_FAILED - Static variable in class weka.experiment.RemoteExperiment
 
CONST_AUTOMATIC_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
CONVERSION_EXPONENTIAL - Static variable in class weka.core.metrics.BarHillelMetric
 
CONVERSION_EXPONENTIAL - Static variable in class weka.core.metrics.BarHillelMetricMatlab
 
CONVERSION_EXPONENTIAL - Static variable in class weka.core.metrics.KL
 
CONVERSION_EXPONENTIAL - Static variable in class weka.core.metrics.WeightedDotP
 
CONVERSION_EXPONENTIAL - Static variable in class weka.core.metrics.WeightedEuclidean
 
CONVERSION_EXPONENTIAL - Static variable in class weka.core.metrics.WeightedMahalanobis
 
CONVERSION_EXPONENTIAL - Static variable in class weka.core.metrics.XingMetric
 
CONVERSION_EXPONENTIAL - Static variable in class weka.deduping.metrics.AffineMetric
 
CONVERSION_EXPONENTIAL - Static variable in class weka.deduping.metrics.AffineProbMetric
 
CONVERSION_EXPONENTIAL - Static variable in class weka.deduping.metrics.JaccardMetric
 
CONVERSION_EXPONENTIAL - Static variable in class weka.deduping.metrics.KernelVSMetric
 
CONVERSION_EXPONENTIAL - Static variable in class weka.deduping.metrics.VectorSpaceMetric
 
CONVERSION_LAPLACIAN - Static variable in class weka.core.metrics.BarHillelMetric
We can have different ways of converting from distance to similarity
CONVERSION_LAPLACIAN - Static variable in class weka.core.metrics.BarHillelMetricMatlab
We can have different ways of converting from distance to similarity
CONVERSION_LAPLACIAN - Static variable in class weka.core.metrics.KL
We can have different ways of converting from distance to similarity
CONVERSION_LAPLACIAN - Static variable in class weka.core.metrics.WeightedDotP
We can have different ways of converting from similarity to distance
CONVERSION_LAPLACIAN - Static variable in class weka.core.metrics.WeightedEuclidean
We can have different ways of converting from distance to similarity
CONVERSION_LAPLACIAN - Static variable in class weka.core.metrics.WeightedMahalanobis
We can have different ways of converting from distance to similarity
CONVERSION_LAPLACIAN - Static variable in class weka.core.metrics.XingMetric
We can have different ways of converting from distance to similarity
CONVERSION_LAPLACIAN - Static variable in class weka.deduping.metrics.AffineMetric
We can have different ways of converting from distance to similarity
CONVERSION_LAPLACIAN - Static variable in class weka.deduping.metrics.AffineProbMetric
We can have different ways of converting from distance to similarity
CONVERSION_LAPLACIAN - Static variable in class weka.deduping.metrics.JaccardMetric
We can have different ways of converting from similarity to distance
CONVERSION_LAPLACIAN - Static variable in class weka.deduping.metrics.KernelVSMetric
We can have different ways of converting from similarity to distance
CONVERSION_LAPLACIAN - Static variable in class weka.deduping.metrics.VectorSpaceMetric
We can have different ways of converting from similarity to distance
CONVERSION_UNIT - Static variable in class weka.core.metrics.BarHillelMetric
 
CONVERSION_UNIT - Static variable in class weka.core.metrics.BarHillelMetricMatlab
 
CONVERSION_UNIT - Static variable in class weka.core.metrics.KL
 
CONVERSION_UNIT - Static variable in class weka.core.metrics.WeightedDotP
 
CONVERSION_UNIT - Static variable in class weka.core.metrics.WeightedEuclidean
 
CONVERSION_UNIT - Static variable in class weka.core.metrics.WeightedMahalanobis
 
CONVERSION_UNIT - Static variable in class weka.core.metrics.XingMetric
 
CONVERSION_UNIT - Static variable in class weka.deduping.metrics.AffineMetric
 
CONVERSION_UNIT - Static variable in class weka.deduping.metrics.AffineProbMetric
 
CONVERSION_UNIT - Static variable in class weka.deduping.metrics.JaccardMetric
 
CONVERSION_UNIT - Static variable in class weka.deduping.metrics.KernelVSMetric
 
CONVERSION_UNIT - Static variable in class weka.deduping.metrics.VectorSpaceMetric
 
CONVICTION - Static variable in class weka.associations.Apriori
 
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
 
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
 
CVParameterSelection.CVParameter - class weka.classifiers.meta.CVParameterSelection.CVParameter.
 
CVParameterSelection.CVParameter(String) - Constructor for class weka.classifiers.meta.CVParameterSelection.CVParameter
Constructs a CVParameter.
CVResultsString() - Method in class weka.attributeSelection.AttributeSelection
returns a string summarizing the results of repeated attribute selection runs on splits of a dataset.
CalcNodeScore(int) - Method in class weka.classifiers.bayes.BayesNet
Calc Node Score for given parent set
CalcScoreOfCounts(int[], int, int, Instances) - Method in class weka.classifiers.bayes.BayesNet
utility function used by CalcScore and CalcNodeScore to determine the score based on observed frequencies.
CalcScoreOfCounts2(int[][], int, int, Instances) - Method in class weka.classifiers.bayes.BayesNet
 
CalcScoreWithExtraParent(int, int) - Method in class weka.classifiers.bayes.BayesNet
Calc Node Score With AddedParent
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, Vector, double, double, double[], boolean) - Constructor for class weka.gui.beans.ChartEvent
Creates a new ChartEvent instance.
ChartEvent(Object) - Constructor for class weka.gui.beans.ChartEvent
Creates a new ChartEvent instance.
ChartListener - interface weka.gui.beans.ChartListener.
Interface to something that can process a ChartEvent
CheckClassifier - class weka.classifiers.CheckClassifier.
Class for examining the capabilities and finding problems with classifiers.
CheckClassifier() - Constructor for class weka.classifiers.CheckClassifier
 
CheckOptionHandler - class weka.core.CheckOptionHandler.
Simple command line checking of classes that implement OptionHandler.
CheckOptionHandler() - Constructor for class weka.core.CheckOptionHandler
 
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
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
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
 
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
 
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
 
ClassificationViaRegression - class weka.classifiers.meta.ClassificationViaRegression.
Class for doing classification using regression methods.
ClassificationViaRegression() - Constructor for class weka.classifiers.meta.ClassificationViaRegression
 
Classifier - class weka.classifiers.Classifier.
Abstract classifier.
Classifier() - Constructor for class weka.classifiers.Classifier
 
Classifier - class weka.gui.beans.Classifier.
Bean that wraps around weka.classifiers
Classifier() - Constructor for class weka.gui.beans.Classifier
Creates a new Classifier instance.
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.
ClassifierInstanceMetric - class weka.deduping.metrics.ClassifierInstanceMetric.
ClassifierInstanceMetric class employs a classifier that uses values returned by various StringMetric's on individual fields as features and outputs a confidence value that corresponds to similarity between records
ClassifierInstanceMetric() - Constructor for class weka.deduping.metrics.ClassifierInstanceMetric
A default constructor
ClassifierMetricLearner - class weka.core.metrics.ClassifierMetricLearner.
ClassifierMetricLearner - learns metric parameters by constructing "difference instances" and then learning weights that classify same-class instances as positive, and different-class instances as negative.
ClassifierMetricLearner() - Constructor for class weka.core.metrics.ClassifierMetricLearner
Create a new classifier metric learner
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
 
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
 
ClassifierSubsetEval - class weka.attributeSelection.ClassifierSubsetEval.
Classifier subset evaluator.
ClassifierSubsetEval() - Constructor for class weka.attributeSelection.ClassifierSubsetEval
 
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.
Cluster - class weka.clusterers.Cluster.
 
Cluster() - Constructor for class weka.clusterers.Cluster
Creates an empty cluster
Cluster(int) - Constructor for class weka.clusterers.Cluster
Creates an empty cluster with an id
Cluster(Object) - Constructor for class weka.clusterers.Cluster
Creates a singleton cluster, assignes weight 1 to the instance
Cluster(Object, double) - Constructor for class weka.clusterers.Cluster
Creates a singleton cluster, assignes specified weight to the instance
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
 
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
ClusteringExtractor - class weka.extraction.ClusteringExtractor.
An abstract extractor class.
ClusteringExtractor() - Constructor for class weka.extraction.ClusteringExtractor
A default constructor
Cobweb - class weka.clusterers.Cobweb.
Class implementing the Cobweb and Classit clustering algorithms.
Cobweb() - Constructor for class weka.clusterers.Cobweb
 
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
 
Compute - interface weka.experiment.Compute.
Interface to something that can accept remote connections and execute a task.
ConditionalEstimator - interface weka.estimators.ConditionalEstimator.
Interface for conditional probability estimators.
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(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
 
ConsistencySubsetEval - class weka.attributeSelection.ConsistencySubsetEval.
Consistency attribute subset evaluator.
ConsistencySubsetEval() - Constructor for class weka.attributeSelection.ConsistencySubsetEval
Constructor.
ConsistencySubsetEval.hashKey - class weka.attributeSelection.ConsistencySubsetEval.hashKey.
Class providing keys to the hash table.
ConsistencySubsetEval.hashKey(Instance, int) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey
Constructor for a hashKey
ConsistencySubsetEval.hashKey(double[]) - Constructor for class weka.attributeSelection.ConsistencySubsetEval.hashKey
Constructor for a hashKey
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
 
Copy - class weka.filters.unsupervised.attribute.Copy.
An instance filter that copies a range of attributes in the dataset.
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.
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.
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
 
CramersV(double[][]) - Static method in class weka.core.ContingencyTables
Computes Cramer's V for a contingency table.
Crate - class weka.classifiers.meta.Crate.
CRATE (Committee Regressor using Artificial Training Examples) is a meta-learner for building diverse ensembles of regressors by adding specially constructed artificial training examples.
Crate() - Constructor for class weka.classifiers.meta.Crate
 
CrossValidateAttributes() - Method in class weka.attributeSelection.AttributeSelection
Perform a cross validation for attribute selection.
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
 
CustomPanelSupplier - interface weka.gui.CustomPanelSupplier.
An interface for objects that are capable of supplying their own custom GUI components.
cacheKeyNameTipText() - Method in class weka.experiment.DatabaseResultListener
Returns the tip text for this property
calcEuclideanDis(Instance) - Method in class weka.classifiers.meta.ActiveDecorate
Calculate the disagreement in the ensemble over the label of given examples.
calcEuclideanDis(Instance) - Method in class weka.classifiers.meta.Fable
Calculate the disagreement in the ensemble over the label of given examples.
calcJSDivergence(Instance) - Method in class weka.classifiers.meta.ActiveDecorate
Calculate the disagreement in the ensemble over the label of given examples.
calcJSDivergence(Instance) - Method in class weka.classifiers.meta.Fable
Calculate the disagreement in the ensemble over the label of given examples.
calcKLdivergence(double[], double[]) - Method in class weka.classifiers.meta.ActiveDecorate
Calculate the KL divergence between two probability distributions.
calcKLdivergence(double[], double[]) - Method in class weka.classifiers.meta.Fable
Calculate the KL divergence between two probability distributions.
calcMajorityDis(Instance) - Method in class weka.classifiers.meta.ActiveDecorate
Calculate the disagreement in the ensemble over the label of given examples.
calcMajorityDis(Instance) - Method in class weka.classifiers.meta.Fable
Calculate the disagreement in the ensemble over the label of given examples.
calculateConstraintPenalties() - Method in class weka.clusterers.MPCKMeans
Go through all the constraints and accumulate the penalties from violated constraints
calculateConstraintPenaltiesDotP() - Method in class weka.clusterers.MPCKMeans
Go through all the constraints and accumulate the penalties from violated constraints using distanced-squared penalties
calculateConstraintPenaltiesEuclidean() - Method in class weka.clusterers.MPCKMeans
Go through all the constraints and accumulate the penalties from violated constraints using distanced-squared penalties
calculateConstraintPenaltiesKL() - Method in class weka.clusterers.MPCKMeans
Go through all the constraints and accumulate the penalties from violated constraints using distanced penalties
calculateCutPoints() - Method in class weka.filters.supervised.attribute.Discretize
Generate the cutpoints for each attribute
calculateCutPoints() - Method in class weka.filters.unsupervised.attribute.Discretize
Generate the cutpoints for each attribute
calculateCutPointsByEqualFrequencyBinning(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Set cutpoints for a single attribute.
calculateCutPointsByEqualWidthBinning(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Set cutpoints for a single attribute.
calculateCutPointsByMDL(int, Instances) - Method in class weka.filters.supervised.attribute.Discretize
Set cutpoints for a single attribute using MDL.
calculateDerived() - Method in class weka.experiment.PairedStats
Calculates the derived statistics (significance etc).
calculateDerived() - Method in class weka.experiment.Stats
Tells the object to calculate any statistics that don't have their values automatically updated during add.
calculateDisagreement(Instance) - Method in class weka.classifiers.meta.ActiveDecorate
Calculate the disagreement in the ensemble over the label of given examples depending on the chosen selection scheme.
calculateDisagreement(Instance) - Method in class weka.classifiers.meta.Fable
Calculate the disagreement in the ensemble over the label of given examples depending on the chosen selection scheme.
calculateEntropy(Instance) - Method in class weka.classifiers.DistributionClassifier
 
calculateGradients(double[]) - Method in class weka.core.metrics.GDMetricLearner
A helper function that calculates the current gradient value
calculateLabeledInstanceMargin(Instance) - Method in class weka.classifiers.DistributionClassifier
 
calculateMargin(Instance) - Method in class weka.classifiers.DistributionClassifier
 
calculateMaxCannotLinkDistanceAndSimilarity() - Method in class weka.clusterers.MPCKMeans
Go through the must-link constraints and find the current maximum distance
calculateMaxCannotLinkDistances() - Method in class weka.clusterers.MPCKMeans
Go through the cannot-link constraints and find the current maximum distance
calculateMaxDistances(Instance[][]) - Method in class weka.clusterers.assigners.LPAssigner
 
calculateObjectiveFunction() - Method in class weka.clusterers.MPCKMeans
calculates objective function
calculateObjectiveFunction() - Method in class weka.clusterers.PCKMeans
calculates objective function
calculateObjectiveFunction() - Method in class weka.clusterers.SeededKMeans
calculates objective function
calculateScore(Instance) - Method in class weka.classifiers.meta.Fable
Calculate the feature acquisition score for given examples depending on the chosen selection scheme.
calculateStatistics(Instance, int, int, int) - Method in class weka.experiment.PairedTTester
Computes a paired t-test comparison for a specified dataset between two resultsets.
calculateStdDevsTipText() - Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
canHandleMissing(boolean, boolean, boolean, boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks basic missing value handling of the scheme.
canHandleNClasses(boolean, boolean, int) - Method in class weka.classifiers.CheckClassifier
Checks whether nominal schemes can handle more than two classes.
canHandleZeroTraining(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme can handle zero training instances.
canPredict(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Checks basic prediction of the scheme, for simple non-troublesome datasets.
canTakeOptions() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme can take command line options.
cancelShapes() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Sets the list of shapes to empty and also cancels the current shape being drawn (if applicable).
capacity() - Method in class weka.classifiers.functions.pace.DoubleVector
Gets the capacity of the vector.
capacity() - Method in class weka.classifiers.functions.pace.IntVector
Returns the capacity of the vector
capacity() - Method in class weka.core.FastVector
Returns the capacity of the vector.
cat(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector
Combine two vectors together
cbind(PaceMatrix) - Method in class weka.classifiers.functions.pace.PaceMatrix
Returns a new matrix which binds two matrices with columns.
centerInstances(Instances, int[], double) - Method in class weka.core.KDTree
Assigns instances to centers using KDTree.
changeInputNum(int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Changes the connection value information for one of the connections.
changeLength(double) - Method in class weka.clusterers.AlgVector
Changes the length of a vector.
changeLength(double) - Method in class weka.core.AlgVector
Changes the length of a vector.
changeOutputNum(int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Changes the connection value information for one of the connections.
check(double) - Method in class weka.classifiers.trees.j48.Distribution
Checks if at least two bags contain a minimum number of instances.
checkClusters() - Method in class weka.clusterers.HAC
 
checkForDuplicateKeys(Object[]) - Method in class weka.experiment.AveragingResultProducer
Checks whether any duplicate results (with respect to a key template) were received.
checkForMissing(Instances) - Method in class weka.classifiers.functions.pace.PaceRegression
Checks if instances have a missing value.
checkForMissing(Instance, Instances) - Method in class weka.classifiers.functions.pace.PaceRegression
Checks if an instance has a missing value.
checkForMultipleDifferences() - Method in class weka.experiment.AveragingResultProducer
Checks that the keys for a run only differ in one key field.
checkForNominalAttributes(Instances) - Method in class weka.clusterers.XMeans
Checks for nominal attributes in the dataset.
checkForNominalAttributes() - Method in class weka.core.Instances
Checks for nominal attributes in the dataset
checkForNonBinary(Instances) - Method in class weka.classifiers.functions.pace.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.
checkInstances() - Method in class weka.core.DistanceFunction
Checks the instances if compatibel with the distance function.
checkInstances() - Method in class weka.core.EuclideanDistance
Checks the instances.
checkModel() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Checks if generated model is valid.
checkOptionHandler(OptionHandler, String[]) - Static method in class weka.core.CheckOptionHandler
Runs some diagnostic tests on an optionhandler object.
checkStatus(Object) - Method in interface weka.experiment.Compute
Check on the status of a Task
checkStatus(Object) - Method in class weka.experiment.RemoteEngine
Returns status information on a particular task
chiSquared(double[][], boolean) - Static method in class weka.core.ContingencyTables
Returns chi-squared probability for a given matrix.
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.
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
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.
chunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
class1 - Variable in class weka.deduping.StringPair
 
class2 - Variable in class weka.deduping.StringPair
 
classAttribute() - Method in class weka.core.Instance
Returns class attribute.
classAttribute() - Method in class weka.core.Instances
Returns the class attribute.
classDistributionString(SoftClassifiedInstance) - Method in class weka.classifiers.bayes.SemiSupEM
 
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.
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
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.
classNoiseTestTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
classNoiseTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
classOrderTipText() - Method in class weka.filters.supervised.attribute.ClassOrder
Returns the tip text for this property
classPartitionInstances(Instances) - Method in class weka.classifiers.misc.PrototypeMetric
Partition instances into a set for each class
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.BinC45Split
Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.C45Split
Gets class probability for instance.
classProb(int, Instance, int) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Gets class probability for instance.
classProbLaplace(int, Instance, int) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Gets class probability for instance.
classSelected(String) - Method in class weka.gui.GenericObjectEditor
Called when the user selects an class type to change to.
classValue() - Method in class weka.core.Instance
Returns an instance's class value in internal format.
classifierTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
classifierTipText() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.bayes.SemiSupEM
 
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.CostSensitiveClassifier
 
classifierTipText() - Method in class weka.classifiers.meta.Decorate
Returns the tip text for this property
classifierTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
classifierTipText() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns the tip text for this property
classifierTipText() - Method in class weka.experiment.RegressionSplitEvaluator
Returns the tip text for this property
classifierTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
classifiers() - Method in class weka.classifiers.meta.LogitBoost
Returns the array of classifiers that have been built.
classifyInstance(Instance) - Method in class weka.classifiers.Classifier
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.DistributionClassifier
Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LeastMedSq
Classify a given instance using the best generated LinearRegression Classifier.
classifyInstance(Instance) - Method in class weka.classifiers.functions.LinearRegression
Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.functions.UnivariateLinearRegression
 
classifyInstance(Instance) - Method in class weka.classifiers.functions.Winnow
Outputs the prediction for the given instance.
classifyInstance(Instance) - Method in class weka.classifiers.functions.pace.PaceRegression
Classifies the given instance using the linear regression function.
classifyInstance(Instance) - Method in class weka.classifiers.lazy.IB1
Classifies the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.lazy.LWR
Predicts the class value for the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.AdditiveRegression
Classify an instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.CVParameterSelection
Predicts the class value for the given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Classifies a given instance by choosing the class with the minimum expected misclassification cost.
classifyInstance(Instance) - Method in class weka.classifiers.meta.Crate
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.MetaCost
Classifies a given test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.MultiScheme
Classifies a given instance using the selected classifier.
classifyInstance(double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
classifyInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
classifyInstance(Instance) - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns a predicted class for the test instance.
classifyInstance(Instance) - Method in class weka.classifiers.meta.Stacking
Classifies a given instance using the stacked classifier.
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.Ridor
Classify the test instance with the rule learner
classifyInstance(Instance) - Method in class weka.classifiers.rules.ZeroR
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.MakeDecList
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.rules.part.PART
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.Id3
Classifies a given test instance using the decision tree.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Classifies a given instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.ClassifierTree
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.j48.J48
Classifies an instance.
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.M5Base
Calculates a prediction for an instance using a set of rules or an M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.Rule
Calculates a prediction for an instance using this rule or M5 model tree
classifyInstance(Instance) - Method in class weka.classifiers.trees.m5.RuleNode
Classify an instance using this node.
classifySVMlight(Instance) - Method in class weka.classifiers.sparse.SVMlight
Launch an SVM-light process and classify a given instance
cleanup(Instances) - Method in class weka.classifiers.rules.part.ClassifierDecList
Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
Sets reference to training data to null.
cleanup() - Method in class weka.classifiers.trees.j48.C45ModelSelection
Sets reference to training data to null.
cleanup(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
Cleanup in order to save memory.
cleanupIO() - Method in class weka.classifiers.sparse.SVMlight
The buffered version of SVM-light needs to release some I/O resources before exiting
clear(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
unset a bit in the chromosome
clear() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Clears this hashtable so that it contains no keys.
clear() - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
clear() - Method in class weka.deduping.metrics.HashMapVector
Clears the vector back to all zeros
clone() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
makes a copy of this GABitSet
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.evaluation.ConfusionMatrix
Creates and returns a clone of this object.
clone() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Clones the discrete function
clone() - Method in class weka.classifiers.functions.pace.DoubleVector
Clones the DoubleVector object.
clone() - Method in class weka.classifiers.functions.pace.IntVector
Clones the IntVector object.
clone() - Method in class weka.classifiers.functions.pace.Matrix
Clone the Matrix object.
clone() - Method in class weka.classifiers.functions.pace.PaceMatrix
Clone the PaceMatrix object.
clone() - Method in class weka.classifiers.trees.adtree.ADTree
Creates a clone that is identical to the current tree, but is independent.
clone() - Method in class weka.classifiers.trees.adtree.PredictionNode
Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.Splitter
Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Clones this node.
clone() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Clones this node.
clone() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Allows to clone a model (shallow copy).
clone() - Method in class weka.classifiers.trees.j48.Distribution
Clones distribution (Deep copy of distribution).
clone() - Method in class weka.core.AlgVector
Creates and returns a clone of this object.
clone() - Method in class weka.core.Matrix
Creates and returns a clone of this object.
clone() - Method in class weka.core.metrics.KL
Create a copy of this metric
clone() - Method in class weka.core.metrics.LearnableMetric
Create a copy of this metric
clone() - Method in class weka.core.metrics.Metric
Create a copy of this metric
clone() - Method in class weka.core.metrics.WeightedEuclidean
Create a copy of this metric
clone() - Method in class weka.core.metrics.WeightedMahalanobis
Create a copy of this metric
clone() - Method in class weka.deduping.metrics.AffineMetric
Create a copy of this metric
clone() - Method in class weka.deduping.metrics.AffineProbMetric
Create a copy of this metric
clone() - Method in class weka.deduping.metrics.JaccardMetric
Create a copy of this metric
clone() - Method in class weka.deduping.metrics.KernelVSMetric
Create a copy of this metric
clone() - Method in class weka.deduping.metrics.StringMetric
Create a copy of this metric
clone() - Method in class weka.deduping.metrics.VectorSpaceMetric
Create a copy of this metric
closestPoint(Instance, Instances, int[]) - Method in class weka.core.DistanceFunction
Returns the index of the closest point to the current instance.
cluster() - Method in class weka.clusterers.HAC
Internal method that produces the actual clusters
clusterDistance(Cluster, Cluster) - Method in class weka.clusterers.HAC
internal method that returns the distance between two clusters
clusterDistance(Cluster, Cluster) - Method in class weka.deduping.BasicDeduper
internal method that returns the distance between two clusters
clusterID - Variable in class weka.clusterers.Cluster
corresponds to a cluster ID number, useful for identifying the cluster
clusterInstance(Instance) - Method in class weka.clusterers.Clusterer
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.Cobweb
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.DistributionClusterer
Assigns an instance to a Cluster.
clusterInstance(Instance) - Method in class weka.clusterers.FarthestFirst
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.HAC
Clusters an instance.
clusterInstance(Instance) - Method in class weka.clusterers.MPCKMeans
Checks if instance has to be normalized and classifies the instance using the current clustering
clusterInstance(Instance) - Method in class weka.clusterers.PCKMeans
Checks if instance has to be normalized and classifies the instance using the current clustering
clusterInstance(Instance) - Method in class weka.clusterers.SeededKMeans
Checks if instance has to be normalized and classifies the instance using the current clustering
clusterInstance(Instance) - Method in class weka.clusterers.SimpleKMeans
Classifies a given instance.
clusterInstance(Instance) - Method in class weka.clusterers.XMeans
Classifies a given instance.
clusterProcessedInstance(Instance) - Method in class weka.clusterers.FarthestFirst
clusters an instance that has been through the filters
clusterResultsToString() - Method in class weka.clusterers.ClusterEvaluation
return the results of clustering.
clustererTipText() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Returns the tip text for this property
clustererTipText() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns the tip text for this property
cochransCriterion(double[][]) - Static method in class weka.core.ContingencyTables
Tests if Cochran's criterion is fullfilled for the given contingency table.
codingCost() - Method in class weka.classifiers.trees.j48.C45Split
Returns coding cost for split (used in rule learner).
codingCost() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns coding costs of model.
coefficients() - Method in class weka.classifiers.functions.LinearRegression
Returns the coefficients for this linear model.
coefficients() - Method in class weka.classifiers.functions.pace.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.
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.
committeeSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
compactify() - Method in class weka.core.Instances
Compactifies the set of instances.
compare(Object, Object) - Method in class weka.core.metrics.HardPairwiseSelector.ReverseComparator
 
compare(Object, Object) - Method in class weka.deduping.PairwiseSelector.ReverseComparator
 
compareDatasets(Instances, Instances) - Method in class weka.classifiers.CheckClassifier
Compare two datasets to see if they differ.
compareOptions(String[], String[]) - Static method in class weka.core.CheckOptionHandler
Compares the two given sets of options.
compareTo(Object) - Method in class weka.clusterers.InstancePair
Compare function
compareTo(InstancePair) - Method in class weka.clusterers.InstancePair
Compare function
compareTo(Object) - Method in class weka.core.metrics.TrainingPair
 
compareTo(Object) - Method in class weka.datagenerators.TextSource.Int
 
compareTo(Object) - Method in class weka.deduping.InstancePair
 
compareTo(Object) - Method in class weka.deduping.StringPair
 
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the string describing the comparision the split depends on for a particular branch.
comparisonString(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the string describing the comparision the split depends on for a particular branch.
complexityParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
computeAccuracy(Instances) - Method in class weka.classifiers.meta.DEC
Computes classification accuracy on the given data.
computeAccuracy(Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
Computes classification accuracy on the given data.
computeEnsembleMeasures(Instances) - Method in class weka.classifiers.EnsembleClassifier
Compute ensemble measures.
computeEnsembleWt(Classifier, Instances) - Method in class weka.classifiers.meta.DEC
Compute ensemble weight.
computeEnsembleWt(Classifier, Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
Compute ensemble weight.
computeError(Instances) - Method in class weka.classifiers.meta.ActiveDecorate
Computes the error in classification on the given data.
computeError(Instances) - Method in class weka.classifiers.meta.Crate
Computes the error in prediction on the given data.
computeError(Instances) - Method in class weka.classifiers.meta.DEC
Computes the error in classification on the given data.
computeError(Instances) - Method in class weka.classifiers.meta.Decorate
Computes the error in classification on the given data.
computeError(Instances) - Method in class weka.classifiers.meta.Fable
Computes the error in classification on the given data.
computeError(Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
Computes the error in classification on the given data.
computeIDFandStringLengths() - Method in class weka.deduping.blocking.Blocking
Compute the IDF factor for every token in the index and the length of the string vector for every string referenced in the index.
computeIDFandStringLengths() - Method in class weka.deduping.metrics.KernelVSMetric
Compute the IDF factor for every token in the index and the length of the string vector for every string referenced in the index.
computeIDFandStringLengths() - Method in class weka.deduping.metrics.VectorSpaceMetric
Compute the IDF factor for every token in the index and the length of the string vector for every string referenced in the index.
computeStats(Instances) - Method in class weka.classifiers.meta.ActiveDecorate
Compute and store statistics required for generating artificial data.
computeStats(Instances) - Method in class weka.classifiers.meta.Crate
Compute and store statistics required for generating artificial data.
computeStats(Instances) - Method in class weka.classifiers.meta.DEC
Find and store mean and std devs for numeric attributes.
computeStats(Instances) - Method in class weka.classifiers.meta.Decorate
Compute and store statistics required for generating artificial data.
computeStats(Instances) - Method in class weka.classifiers.meta.Fable
Compute and store statistics required for generating artificial data.
computeStats(Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
Find and store mean and std devs for numeric attributes.
concatStringArray(String[]) - Static method in class weka.classifiers.misc.PrototypeMetric
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.classifiers.sparse.IBkMetric
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.classifiers.sparse.SVMlight
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.clusterers.SeededKMeans
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.core.metrics.AttrEvalMetricLearner
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.core.metrics.ClassifierMetricLearner
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.core.metrics.GDMetricLearner
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.deduping.BasicDeduper
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.deduping.metrics.ClassifierInstanceMetric
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.deduping.metrics.SumInstanceMetric
A little helper to create a single String from an array of Strings
concatStringArray(String[]) - Static method in class weka.extraction.ClusteringExtractor
A little helper to create a single String from an array of Strings
confIntErrorBars - Variable in class weka.experiment.Grapher
Set if desire error bars based on 95% confidence intervals
confIntErrorBars - Variable in class weka.experiment.NoiseGrapher
Set if desire error bars based on 95% confidence intervals
confidenceForRule(ItemSet, ItemSet) - Static method in class weka.associations.ItemSet
Outputs the confidence for a rule.
confusionMatrix() - Method in class weka.classifiers.EnsembleEvaluation
Returns a copy of the confusion matrix.
confusionMatrix() - Method in class weka.classifiers.Evaluation
Returns a copy of the confusion matrix.
connect(NeuralConnection, NeuralConnection) - Static method in class weka.classifiers.functions.neural.NeuralConnection
Connects two units together.
connectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
This will connect the specified unit to be an input to this unit.
connectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralNode
This will connect the specified unit to be an input to this unit.
connectOutput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
This will connect the specified unit to be an output to this unit.
connectToDatabase() - Method in class weka.experiment.DatabaseUtils
Opens a connection to the database
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.AbstractTestSetProducer
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.AbstractTrainingSetProducer
Returns true if, at this time, the object will accept a connection according 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.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.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 class weka.gui.beans.StripChart
Returns true if, at this time, the object will accept a connection via the named event
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractEvaluator
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTestSetProducer
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in interface weka.gui.beans.BeanCommon
Notify this object that it has been registered as a listener with a source for recieving events described by the named event This object is responsible for recording this fact.
connectionNotification(String, Object) - Method in class weka.gui.beans.ClassAssigner
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.Classifier
Notify this object that it has been registered as a listener with a source with respect to the named event
connectionNotification(String, Object) - Method in class weka.gui.beans.Filter
Notify this object that it has been registered as a listener with a source with respect to the supplied event name
connectionNotification(String, Object) - Method in class weka.gui.beans.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.
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(String) - Method in class weka.gui.HierarchyPropertyParser
Whether the HierarchyPropertyParser contains the given string
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Tests if the specified double is a key in this hashtable.
containsKey(double) - Method in class weka.classifiers.lazy.kstar.KStarCache
Checks if the specified key maps with an entry in the cache table
contents(Object) - Method in class weka.core.Queue.QueueNode
Sets the contents of the node.
contents() - Method in class weka.core.Queue.QueueNode
Returns the contents in the node.
context() - Method in class weka.gui.HierarchyPropertyParser
The context of the current node, i.e.
convergenceCheck(double, double, boolean) - Method in class weka.clusterers.MPCKMeans
checks for convergence of NR iteration
convertFrequency(double, double, String) - Method in class weka.core.metrics.KL
Given a frequency of a given token in a document, convert it to a probability value for that document's distribution
convertInstance(Instance) - Method in interface weka.attributeSelection.AttributeTransformer
Transforms an instance in the format of the original data to the transformed space
convertInstance(Instance) - Method in class weka.attributeSelection.MatlabICA
Transform an instance in original (unormalized) format.
convertInstance(Instance) - Method in class weka.attributeSelection.MatlabNMF
Transform an instance in original (unnormalized) format.
convertInstance(Instance) - Method in class weka.attributeSelection.MatlabPCA
Transform an instance in original (unormalized) format.
convertInstance(Instance) - Method in class weka.attributeSelection.PrincipalComponents
Transform an instance in original (unormalized) format.
convertInstance(Instance) - Method in interface weka.core.metrics.InstanceConverter
Take an instance and convert it for use by the metric
convertInstance(Instance) - Method in class weka.core.metrics.KL
 
convertInstance(Instance) - Method in class weka.filters.supervised.attribute.AttributeSelection
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.supervised.attribute.Discretize
Convert a single instance over.
convertInstance(Instance) - Method in class weka.filters.unsupervised.attribute.Discretize
Convert a single instance over.
convertNewLines(String) - Static method in class weka.core.Utils
Converts carriage returns and new lines in a string into \r and \n.
convertToAttribX(double) - Method in class weka.gui.visualize.Plot2D
convert a Panel x coordinate to a raw x value.
convertToAttribY(double) - Method in class weka.gui.visualize.Plot2D
convert a Panel y coordinate to a raw y value.
convertToPanelX(double) - Method in class weka.gui.visualize.Plot2D
Convert an raw x value to Panel x coordinate.
convertToPanelY(double) - Method in class weka.gui.visualize.Plot2D
Convert an raw y value to Panel y coordinate.
convertToRelativePath(File) - Method in class weka.gui.experiment.DatasetListPanel
Converts a File's absolute path to a path relative to the user (ie start) directory
convictionForRule(ItemSet, ItemSet, int, int) - Method in class weka.associations.ItemSet
Outputs the conviction for a rule.
copy() - Method in class weka.classifiers.functions.pace.DoubleVector
Makes a deep copy of the vector
copy() - Method in class weka.classifiers.functions.pace.IntVector
Makes a deep copy of the vector
copy() - Method in class weka.classifiers.functions.pace.Matrix
Make a deep copy of a matrix
copy() - Method in class weka.classifiers.rules.JRip.RipperRule
Get a shallow copy of this rule
copy() - Method in class weka.classifiers.rules.Rule
Get a shallow copy of this rule
copy() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Makes a copy of this CorrelationSplitInfo object
copy() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
makes a copy of the SplitEvaluate object
copy() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Makes a copy of this SplitInfo object
copy() - Method in class weka.core.Attribute
Produces a shallow copy of this attribute.
copy() - Method in class weka.core.BinarySparseInstance
Produces a shallow copy of this instance.
copy() - Method in interface weka.core.Copyable
This method produces a shallow copy of an object.
copy() - Method in class weka.core.FastVector
Produces a shallow copy of this vector.
copy() - Method in class weka.core.Instance
Produces a shallow copy of this instance.
copy() - Method in class weka.core.SoftClassifiedFullInstance
Produces a shallow copy of this instance.
copy() - Method in class weka.core.SoftClassifiedSparseInstance
Produces a shallow copy of this instance.
copy() - Method in class weka.core.SparseInstance
Produces a shallow copy of this instance.
copy() - Method in class weka.deduping.metrics.HashMapVector
Produce a copy of this HashMapVector with a new HashMap and new Weight's
copyElements(Cluster) - Method in class weka.clusterers.Cluster
Adds elements from another cluster to this cluster.
copyElements() - Method in class weka.core.FastVector
Clones the vector and shallow copies all its elements.
copyObject(Object) - Method in class weka.gui.GenericObjectEditor.GOEPanel
Makes a copy of an object using serialization
copyObject(Object) - Method in class weka.gui.experiment.AlgorithmListPanel
Makes a copy of an object using serialization
copyStringValues(Instance, boolean, Instances, Instances) - Method in class weka.filters.Filter
Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
copyStringValues(Instance, boolean, Instances, int[], Instances, int[]) - Method in class weka.filters.Filter
Takes string values referenced by an Instance and copies them from a source dataset to a destination dataset.
correct() - Method in class weka.classifiers.EnsembleEvaluation
Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correct() - Method in class weka.classifiers.Evaluation
Gets the number of instances correctly classified (that is, for which a correct prediction was made).
correct() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of correct classifications (that is, for which a correct prediction was made).
correctBuildInitialisation(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme correctly initialises models when buildClassifier is called.
correlation(double[], double[], int) - Static method in class weka.core.Utils
Returns the correlation coefficient of two double vectors.
correlation - Variable in class weka.experiment.PairedStats
The correlation coefficient
correlationCoefficient() - Method in class weka.classifiers.EnsembleEvaluation
Returns the correlation coefficient if the class is numeric.
correlationCoefficient() - Method in class weka.classifiers.Evaluation
Returns the correlation coefficient if the class is numeric.
cosineTo(HashMapVector) - Method in class weka.deduping.metrics.HashMapVector
Computes cosine of angle to otherVector.
cosineTo(HashMapVector, double) - Method in class weka.deduping.metrics.HashMapVector
Computes cosine of angle to otherVector when also given otherVector's Euclidian length (Allows saving computation if length already known.
cost - Variable in class weka.clusterers.InstancePair
cost of violating constraint
costDistance(String, String) - Method in class weka.deduping.metrics.AffineProbMetric
Calculate affine gapped distance using learned costs
costMatrixSourceTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
costMatrixTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
count - Variable in class weka.deduping.blocking.TokenInstanceOccurrence
The number of times it occurs in the Document
count - Variable in class weka.experiment.PairedStats
The number of data points seen
count - Variable in class weka.experiment.Stats
The number of values seen
countData() - Method in class weka.classifiers.rules.RuleStats
Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule
countData(int, Instances, double[][]) - Method in class weka.classifiers.rules.RuleStats
Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...(index-1), and the statistics of these rules are provided.
This procedure is typically useful when a temporary object of RuleStats is constructed in order to efficiently calculate the relative DL of rule in position index, thus all other stuff is not needed.
countPresentClasses(Instances) - Method in class weka.deduping.DedupingEvaluation
A helper function that determines how many classes are actually represented in an Instances object
countsForInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet
Calculates the counts for Dirichlet distribution for the class membership probabilities for the given test instance.
covers(Instance) - Method in class weka.classifiers.rules.JRip.RipperRule
Whether the instance covered by this rule
covers(Instance) - Method in class weka.classifiers.rules.Rule
Whether the instance covered by this rule
create(Reader) - Method in class weka.gui.treevisualizer.TreeBuild
This will build A node structure from the dotty format passed.
createCentroids() - Method in class weka.clusterers.PCKMeans
Creates the global cluster centroid
createCentroids() - Method in class weka.clusterers.PCSoftKMeans
Creates the global cluster centroid
createChooseClassButton() - Method in class weka.gui.GenericObjectEditor
Creates a button that when clicked will enable the user to change the class of the object being edited.
createDefaultPanel() - Method in class weka.gui.PropertyPanel
Creates the default style of panel for editors that do not supply their own.
createDiffInstance(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetric
Create an instance with features corresponding to components of the two given instances
createDiffInstance(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetricMatlab
Create an instance with features corresponding to components of the two given instances
createDiffInstance(Instance, Instance) - Method in class weka.core.metrics.KL
Create an instance with features corresponding to dot-product components of the two given instances
createDiffInstance(Instance, Instance) - Method in class weka.core.metrics.LearnableMetric
Create an instance with features corresponding to components of the two given instances
createDiffInstance(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Create an Instance with features corresponding to internal "features": for x'y returns an instance with the following features: [x1*y1, x2*y2, ..., xn*yn]
createDiffInstance(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Create an instance with features corresponding to dot-product components of the two given instances
createDiffInstance(Instance, Instance) - Method in class weka.core.metrics.WeightedMahalanobis
Create an instance with features corresponding to dot-product components of the two given instances
createDiffInstance(Instance, Instance) - Method in class weka.core.metrics.XingMetric
Create an instance with features corresponding to components of the two given instances
createDiffInstanceJS(Instance, Instance) - Method in class weka.core.metrics.KL
Create an instance with features corresponding to JS components
createDiffInstanceJSNonSparse(Instance, Instance) - Method in class weka.core.metrics.KL
Create a nonsparse instance with features corresponding to dot-product components of the two given instances
createDiffInstanceJSSparse(SparseInstance, SparseInstance) - Method in class weka.core.metrics.KL
Create a sparse instance with features corresponding to dot-product components of the two given instances
createDiffInstanceJSSparseNonSparse(SparseInstance, Instance) - Method in class weka.core.metrics.KL
Create an instance with features corresponding to dot-product components of the two given instances
createDiffInstanceLists(Instances, LearnableMetric, int, double) - Method in class weka.core.metrics.MetricLearner
Create two lists: one of diff-instances belonging to same class, another of diff-instances belonging to different classes.
createDiffInstanceNonSparse(Instance, Instance) - Method in class weka.core.metrics.KL
Create a nonsparse instance with features corresponding to dot-product components of the two given instances
createDiffInstanceNonSparse(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Create an Instance with features corresponding to internal "features": for x'y returns an instance with the following features: [x1*y1, x2*y2, ..., xn*yn]
createDiffInstanceNonSparse(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Create a nonsparse instance with features corresponding to dot-product components of the two given instances
createDiffInstanceSparse(SparseInstance, SparseInstance) - Method in class weka.core.metrics.KL
Create a sparse instance with features corresponding to dot-product components of the two given instances
createDiffInstanceSparse(SparseInstance, SparseInstance) - Method in class weka.core.metrics.WeightedDotP
Create a SparseInstance with features corresponding to internal "features": for x'y returns an instance with the following features: [x1*y1, x2*y2, ..., xn*yn]
createDiffInstanceSparse(SparseInstance, SparseInstance) - Method in class weka.core.metrics.WeightedEuclidean
Create a sparse instance with features corresponding to dot-product components of the two given instances
createDiffInstanceSparseNonSparse(SparseInstance, Instance) - Method in class weka.core.metrics.KL
Create an instance with features corresponding to dot-product components of the two given instances
createDiffInstanceSparseNonSparse(SparseInstance, Instance) - Method in class weka.core.metrics.WeightedDotP
Create a SparseInstance with features corresponding to internal "features": for x'y returns an instance with the following features: [x1*y1, x2*y2, ..., xn*yn]
createDiffInstanceSparseNonSparse(SparseInstance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Create an instance with features corresponding to dot-product components of the two given instances
createDiffInstances(ArrayList, ArrayList) - Method in class weka.core.metrics.MetricLearner
Given two ArrayList of pairs of same-class and different-class diff-instances, create an Instances dataset of DiffInstances
createDiffInstances(ArrayList, LearnableMetric) - Method in class weka.core.metrics.MetricLearner
Given an ArrayList of TrainingPair's of same-class and different-class diff-instances, create an Instances dataset of DiffInstances
createDiffMatrix(Instance, Instance) - Method in class weka.core.metrics.WeightedMahalanobis
Create a matrix of the form (inst1 - inst2) * (inst1 - inst2)^T
createDistanceMatrix() - Method in class weka.clusterers.HAC
Fill the distance matrix with values using the metric
createDistanceMatrix() - Method in class weka.deduping.BasicDeduper
Fill the distance matrix with values using the metric
createDocFillerMap(Instances) - Method in class weka.extraction.ExtractionEvaluation
Given a set of data, create a HashMap which maps each Instance's uniqueID to a fillerPositionListMap.
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.
createFileChooser() - Method in class weka.gui.GenericObjectEditor.GOEPanel
Creates the file chooser the user will use to save/load files with.
createGlobalCentroids() - Method in class weka.clusterers.MPCKMeans
Creates the global cluster centroid
createInstance(InstancePair, int[], StringMetric[][]) - Method in class weka.deduping.PairwiseSelector
Create a nonsparse instance with features corresponding to the metric values between used fields of the two given instances
createNegPairList() - Method in class weka.deduping.PairwiseSelector
Populate m_negPairList with negative InstancePair's
createNodes(DefaultMutableTreeNode) - Method in class weka.gui.PropertySelectorDialog
Creates the property tree below the current node.
createOptions() - Method in class weka.classifiers.meta.CVParameterSelection
Create the options array to pass to the classifier.
createPairInstance(String, String) - Method in class weka.deduping.metrics.KernelVSMetric
Given a pair of strings and a label (same-class/different-class), create a diff-instance
createPairList(Instances, int, int) - Method in class weka.core.metrics.GDMetricLearner
Create a lists of pairs of two kinds: pairs of instances belonging to same class, and pairs of instances belonging to different classes.
createPairList(Instances, int, int, Metric) - Method in class weka.core.metrics.HardPairwiseSelector
Provide an array of metric pairs metric using given training instances
createPairList(Instances, int, int, Metric) - Method in class weka.core.metrics.PairwiseSelector
Provide an array of metric pairs metric using given training instances
createPairList(Instances, int, int, Metric) - Method in class weka.core.metrics.RandomPairwiseSelector
Provide an array of metric pairs metric using given training instances
createPairSet() - Method in class weka.deduping.blocking.Blocking
Populate m_pairSet with all the instancePairs that contain common tokens, so that they can be retrieved in the order of decreasing similarity later
createPosPairList() - Method in class weka.deduping.PairwiseSelector
Populate m_posPairList with all positive InstancePair's
createRandomTrainInstancePair(HashSet, HashMap) - Method in class weka.deduping.PairwiseSelector
 
createResultsTable(ResultProducer, String) - Method in class weka.experiment.DatabaseUtils
Creates a results table for the supplied result producer.
createSeeds(ArrayList) - Method in class weka.clusterers.Seeder
Set the current seeds
createTree(HierarchyPropertyParser) - Method in class weka.gui.GenericObjectEditor
Creates a JTree from an object heirarchy.
crossValidate() - Method in class weka.classifiers.sparse.IBkMetric
Select the best value for k by hold-one-out cross-validation.
crossValidateModel(Classifier, Instances, int) - Method in class weka.classifiers.EnsembleEvaluation
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 weka.classifiers.EnsembleEvaluation
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
crossValidateModel(Classifier, Instances, int) - 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 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[]) - Static method in class weka.clusterers.ClusterEvaluation
Performs a cross-validation for a distribution clusterer on a set of instances.
crossoverProbTipText() - Method in class weka.attributeSelection.GeneticSearch
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
currentLength() - Method in class weka.classifiers.sparse.IBkMetric.NeighborList
Gets the current length of the list.
cutoffTipText() - Method in class weka.clusterers.Cobweb
Returns the tip text for this property

D

DATASET_FIELD_NAME - Static variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.ActiveLearningCurveCVResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.ExtractionResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.LearningCurveCrossValidationResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.SemiSupCrossValidationResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
DATASET_FIELD_NAME - Static variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
DATE - Static variable in class weka.core.Attribute
Constant set for attributes with date values.
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
DEC - class weka.classifiers.meta.DEC.
Class for creating Diverse Ensembles of a Classifier Valid options are:
DEC() - Constructor for class weka.classifiers.meta.DEC
 
DEFAULT_NUM_PRECISION - Static variable in class weka.classifiers.bayes.NaiveBayes
The precision parameter used for numeric attributes
DEFAULT_SHAPE_SIZE - Static variable in class weka.gui.visualize.Plot2D
 
DEST_ARFF_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
DEST_CSV_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
DEST_DATABASE_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
The strings used to identify the combo box choices
DFS() - Method in class weka.clusterers.MPCKMeans
Main Depth First Search routine
DFS() - Method in class weka.clusterers.PCKMeans
Main Depth First Search routine
DFS() - Method in class weka.clusterers.PCSoftKMeans
Main Depth First Search routine
DFS_VISIT(int, int[]) - Method in class weka.clusterers.MPCKMeans
Recursive subroutine for DFS
DFS_VISIT(int, int[]) - Method in class weka.clusterers.PCKMeans
Recursive subroutine for DFS
DFS_VISIT(int, int[]) - Method in class weka.clusterers.PCSoftKMeans
Recursive subroutine for DFS
DIAMOND_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
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
DONT_CARE_LINK - Static variable in class weka.clusterers.InstancePair
don't care
DRIVERS - Static variable in class weka.experiment.DatabaseUtils
Holds the jdbc drivers to be used (only to stop them being gc'ed)
D_CONVCHCLOSER - Static variable in class weka.clusterers.XMeans
 
D_CURR - Static variable in class weka.clusterers.XMeans
 
D_FOLLOWSPLIT - Static variable in class weka.clusterers.XMeans
 
D_GENERAL - Static variable in class weka.clusterers.XMeans
 
D_ITERCOUNT - Static variable in class weka.clusterers.XMeans
 
D_KDTREE - Static variable in class weka.clusterers.XMeans
 
D_METH_MISUSE - Static variable in class weka.clusterers.XMeans
 
D_PRINTCENTERS - Static variable in class weka.clusterers.XMeans
 
D_RANDOMVEKTOR - Static variable in class weka.clusterers.XMeans
 
DataDependentStringMetric - interface weka.deduping.metrics.DataDependentStringMetric.
An interface for data-dependent metrics that are built on supplied data
DataGenerator - interface weka.gui.boundaryvisualizer.DataGenerator.
Interface to something that can generate new instances based on a set of input instances
DataSetEvent - class weka.gui.beans.DataSetEvent.
Event encapsulating a data set
DataSetEvent(Object, Instances) - Constructor for class weka.gui.beans.DataSetEvent
 
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
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
 
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.
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
DatasetListPanel - class weka.gui.experiment.DatasetListPanel.
This panel controls setting a list of datasets for an experiment to iterate over.
DatasetListPanel(Experiment) - Constructor for class weka.gui.experiment.DatasetListPanel
Creates the dataset list panel with the given experiment.
DatasetListPanel() - Constructor for class weka.gui.experiment.DatasetListPanel
Create the dataset list panel initially disabled.
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() - Constructor for class weka.classifiers.rules.DecisionTable
Constructor for a DecisionTable
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.hashKey - class weka.classifiers.rules.DecisionTable.hashKey.
Class providing keys to the hash table
DecisionTable.hashKey(Instance, int) - Constructor for class weka.classifiers.rules.DecisionTable.hashKey
Constructor for a hashKey
DecisionTable.hashKey(double[]) - Constructor for class weka.classifiers.rules.DecisionTable.hashKey
Constructor for a hashKey
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
 
Deduper - class weka.deduping.Deduper.
An abstract class that takes a set of objects and identifies disjoint subsets of duplicates
Deduper() - Constructor for class weka.deduping.Deduper
 
DeduperSplitEvaluator - class weka.experiment.DeduperSplitEvaluator.
A SplitEvaluator that produces results for a deduper scheme on a nominal class attribute.
DeduperSplitEvaluator() - Constructor for class weka.experiment.DeduperSplitEvaluator
No args constructor.
DedupingEvaluation - class weka.deduping.DedupingEvaluation.
Class for evaluating deduping
DedupingEvaluation() - Constructor for class weka.deduping.DedupingEvaluation
A default constructor
DedupingPRCurveCVResultProducer - class weka.experiment.DedupingPRCurveCVResultProducer.
N-fold cross-validation learning curve for deduping applications
DedupingPRCurveCVResultProducer() - Constructor for class weka.experiment.DedupingPRCurveCVResultProducer
 
DedupingPRCurveCVResultProducerSplit - class weka.experiment.DedupingPRCurveCVResultProducerSplit.
N-fold cross-validation learning curve for deduping applications
DedupingPRCurveCVResultProducerSplit() - Constructor for class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
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)
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() - Constructor for class weka.filters.supervised.attribute.Discretize
Constructor - initialises the filter
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.unsupervised.attribute.Discretize
Constructor - initialises the filter
DistanceFunction - class weka.core.DistanceFunction.
Abstract class to implement a distance function.
DistanceFunction() - Constructor for class weka.core.DistanceFunction
Constructs a distance function object.
DistanceFunction(Instances) - Constructor for class weka.core.DistanceFunction
Constructs a distance function object.
DistanceFunction(Instances, double[][]) - Constructor for class weka.core.DistanceFunction
Constructs a distance function object.
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(int, int) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates and initializes a new distribution.
Distribution(double[][]) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates and initializes a new distribution using the given array.
Distribution(Instances) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates a distribution with only one bag according to instances in source.
Distribution(Instances, ClassifierSplitModel) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates a distribution according to given instances and split model.
Distribution(Distribution) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates distribution with only one bag by merging all bags of given distribution.
Distribution(Distribution, int) - Constructor for class weka.classifiers.trees.j48.Distribution
Creates distribution with two bags by merging all bags apart of the indicated one.
DistributionClassifier - class weka.classifiers.DistributionClassifier.
Abstract classification model that produces (for each test instance) an estimate of the membership in each class (ie.
DistributionClassifier() - Constructor for class weka.classifiers.DistributionClassifier
 
DistributionClusterer - class weka.clusterers.DistributionClusterer.
Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
DistributionClusterer() - Constructor for class weka.clusterers.DistributionClusterer
 
DistributionMetaClassifier - class weka.classifiers.meta.DistributionMetaClassifier.
Class for wrapping a Classifier to make it return a distribution.
DistributionMetaClassifier() - Constructor for class weka.classifiers.meta.DistributionMetaClassifier
Default constructor.
DistributionMetaClassifier(Classifier) - Constructor for class weka.classifiers.meta.DistributionMetaClassifier
Contructs a DistributionMetaClassifier wrapping a given Classifier.
DistributionMetaClusterer - class weka.clusterers.DistributionMetaClusterer.
Class for wrapping a Clusterer to make it return a distribution.
DistributionMetaClusterer() - Constructor for class weka.clusterers.DistributionMetaClusterer
Default constructor.
DistributionMetaClusterer(Clusterer) - Constructor for class weka.clusterers.DistributionMetaClusterer
Contructs a DistributionMetaClusterer wrapping a given Clusterer.
DoActiveTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
DoActiveTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the tip text for this property
DoubleVector - class weka.classifiers.functions.pace.DoubleVector.
 
DoubleVector() - Constructor for class weka.classifiers.functions.pace.DoubleVector
Constructs a null vector.
DoubleVector(int) - Constructor for class weka.classifiers.functions.pace.DoubleVector
Constructs an n-vector of zeros.
DoubleVector(int, double) - Constructor for class weka.classifiers.functions.pace.DoubleVector
Constructs a constant n-vector.
DoubleVector(double[]) - Constructor for class weka.classifiers.functions.pace.DoubleVector
Constructs a vector directly from a double array
Drawable - interface weka.core.Drawable.
Interface to something that can be drawn as a graph.
DynamicArrayOfPosInt - class weka.core.DynamicArrayOfPosInt.
Implements a dynamic array of positive integers.
DynamicArrayOfPosInt() - Constructor for class weka.core.DynamicArrayOfPosInt
Constructor
DynamicArrayOfPosInt(int) - Constructor for class weka.core.DynamicArrayOfPosInt
Constructor with size larger than 1.
data - Variable in class weka.experiment.Grapher
Experimental result data in arff format
data - Variable in class weka.experiment.NoiseGrapher
Experimental result data in arff format
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.
databaseURLTipText() - Method in class weka.experiment.DatabaseUtils
Returns the tip text for this property
dataset() - Method in class weka.core.Instance
Returns the dataset this instance has access to.
dataset - Variable in class weka.experiment.Grapher
The name of the dataset to plot performance for
dataset - Variable in class weka.experiment.NoiseGrapher
The name of the dataset to plot performance for
datasetIntegrity(boolean, boolean, boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme alters the training dataset during training.
datasets - Variable in class weka.experiment.Grapher
Names of datasets in data
datasets - Variable in class weka.experiment.NoiseGrapher
Names of datasets in data
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.
debugTipText() - Method in class weka.attributeSelection.RaceSearch
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.meta.RacedIncrementalLogitBoost
 
debugTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the tip text for this property
decayTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
decimal - Variable in class weka.classifiers.functions.pace.FloatingPointFormat
 
decompose() - Method in class weka.classifiers.BVDecompose
Carry out the bias-variance decomposition
decompose() - Method in class weka.classifiers.RegressionBVDecompose
Carry out the bias-variance decomposition
decrement() - Method in class weka.deduping.metrics.Weight
Decrement and return the new count
decrement(int) - Method in class weka.deduping.metrics.Weight
Decrement by n and return the new count
decrement(double) - Method in class weka.deduping.metrics.Weight
Decrement by n and return the new count
deduperTipText() - Method in class weka.experiment.DeduperSplitEvaluator
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.
defineDataFormat() - Method in class weka.datagenerators.TextSource
 
del(int, Instance) - Method in class weka.classifiers.trees.j48.Distribution
Deletes given instance from given bag.
delRange(int, Instances, int, int) - Method in class weka.classifiers.trees.j48.Distribution
Deletes all instances in given range 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.
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).
deleteClassAttribute() - Method in class weka.core.Instances
Deletes attribute at position classIndex
deleteInstance(Instance) - Method in class weka.core.KDTree
Deletes one instance in the KDTree.
deleteInstance(int) - Method in class weka.core.KDTree
Deletes one instance in the KDTree.
deleteItemSets(FastVector, int, int) - Static method in class weka.associations.ItemSet
Deletes all item sets that don't have minimum support.
deleteOneIndex(int) - Method in class weka.core.DynamicArrayOfPosInt
Deletes an entry.
deleteStringAttributes() - Method in class weka.core.Instances
Deletes all string attributes in the dataset.
deleteWithMissing(int) - Method in class weka.core.Instances
Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissing(Attribute) - Method in class weka.core.Instances
Removes all instances with missing values for a particular attribute from the dataset.
deleteWithMissingClass() - Method in class weka.core.Instances
Removes all instances with a missing class value from the dataset.
deltaTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
densityForInstance(Instance) - Method in class weka.clusterers.DistributionClusterer
Computes the density for a given instance.
densityForInstance(Instance) - Method in class weka.clusterers.DistributionMetaClusterer
Computes the density for a given instance.
densityForInstance(Instance) - Method in class weka.clusterers.EM
Computes the density for a given instance.
densityForInstance(Instance) - Method in class weka.clusterers.PCSoftKMeans
Computes the density for a given instance.
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.
designatedClassTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
desiredSizeTipText() - Method in class weka.classifiers.meta.Decorate
Returns the tip text for this property
determineBounds() - Method in class weka.gui.visualize.Plot2D
Determine the min and max values for axis and colouring attributes
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.AveragingResultProducer
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.CSVResultListener
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.DatabaseResultListener
Determines if there are any constraints (imposed by the destination) on any additional measures produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
Determines if there are any constraints (imposed by the destination) on the result columns to be produced by resultProducers.
determineColumnConstraints(ResultProducer) - Method in interface weka.experiment.ResultListener
Determines if there are any constraints (imposed by the destination) on additional result columns to be produced by resultProducers.
determineTemplate(int) - Method in class weka.experiment.AveragingResultProducer
Simulates a run to collect the keys the sub-resultproducer could generate.
difference(int, double, double) - Method in class weka.clusterers.FarthestFirst
Computes the difference between two given attribute values.
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
digits - Variable in class weka.classifiers.functions.pace.ExponentialFormat
 
directionTipText() - Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
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.
disconnectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
This will disconnect the input with the specific connection number From this node (only on this end however).
disconnectInput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralNode
This will disconnect the input with the specific connection number From this node (only on this end however).
disconnectOutput(NeuralConnection, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
This will disconnect the output with the specific connection number From this node (only on this end however).
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractEvaluator
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event named
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTestSetProducer
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in interface weka.gui.beans.BeanCommon
Notify this object that it has been deregistered as a listener with a source for named event.
disconnectionNotification(String, Object) - Method in class weka.gui.beans.ClassAssigner
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Classifier
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.Filter
Notify this object that it has been deregistered as a listener with a source with respect to the supplied event name
disconnectionNotification(String, Object) - Method in class weka.gui.beans.StripChart
Notify this object that it has been deregistered as a listener with a source for named event.
distance(Instance, Instance) - Method in class weka.classifiers.bayes.SemiSupEM
Calculates the distance between two instances
distance(Instance, Instance) - Method in class weka.clusterers.FarthestFirst
Calculates the distance between two instances
distance(Instance, Cluster) - Method in class weka.clusterers.HAC
internal method that returns the distance between an instance and a cluster
distance(Instance, Instance) - Method in class weka.core.DistanceFunction
Calculates the distance (or similarity) between two instances.
distance(Instance, Instance) - Method in class weka.core.EuclideanDistance
Calculates the distance (or similarity) between two instances.
distance(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetric
Returns a distance value between two instances.
distance(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetricMatlab
Returns a distance value between two instances.
distance(Instance, Instance) - Method in class weka.core.metrics.KL
Returns a distance value between two instances.
distance(Instance, Instance) - Method in class weka.core.metrics.Metric
Returns a distance value between two instances.
distance(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns distance between two instances using the current conversion type (CONVERSION_LAPLACIAN, CONVERSION_EXPONENTIAL, CONVERSION_UNIT, ...)
distance(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between two instances.
distance(Instance, Instance) - Method in class weka.core.metrics.WeightedMahalanobis
Returns a distance value between two instances.
distance(Instance, Instance) - Method in class weka.core.metrics.XingMetric
Returns a distance value between two instances.
distance(String, String) - Method in class weka.deduping.metrics.AffineMetric
Obtain the distance between two strings
distance(String, String) - Method in class weka.deduping.metrics.AffineProbMetric
Get the distance between two strings
distance(Instance, Instance) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Returns distance between two records
distance(Instance, Instance) - Method in class weka.deduping.metrics.InstanceMetric
Returns a distance value between two instances.
distance(String, String) - Method in class weka.deduping.metrics.JaccardMetric
Returns distance between two strings using the current conversion type (CONVERSION_LAPLACIAN, CONVERSION_EXPONENTIAL, CONVERSION_UNIT, ...)
distance(String, String) - Method in class weka.deduping.metrics.KernelVSMetric
Returns distance between two strings using the current conversion type (CONVERSION_LAPLACIAN, CONVERSION_EXPONENTIAL, CONVERSION_UNIT, ...)
distance(String, String) - Method in class weka.deduping.metrics.StringMetric
Compute a measure of distance between two strings
distance(Instance, Instance) - Method in class weka.deduping.metrics.SumInstanceMetric
Returns distance between two instances without using the weights.
distance(String, String) - Method in class weka.deduping.metrics.VectorSpaceMetric
Returns distance between two strings using the current conversion type (CONVERSION_LAPLACIAN, CONVERSION_EXPONENTIAL, CONVERSION_UNIT, ...)
distance1(String, String) - Method in class weka.deduping.metrics.JaccardMetric
 
distanceInternal(Instance, Instance) - Method in class weka.core.metrics.KL
Returns a distance value between two instances.
distanceInternal(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between two instances.
distanceJS(Instance, Instance) - Method in class weka.core.metrics.KL
Returns Jensen-Shannon distance value between two instances.
distanceJSNonSparse(Instance, Instance) - Method in class weka.core.metrics.KL
Returns Jensen-Shannon distance between non-sparse instances without using the weights
distanceJSSparse(SparseInstance, SparseInstance) - Method in class weka.core.metrics.KL
Returns Jensen-Shannon distance between two sparse instances.
distanceJSSparseNonSparse(SparseInstance, Instance) - Method in class weka.core.metrics.KL
Returns Jensen-Shannon distance between a non-sparse instance and a sparse instance
distanceNonSparse(Instance, Instance) - Method in class weka.core.metrics.KL
Returns a distance value between non-sparse instances without using the weights
distanceNonSparse(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between non-sparse instances without using the weights
distanceNonSparseNonWeighted(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between non-sparse instances (or a non-sparse instance and a sparse instance) without using the weights
distanceNonWeighted(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetric
Returns a distance value between two instances.
distanceNonWeighted(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetricMatlab
Returns a distance value between two instances.
distanceNonWeighted(Instance, Instance) - Method in class weka.core.metrics.KL
Returns distance between two instances without using the weights.
distanceNonWeighted(Instance, Instance) - Method in class weka.core.metrics.Metric
Returns distance between two instances without using the weights.
distanceNonWeighted(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns distance between two instances using the current conversion without using the weights type (CONVERSION_LAPLACIAN, CONVERSION_EXPONENTIAL, CONVERSION_UNIT, ...)
distanceNonWeighted(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between two instances.
distanceNonWeighted(Instance, Instance) - Method in class weka.core.metrics.WeightedMahalanobis
Returns a non-weighted distance value between two instances.
distanceNonWeighted(Instance, Instance) - Method in class weka.core.metrics.XingMetric
Returns a distance value between two instances.
distancePenaltyInCombinedModel(int, int) - Method in class weka.clusterers.MPCKMeans
Delegate the distance calculation to the method appropriate for the current metric
distancePenaltyInPottsModel(int, int) - Method in class weka.clusterers.MPCKMeans
Delegate the distance calculation to the method appropriate for the current metric
distanceSparse(SparseInstance, SparseInstance) - Method in class weka.core.metrics.KL
Returns a distance value between two sparse instances.
distanceSparse(SparseInstance, SparseInstance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between two sparse instances.
distanceSparseNonSparse(SparseInstance, Instance) - Method in class weka.core.metrics.KL
Returns a distance value between a non-sparse instance and a sparse instance
distanceSparseNonSparse(SparseInstance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between a non-sparse instance and a sparse instance
distanceSparseNonSparseNonWeighted(SparseInstance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between a non-sparse instance and a sparse instance
distanceSparseNonWeighted(SparseInstance, SparseInstance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a distance value between two sparse instances without using the weights.
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
distribution() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets the predicted probabilities
distribution(double[][], double[][][], int, int[], double[], double[][], Instances) - Method in class weka.classifiers.trees.REPTree.Tree
Computes class distribution for an attribute.
distribution(double[][], double[][][], int, int[], double[], Instances) - Method in class weka.classifiers.trees.RandomTree
Computes class distribution for an attribute.
distribution() - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns the distribution of class values induced by the model.
distributionClassifier() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme is a distribution classifier.
distributionClassifierTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
 
distributionClassifierTipText() - Method in class weka.classifiers.meta.OrdinalClassClassifier
 
distributionClassifierTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
distributionForInstance(Instance) - Method in class weka.classifiers.DistributionClassifier
Predicts the class memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.BayesNet
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayes
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesSimple
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.SemiSupEM
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.Logistic
Classifies an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.SMO
Estimates class probabilities for given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.VotedPerceptron
Outputs the distribution for the given output.
distributionForInstance(Instance) - Method in class weka.classifiers.functions.neural.NeuralNetwork
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.lazy.IBk
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.LBR
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.lazy.kstar.KStar
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.ActiveDecorate
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.AdaBoostM1
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Classifies a given instance after attribute selection
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Bagging
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.ClassificationViaRegression
Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.DEC
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Decorate
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.DistributionMetaClassifier
Returns the class probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.Fable
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.LogitBoost
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiBoostAB
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.MultiClassClassifier
Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns the distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.QBag
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.QBoost
Calculates the class membership probabilities for the given test instance.
distributionForInstance(double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
distributionForInstance(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
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.SemiSupDecorate
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.meta.TestEnsembleClassifier
Calculates the class membership probabilities for the given test 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.misc.HyperPipes
Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.misc.Prototype
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.misc.PrototypeMetric
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.misc.VFI
Classifies the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.ConjunctiveRule
Computes class distribution for the given instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.DecisionTable
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.JRip
Classify the test instance with the rule learner and provide the class distributions
distributionForInstance(Instance) - Method in class weka.classifiers.rules.ZeroR
Calculates the class membership probabilities for the given test 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.rules.part.MakeDecList
Returns the class distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.rules.part.PART
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.sparse.IBkMetric
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.sparse.SVMlight
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.DecisionStump
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.Id3
Computes class distribution for instance using decision tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.REPTree.Tree
Computes class distribution of an instance using the tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.REPTree
Computes class distribution of an instance using the tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.RandomForest
Returns the class probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.RandomTree
Computes class distribution of an instance using the decision tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.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.adtree.ADTree
Returns the class probability distribution for an instance.
distributionForInstance(Instance, boolean) - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns class probabilities for a weighted instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.j48.J48
Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.clusterers.DistributionClusterer
Predicts the cluster memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.clusterers.DistributionMetaClusterer
Returns the cluster probability distribution for an instance.
distributionForInstance(Instance) - Method in class weka.clusterers.EM
Predicts the cluster memberships for a given instance.
distributionForInstance(Instance) - Method in class weka.clusterers.PCSoftKMeans
Checks if instance has to be normalized and returns the distribution of the instance using the current clustering
distributionForInstanceUsingWeights(Instance) - Method in class weka.classifiers.meta.DEC
Calculates the class membership probabilities for the given test instance.
distributionForInstanceUsingWeights(Instance) - Method in class weka.classifiers.meta.SemiSupDecorate
Calculates the class membership probabilities for the given test 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
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
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.
doAverageResult(Object[]) - Method in class weka.experiment.AveragingResultProducer
Asks the resultlistener whether an average result is required, and if so, calculates it.
doHistory(KeyEvent) - Method in class weka.gui.SimpleCLI
Changes the currently displayed command line when certain keys are pressed.
doLayout() - Method in class weka.gui.beans.KnowledgeFlow.BeanLayout
 
doRun(int) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
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.CrossValidationResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.DatabaseResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.ExtractionResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
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.NoiseCurveCrossValidationResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.RandomSplitResultProducer
Gets the results for a specified run number.
doRun(int) - Method in interface weka.experiment.ResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets the results for a specified run number.
doRun(int) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets the results for a specified run number.
doRunKeys(int) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
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.CrossValidationResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.DatabaseResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.ExtractionResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
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.NoiseCurveCrossValidationResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.RandomSplitResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in interface weka.experiment.ResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets the keys for a specified run number.
doRunKeys(int) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets the keys for a specified run number.
doTests() - Method in class weka.classifiers.CheckClassifier
Begin the tests, reporting results to System.out
doesntUseTestClassVal(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Checks whether the classifier erroneously uses the class value of test instances (if provided).
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.ADTree
Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.
dotMultiply(AlgVector) - Method in class weka.clusterers.AlgVector
Returns the inner (or dot) product of two vectors
dotMultiply(AlgVector) - Method in class weka.core.AlgVector
Returns the inner (or dot) product of two vectors
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.
drawDataPoint(double, double, double, double, int, int, Graphics) - Static method in class weka.gui.visualize.Plot2D
Draws a data point at a given set of panel coordinates at a given size and connects a line to the previous point.
drawDataPoint(double, double, int, int, Graphics) - Static method in class weka.gui.visualize.Plot2D
Draws a data point at a given set of panel coordinates at a given size.
drawHighlight(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to draw the node highlighted.
drawHighlight(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd
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.
drawNode(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd
This will draw the node id to the graphics context.
drawOutputLines(Graphics, int, int) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to draw the nodes output connections.
dumpAttributeNames(Instances, String) - Static method in class weka.attributeSelection.MatlabPCA
Dump attribute names into a text file
dumpData(double[], double[][], double[], double[][], double[]) - Method in class weka.clusterers.assigners.LPAssigner
Dump data matrix into a file
dumpDataTomLab(double[], double[][], double[], double[][], double[]) - Method in class weka.clusterers.assigners.LPAssigner
Dump data matrix into a file
dumpDistribution() - Method in class weka.classifiers.trees.j48.Distribution
Prints distribution.
dumpInstance(Instance, File) - Method in class weka.classifiers.sparse.SVMlight
Dump a single instance into a file in SVM-light format
dumpInstanceList(ArrayList, String) - Method in class weka.core.metrics.MatlabMetricLearner
Dump a list of instances as a matrix of attribute values
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.
dumpTrainingData(Instances) - Method in class weka.classifiers.sparse.SVMlight
Dump training instances into a file in SVM-light format

E

EAST_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
 
EDITOR_PROPERTIES - Static variable in class weka.gui.GenericObjectEditor
Contains the editor properties
EM - class weka.clusterers.EM.
Simple EM (expectation maximisation) class.
EM() - Constructor for class weka.clusterers.EM
Constructor.
EMDataGenerator - class weka.gui.boundaryvisualizer.EMDataGenerator.
Class that uses EM to build a probabilistic clustering model of supplied input data and then generates new random instances based that model.
EMDataGenerator() - Constructor for class weka.gui.boundaryvisualizer.EMDataGenerator
 
ENGINE_JMATLINK - Static variable in class weka.clusterers.assigners.LPAssigner
Different engines that can be used to solve the LP
ENGINE_MATLAB - Static variable in class weka.clusterers.assigners.LPAssigner
 
ENGINE_OCTAVE - Static variable in class weka.clusterers.assigners.LPAssigner
 
ENGINE_TOMLAB - Static variable in class weka.clusterers.assigners.LPAssigner
 
ENTROPY - Static variable in interface weka.classifiers.bayes.Scoreable
 
EPSILON - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
ERROR_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
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
 
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)
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.
EnsembleClassifier - class weka.classifiers.EnsembleClassifier.
Abstract class for Ensemble Classifiers
EnsembleClassifier() - Constructor for class weka.classifiers.EnsembleClassifier
 
EnsembleClassifierSplitEvaluator - class weka.experiment.EnsembleClassifierSplitEvaluator.
A SplitEvaluator that produces results for an ensemble classification scheme
EnsembleClassifierSplitEvaluator() - Constructor for class weka.experiment.EnsembleClassifierSplitEvaluator
 
EnsembleEvaluation - class weka.classifiers.EnsembleEvaluation.
Class for evaluating machine learning models.
EnsembleEvaluation(Instances) - Constructor for class weka.classifiers.EnsembleEvaluation
Initializes all the counters for the evaluation.
EnsembleEvaluation(Instances, CostMatrix) - Constructor for class weka.classifiers.EnsembleEvaluation
Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
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
 
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
 
ErrorBasedMeritEvaluator - interface weka.attributeSelection.ErrorBasedMeritEvaluator.
Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme
Estimator - interface weka.estimators.Estimator.
Interface for probability estimators.
EuclideanDistance - class weka.core.EuclideanDistance.
Implementing Euclidean distance (or similarity) function.
EuclideanDistance() - Constructor for class weka.core.EuclideanDistance
Constructs an Euclidean Distance object.
EuclideanDistance(Instances) - Constructor for class weka.core.EuclideanDistance
Constructs an Euclidean Distance object.
EuclideanDistance(Instances, boolean) - Constructor for class weka.core.EuclideanDistance
Constructs an Euclidean Distance object.
EuclideanDistance(Instances, double[][]) - Constructor for class weka.core.EuclideanDistance
Constructs an Euclidean Distance object.
EuclideanDistance(Instances, double[][], boolean) - Constructor for class weka.core.EuclideanDistance
Constructs an Euclidean Distance object.
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.
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
 
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.
ExhaustiveSearch - class weka.attributeSelection.ExhaustiveSearch.
Class for performing an exhaustive search.
ExhaustiveSearch() - Constructor for class weka.attributeSelection.ExhaustiveSearch
Constructor
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
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
 
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
ExtractionEvaluation - class weka.extraction.ExtractionEvaluation.
Class for evaluating extractors
ExtractionEvaluation() - Constructor for class weka.extraction.ExtractionEvaluation
A default constructor
ExtractionResultProducer - class weka.experiment.ExtractionResultProducer.
N-fold cross-validation learning curve for information extraction
ExtractionResultProducer() - Constructor for class weka.experiment.ExtractionResultProducer
 
ExtractionSplitEvaluator - class weka.experiment.ExtractionSplitEvaluator.
A SplitEvaluator that produces results for an extraction scheme -W classname
Specify the full class name of the extractor to evaluate.
ExtractionSplitEvaluator() - Constructor for class weka.experiment.ExtractionSplitEvaluator
No args constructor.
Extractor - class weka.extraction.Extractor.
An abstract extractor class.
Extractor() - Constructor for class weka.extraction.Extractor
 
eStep() - Method in class weka.classifiers.bayes.SemiSupEM
 
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.
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.
ensembleDiversity() - Method in class weka.classifiers.EnsembleEvaluation
Gets the mean ensemble diversity.
ensemblePctCorrect() - Method in class weka.classifiers.EnsembleEvaluation
Gets the ensemble mean of percentage of instances correctly classified (that is, for which a correct prediction was made).
ensemblePctIncorrect() - Method in class weka.classifiers.EnsembleEvaluation
Gets the ensemble mean of percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
entropy() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
entropy(double[]) - Static method in class weka.core.ContingencyTables
Computes the entropy of the given array.
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.
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.
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.
enumerateMeasures() - Method in class weka.classifiers.EnsembleClassifier
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.rules.DecisionTable
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.JRip
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.Ridor
Returns an enumeration of the additional measure names
enumerateMeasures() - Method in class weka.classifiers.rules.part.PART
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.adtree.ADTree
Returns an enumeration of the additional measure names.
enumerateMeasures() - Method in class weka.classifiers.trees.j48.J48
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 interface weka.core.AdditionalMeasureProducer
Returns an enumeration of the measure names.
enumerateMeasures() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.AveragingResultProducer
Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns an enumeration of any additional measure names that might be in the classifier
enumerateMeasures() - Method in class weka.experiment.CrossValidationResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.DatabaseResultProducer
Returns an enumeration of any additional measure names that might be in the result producer
enumerateMeasures() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.ExtractionResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
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.NoiseCurveCrossValidationResultProducer
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.RegressionSplitEvaluator
Returns an enumeration of any additional measure names that might be in the classifier
enumerateMeasures() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateMeasures() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns an enumeration of any additional measure names that might be in the SplitEvaluator
enumerateRequests() - Method in class weka.gui.beans.Classifier
Return an enumeration of requests that can be made by the user
enumerateRequests() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Return an enumeration of user activated requests for this bean
enumerateRequests() - Method in class weka.gui.beans.CrossValidationFoldMaker
Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.DataVisualizer
Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.Filter
Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.GraphViewer
Return an enumeration of user requests
enumerateRequests() - Method in class weka.gui.beans.Loader
Get a list of user requests
enumerateRequests() - Method in class weka.gui.beans.StripChart
Describe enumerateRequests method here.
enumerateRequests() - Method in class weka.gui.beans.TextViewer
Get a list of user requests
enumerateRequests() - Method in class weka.gui.beans.TrainTestSplitMaker
Get list of user requests
enumerateRequests() - Method in interface weka.gui.beans.UserRequestAcceptor
Get a list of performable requests
enumerateValues() - Method in class weka.core.Attribute
Returns an enumeration of all the attribute's values if the attribute is nominal or a string, null otherwise.
epsilonParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
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.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.Splitter
Tests whether two splitters are equivalent.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Tests whether two splitters are equivalent.
equalTo(Splitter) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Tests whether two splitters are equivalent.
equalTo(Test) - Method in class weka.datagenerators.Test
Compares the test with the test that is given as parameter.
equals(Object) - Method in class weka.associations.ItemSet
Tests if two item sets are equal.
equals(Object) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
Tests if two instances are equal
equals(Object) - Method in class weka.classifiers.EnsembleEvaluation
Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.classifiers.Evaluation
Tests whether the current evaluation object is equal to another evaluation object
equals(Object) - Method in class weka.classifiers.rules.DecisionTable.hashKey
Tests if two instances are equal
equals(Object) - Method in class weka.clusterers.InstancePair
Equals function
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.datagenerators.TextSource.Int
 
errms(StreamTokenizer, String) - Static method in class weka.core.converters.ConverterUtils
Throws error message with line number and last token read.
error() - Method in class weka.classifiers.evaluation.NumericPrediction
Calculates the prediction error.
errorBars - Variable in class weka.experiment.Grapher
Set if desire error bars of particular type
errorBars - Variable in class weka.experiment.NoiseGrapher
Set if desire error bars of particular type
errorRate() - Method in class weka.classifiers.EnsembleEvaluation
Returns the estimated error rate or the root mean squared error (if the class is numeric).
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.
errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit
This function calculates what the error value should be.
errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this to get the error value of this unit.
errorValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod
This function calculates what the error value should be.
errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd
Call this to get the error value of this unit, which in this case is the difference between the predicted class, and the actual class.
errorValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNode
Call this to get the error value of this unit.
errorValue(NeuralNode) - Method in class weka.classifiers.functions.neural.SigmoidUnit
This function calculates what the error value should be.
estimateCPTs() - Method in class weka.classifiers.bayes.BayesNet
estimateCPTs estimates the conditional probability tables for the Bayes Net using the network structure.
evaluateAttribute(int) - Method in class weka.attributeSelection.AttributeEvaluator
evaluates an individual attribute
evaluateAttribute(int) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
evaluates an individual attribute by measuring its chi-squared value.
evaluateAttribute(int) - Method in class weka.attributeSelection.GainRatioAttributeEval
evaluates an individual attribute by measuring the gain ratio of the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.InfoGainAttributeEval
evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.MatlabICA
Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.MatlabNMF
Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.MatlabPCA
Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.OneRAttributeEval
evaluates an individual attribute by measuring the amount of information gained about the class given the attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.PrincipalComponents
Evaluates the merit of a transformed attribute.
evaluateAttribute(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
Evaluates an individual attribute using ReliefF's instance based approach.
evaluateAttribute(int) - Method in class weka.attributeSelection.SVMAttributeEval
Evaluates an attribute by returning the rank of the square of its coefficient in a linear support vector machine.
evaluateAttribute(int) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
evaluates an individual attribute by measuring the symmetrical uncertainty between it and the class.
evaluateClusterer(Instances) - Method in class weka.clusterers.ClusterEvaluation
Evaluate the clusterer on a set of instances.
evaluateClusterer(Clusterer, String[]) - Static method in class weka.clusterers.ClusterEvaluation
Evaluates a clusterer with the options given in an array of strings.
evaluateGradient(double[]) - Method in class weka.core.Optimization
 
evaluateHessian(double[], int) - Method in class weka.core.Optimization
 
evaluateModel(String, String[]) - Static method in class weka.classifiers.EnsembleEvaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModel(Classifier, String[]) - Static method in class weka.classifiers.EnsembleEvaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModel(Classifier, Instances) - Method in class weka.classifiers.EnsembleEvaluation
Evaluates the classifier on a given set of instances.
evaluateModel(String, String[]) - Static method in class weka.classifiers.Evaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModel(Classifier, String[]) - Static method in class weka.classifiers.Evaluation
Evaluates a classifier with the options given in an array of strings.
evaluateModel(Classifier, Instances) - Method in class weka.classifiers.Evaluation
Evaluates the classifier on a given set of instances.
evaluateModel(Clusterer, Instances, Instances) - Method in class weka.clusterers.SemiSupClustererEvaluation
Evaluates the semi-sup clusterer on a given set of test instances
evaluateModel(Deduper, Instances) - Method in class weka.deduping.DedupingEvaluation
Evaluates the deduper on a given set of test instances
evaluateModel(Extractor, Instances) - Method in class weka.extraction.ExtractionEvaluation
Evaluates an extractor on a given set of test instances
evaluateModelOnce(Classifier, Instance) - Method in class weka.classifiers.EnsembleEvaluation
Evaluates the classifier on a single instance.
evaluateModelOnce(double[], Instance) - Method in class weka.classifiers.EnsembleEvaluation
Evaluates the supplied distribution on a single instance.
evaluateModelOnce(double, Instance) - Method in class weka.classifiers.EnsembleEvaluation
Evaluates the supplied prediction on a single instance.
evaluateModelOnce(Classifier, Instance) - Method in class weka.classifiers.Evaluation
Evaluates the classifier on a single instance.
evaluateModelOnce(double[], Instance) - Method in class weka.classifiers.Evaluation
Evaluates the supplied distribution on a single instance.
evaluateModelOnce(double, Instance) - Method in class weka.classifiers.Evaluation
Evaluates the supplied prediction on a single instance.
evaluateModelOnce(Clusterer, Instance, int) - Method in class weka.clusterers.SemiSupClustererEvaluation
Evaluates the semi-sup clusterer on a given test instance
evaluateSubset(BitSet) - Method in class weka.attributeSelection.CfsSubsetEval
evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.ClassifierSubsetEval
Evaluates a subset of attributes
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.ClassifierSubsetEval
Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.ClassifierSubsetEval
Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet) - Method in class weka.attributeSelection.ConsistencySubsetEval
Evaluates a subset of attributes
evaluateSubset(BitSet, Instances) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator
Evaluates a subset of attributes with respect to a set of instances.
evaluateSubset(BitSet, Instance, boolean) - Method in class weka.attributeSelection.HoldOutSubsetEvaluator
Evaluates a subset of attributes with respect to a single instance.
evaluateSubset(BitSet) - Method in class weka.attributeSelection.SubsetEvaluator
evaluates a subset of attributes
evaluateSubset(BitSet) - Method in class weka.attributeSelection.WrapperSubsetEval
Evaluates a subset of attributes
evaluationModeTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
evaluatorTipText() - Method in class weka.attributeSelection.MatlabNMF
Returns the tip text for this property
evaluatorTipText() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns the tip text for this property
eventGeneratable(String) - Method in class weka.gui.beans.ClassAssigner
Returns true, if at the current time, the named event could be generated.
eventGeneratable(EventSetDescriptor) - Method in class weka.gui.beans.Classifier
Returns true, if at the current time, the event described by the supplied event descriptor could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Classifier
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.CrossValidationFoldMaker
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in interface weka.gui.beans.EventConstraints
Returns true if, at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Filter
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.Loader
Returns true if the named event can be generated at this time
eventGeneratable(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.TrainTestSplitMaker
Returns true, if at the current time, the named event could be generated.
eventGeneratable(String) - Method in class weka.gui.beans.TrainingSetMaker
Returns true, if at the current time, the named event could be generated.
execute(String) - Method in class weka.experiment.DatabaseUtils
Executes a SQL query.
execute() - Method in class weka.experiment.RemoteExperimentSubTask
Run the experiment
execute() - Method in interface weka.experiment.Task
Execute this task.
executeTask(Task) - Method in interface weka.experiment.Compute
Execute a task
executeTask(Task) - Method in class weka.experiment.RemoteEngine
Takes a task object and queues it for execution
exp - Variable in class weka.classifiers.functions.pace.ExponentialFormat
 
expectationStep(String, String, int, boolean) - Method in class weka.deduping.metrics.AffineProbMetric
Expectation part of the EM algorithm accumulates expectations of editop probabilities over example pairs Expectation is calculated based on two examples which are either duplicates (pos=true) or non-duplicates (pos=false).
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
experimentIndexExists() - Method in class weka.experiment.DatabaseUtils
Returns true if the experiment index exists.
expressionTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the tip text for this property
extractorTipText() - Method in class weka.experiment.ExtractionSplitEvaluator
Returns the tip text for this property

F

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
 
FILE_EXTENSION - Static variable in class weka.classifiers.CostMatrix
The deafult file extension for cost matrix files
FILE_EXTENSION - Static variable in class weka.core.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
FILTER_NONE - Static variable in class weka.classifiers.functions.SMO
 
FILTER_NORMALIZE - Static variable in class weka.classifiers.functions.SMO
The filter to apply to the training data
FILTER_STANDARDIZE - Static variable in class weka.classifiers.functions.SMO
 
FINISHED - Static variable in class weka.experiment.TaskStatusInfo
 
FLOOR - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
FLOOR1 - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
FMEASURE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
 
FOLD_CREATION_MODE_RANDOM - Static variable in class weka.experiment.DedupingPRCurveCVResultProducer
 
FOLD_CREATION_MODE_RANDOM - Static variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
FOLD_CREATION_MODE_STRATIFIED - Static variable in class weka.experiment.DedupingPRCurveCVResultProducer
SVM-light can work in classification, regression and preference ranking modes
FOLD_CREATION_MODE_STRATIFIED - Static variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
SVM-light can work in classification, regression and preference ranking modes
FOLD_FIELD_NAME - Static variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.ActiveLearningCurveCVResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.ExtractionResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.LearningCurveCrossValidationResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.SemiSupCrossValidationResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
FOLD_FIELD_NAME - Static variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
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
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.
FRACTION_FIELD_NAME - Static variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
FRACTION_FIELD_NAME - Static variable in class weka.experiment.ActiveLearningCurveCVResultProducer
 
FRACTION_FIELD_NAME - Static variable in class weka.experiment.LearningCurveCrossValidationResultProducer
 
FRACTION_FIELD_NAME - Static variable in class weka.experiment.SemiSupCrossValidationResultProducer
 
FRACTION_FIELD_NAME - Static variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
FRACTION_FIELD_NAME - Static variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
FRACTION_FIELD_NAME - Static variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
FRACTION_FIELD_NAME - Static variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
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
FUNCTION_GAUSS - Static variable in class weka.attributeSelection.MatlabICA
 
FUNCTION_POW3 - Static variable in class weka.attributeSelection.MatlabICA
 
FUNCTION_SKEW - Static variable in class weka.attributeSelection.MatlabICA
 
FUNCTION_TANH - Static variable in class weka.attributeSelection.MatlabICA
 
Fable - class weka.classifiers.meta.Fable.
FABLE is a version of DECORATE that allows for active feature acquisition.
Fable() - Constructor for class weka.classifiers.meta.Fable
 
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
 
FastVector - class weka.core.FastVector.
Implements a fast vector class without synchronized methods.
FastVector() - Constructor for class weka.core.FastVector
Constructs an empty vector with initial capacity zero.
FastVector(int) - Constructor for class weka.core.FastVector
Constructs a vector with the given capacity.
FastVector(int, int, double) - Constructor for class weka.core.FastVector
Constructs a vector with the given capacity, capacity increment and capacity mulitplier.
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.
FileEditor - class weka.gui.FileEditor.
A PropertyEditor for File objects that lets the user select a file.
FileEditor() - Constructor for class weka.gui.FileEditor
 
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() - Constructor for class weka.filters.Filter
 
Filter - class weka.gui.beans.Filter.
A wrapper bean for Weka filters
Filter() - Constructor for class weka.gui.beans.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.
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
 
FlexibleDecimalFormat - class weka.classifiers.functions.pace.FlexibleDecimalFormat.
 
FlexibleDecimalFormat() - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
FlexibleDecimalFormat(int) - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
FlexibleDecimalFormat(int, boolean) - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
FlexibleDecimalFormat(int, boolean, boolean, boolean) - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
FlexibleDecimalFormat(double) - Constructor for class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
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
 
ForwardSelection - class weka.attributeSelection.ForwardSelection.
Class for performing a forward selection hill climbing search.
ForwardSelection() - Constructor for class weka.attributeSelection.ForwardSelection
 
f(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of f(x) given the mixture.
f(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of f(x) given the mixture, where x is a vector.
f(double) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of f(x) given the mixture.
f(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of f(x) given the mixture, where x is a vector.
fMeasure(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the F-Measure with respect to a particular class.
fMeasure(int) - Method in class weka.classifiers.Evaluation
Calculate the F-Measure with respect to a particular class.
falseNegativeRate(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the false negative rate with respect to a particular class.
falseNegativeRate(int) - Method in class weka.classifiers.Evaluation
Calculate the false negative rate with respect to a particular class.
falsePositiveRate(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the false positive 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.
farthestAway(double[], boolean[]) - Method in class weka.clusterers.FarthestFirst
 
farthestInstance(Instances, Instances) - Method in class weka.classifiers.bayes.SemiSupEM
Return the instance in candidateInsts that is farthest from any instance in insts
featureMissTestTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
featureMissTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
featureNoiseTestTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
featureNoiseTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
filterFile(Filter, String[]) - Static method in class weka.filters.Filter
Method for testing filters.
filterInstanceDescriptions(Instances) - Method in class weka.clusterers.HAC
If some of the attributes start with "__", form a separate Instances set with descriptions and filter them out of the argument dataset.
filterTypeTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
finalize() - Method in class weka.classifiers.sparse.SVMlight
Take care of closing the SVM-light process before the object is destroyed
findArgmin(double[], double[][]) - Method in class weka.core.Optimization
Main algorithm.
findAssignments() - Method in class weka.clusterers.PCSoftKMeans
E-step of the KMeans clustering algorithm -- find new cluster assignments and new objective function
findBestAssignments() - Method in class weka.clusterers.MPCKMeans
E-step of the KMeans clustering algorithm -- find best cluster assignments Returns the number of points moved in this step
findBestAssignments() - Method in class weka.clusterers.PCKMeans
E-step of the KMeans clustering algorithm -- find best cluster assignments
findBestAssignments() - Method in class weka.clusterers.SeededKMeans
E-step of the KMeans clustering algorithm -- find best cluster assignments
findBestLeaf(double[], RuleNode[]) - Method in class weka.classifiers.trees.m5.RuleNode
Find the leaf with greatest coverage
findDuplicates(Instances, int) - Method in class weka.deduping.BasicDeduper
Identify duplicates within the testing data
findDuplicates(Instances, int) - Method in class weka.deduping.Deduper
Identify duplicates within the testing data
findInstance(Point) - Static method in class weka.gui.beans.BeanInstance
Looks for a bean (if any) whose bounds contain the supplied point
findKNearestNeighbour(Instance, int, int[], double[]) - Method in class weka.core.KDTree
Find k nearest neighbours to target.
findKeyIndex() - Method in class weka.experiment.AveragingResultProducer
Scans through the key field names of the result producer to find the index of the key field to average over.
findNeighbors(Instance) - Method in class weka.classifiers.sparse.IBkMetric
Build the list of nearest k neighbors to the given test instance.
findNumBins(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Optimizes the number of bins using leave-one-out cross-validation.
findNumBins(int) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Finds the number of bins to use and creates the cut points.
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
findNumBinsTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Returns the tip text for this property
findParamsByCrossValidation(int, Instances) - Method in class weka.classifiers.meta.CVParameterSelection
Finds the best parameter combination.
findThreshold(FastVector) - Method in class weka.classifiers.meta.ThresholdSelector
Finds the best threshold, this implementation searches for the highest FMeasure.
finished() - Method in class weka.experiment.OutputZipper
Closes the zip file.
first - Variable in class weka.clusterers.InstancePair
first instance index
first - Variable in class weka.core.Pair
 
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.
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.
fitToScreen() - Method in class weka.gui.treevisualizer.TreeVisualizer
Fits the tree to the current screen size.
fittingIntervalLength - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
fittingIntervalLength - Variable in class weka.classifiers.functions.pace.NormalMixture
 
fittingIntervalThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Contructs the set of fitting intervals for mixture estimation.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Contructs the set of fitting intervals for mixture estimation.
fittingIntervals(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Contructs the set of fitting intervals for mixture estimation.
flushInput() - Method in class weka.filters.Filter
This will remove all buffered instances from the inputformat dataset.
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
foldsTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
foldsTipText() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
forCnt - Variable in class weka.classifiers.lazy.LBR
 
forName(String, String[]) - Static method in class weka.associations.Associator
Creates a new instance of a associator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.attributeSelection.ASEvaluation
Creates a new instance of an attribute/subset evaluator given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.attributeSelection.ASSearch
Creates a new instance of a search class given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.classifiers.Classifier
Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.clusterers.Clusterer
Creates a new instance of a clusterer given it's class name and (optional) arguments to pass to it's setOptions method.
forName(Class, String, String[]) - Static method in class weka.core.Utils
Creates a new instance of an object given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.core.metrics.Metric
Creates a new instance of a metric given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.core.metrics.MetricLearner
Creates a new instance of a metric learner given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.core.metrics.PairwiseSelector
Creates a new instance of a metric learner given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.deduping.Deduper
 
forName(String, String[]) - Static method in class weka.deduping.metrics.InstanceMetric
Creates a new instance of a metric given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.deduping.metrics.StringMetric
Creates a new instance of a metric given it's class name and (optional) arguments to pass to it's setOptions method.
forName(String, String[]) - Static method in class weka.extraction.Extractor
A helper function that may be needed by command-line Weka
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.ExponentialFormat
 
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
format(double, StringBuffer, FieldPosition) - Method in class weka.classifiers.functions.pace.FloatingPointFormat
 
format() - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(int, int, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
format(int, int, int, int, int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Decimal format for converting a matrix into a string
formatDate(double) - Method in class weka.core.Attribute
 
formatString(String) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
forward(PaceMatrix, IntVector, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Forward ordering of columns in terms of response explanation.
forward(String, String) - Method in class weka.deduping.metrics.AffineProbMetric
Calculate the forward matrices
fractionCommonTokens(String, String) - Static method in class weka.deduping.PairwiseSelector
return the number of commmon tokens that two strings have
fullValue() - Method in class weka.gui.HierarchyPropertyParser
The full value of the current node, i.e.

G

GAUSS - Static variable in class weka.classifiers.lazy.LWR
 
GDMetricLearner - class weka.core.metrics.GDMetricLearner.
GDMetricLearner - sets the weights of a metric using gradient descent
GDMetricLearner() - Constructor for class weka.core.metrics.GDMetricLearner
Create a new gradient descent metric learner
GRID - Static variable in class weka.datagenerators.BIRCHCluster
 
GROUP_AVERAGE - Static variable in class weka.clusterers.HAC
 
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.neural.NeuralNetwork
 
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
 
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() - Constructor for class weka.gui.GenericObjectEditor
Default constructor.
GenericObjectEditor(boolean) - Constructor for class weka.gui.GenericObjectEditor
Constructor that allows specifying whether it is possible to change the class within the editor dialog.
GenericObjectEditor.GOEPanel - class 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.
GeneticSearch - class weka.attributeSelection.GeneticSearch.
Class for performing a genetic based search.
GeneticSearch() - Constructor for class weka.attributeSelection.GeneticSearch
Constructor.
GeneticSearch.GABitSet - class weka.attributeSelection.GeneticSearch.GABitSet.
 
GeneticSearch.GABitSet() - Constructor for class weka.attributeSelection.GeneticSearch.GABitSet
Constructor
GetAllSubPackages - class GetAllSubPackages.
 
GetAllSubPackages() - Constructor for class GetAllSubPackages
 
GetCardinalityOfParents() - Method in class weka.classifiers.bayes.ParentSet
returns cardinality of parents
GetNrOfParents() - Method in class weka.classifiers.bayes.ParentSet
returns number of parents
GetParent(int) - Method in class weka.classifiers.bayes.ParentSet
returns index parent of parent specified by index
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.
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
 
Grapher - class weka.experiment.Grapher.
Class for producing performance graphs for any metric from learning curve results.
Grapher(String, short) - Constructor for class weka.experiment.Grapher
Create an initial Grapher and load in data, names of datasets, and set of points on learning curve.
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
 
gain(double[][], double) - Method in class weka.classifiers.trees.REPTree.Tree
Computes value of splitting criterion after split.
gain(double[][], double) - Method in class weka.classifiers.trees.RandomTree
Computes value of splitting criterion after split.
gainRatio() - Method in class weka.classifiers.trees.j48.BinC45Split
Returns (C4.5-type) gain ratio for the generated split.
gainRatio() - Method in class weka.classifiers.trees.j48.C45Split
Returns (C4.5-type) gain ratio for the generated split.
gainRatio(double[][]) - Static method in class weka.core.ContingencyTables
Computes gain ratio for contingency table (split on rows).
generateArtificialData(int, Instances) - Method in class weka.classifiers.meta.ActiveDecorate
Generate artificial training examples.
generateArtificialData(int, Instances) - Method in class weka.classifiers.meta.Crate
Generate artificial training examples.
generateArtificialData(int, Instances) - Method in class weka.classifiers.meta.Decorate
Generate artificial training examples.
generateArtificialData(int, Instances) - Method in class weka.classifiers.meta.Fable
Generate artificial training examples.
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.
generateExample() - Method in class weka.datagenerators.TextSource
 
generateExamples() - Method in class weka.datagenerators.BIRCHCluster
Generate all examples of the dataset.
generateExamples(Random, Instances) - Method in class weka.datagenerators.BIRCHCluster
Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.RDG1
Generate all examples of the dataset.
generateExamples(int, Random, Instances) - Method in class weka.datagenerators.RDG1
Generate all examples of the dataset.
generateExamples() - Method in class weka.datagenerators.TextSource
 
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.
generateFinished() - Method in class weka.datagenerators.TextSource
 
generateInstance() - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Generate an instance.
generateInstance() - Method in class weka.gui.boundaryvisualizer.EMDataGenerator
Generate a new instance.
generateInstance() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Generate a new instance.
generateInstanceFast() - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Generate an instance.
generateInstanceFast() - Method in class weka.gui.boundaryvisualizer.EMDataGenerator
Generate a new instance.
generateInstanceFast() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Generate a new instance.
generateRandomData(int, int, Instances) - Method in class weka.classifiers.meta.DEC
 
generateRandomData(int, int, Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
 
generateRankingTipText() - Method in class weka.attributeSelection.ForwardSelection
Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
generateRankingTipText() - Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
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
get(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
get the value of a bit in the chromosome
get(int) - Method in class weka.classifiers.functions.pace.DoubleVector
Gets a single element.
get(int) - Method in class weka.classifiers.functions.pace.IntVector
Gets the value of an element.
get(int, int) - Method in class weka.classifiers.functions.pace.Matrix
Get a single element.
get(int) - Method in class weka.core.DynamicArrayOfPosInt
 
getAblationLevel() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
getActive() - Method in class weka.clusterers.MPCKMeans
get the active level of clusterer
getActive() - Method in class weka.clusterers.PCKMeans
get the active level of clusterer
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.
getAlgorithm() - Method in class weka.clusterers.MPCKMeans
Get the KMeans algorithm type.
getAlgorithm() - Method in class weka.clusterers.PCKMeans
Get the KMeans algorithm type.
getAlgorithm() - Method in class weka.clusterers.PCSoftKMeans
Get the KMeans algorithm type.
getAlgorithm() - Method in class weka.clusterers.SeededKMeans
Get the KMeans algorithm type.
getAllExplore() - Method in class weka.clusterers.PCKMeans
Return m_AllExplore
getAllSeeds() - Method in class weka.clusterers.Seeder
Returns the total hashMap, with the instance to cluster assignment mapping for all the seeds
getAlpha() - Method in class weka.classifiers.bayes.BayesNet
Method declaration
getAlpha() - Method in class weka.classifiers.functions.Winnow
Get the value of Alpha.
getAlpha() - Method in class weka.classifiers.meta.Crate
Get the value of Alpha.
getAlpha() - Method in class weka.core.metrics.KL
Get the initial value of the smoothing parameter alpha in DITC smoothing
getAlphaDecayRate() - Method in class weka.core.metrics.KL
Get the initial value of the the decay rate of alpha in DITC smoothing
getAnimatedIcon() - Method in class weka.gui.beans.BeanVisual
Returns the animated icon
getArffFile() - Method in class weka.gui.streams.InstanceLoader
 
getArffFile() - Method in class weka.gui.streams.InstanceSavePanel
 
getArray() - Method in class weka.classifiers.functions.pace.IntVector
Access the internal one-dimensional array.
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.ActiveDecorate
Factor that determines number of artificial examples to generate.
getArtificialSize() - Method in class weka.classifiers.meta.Crate
Factor that determines number of artificial examples to generate.
getArtificialSize() - Method in class weka.classifiers.meta.Decorate
Factor that determines number of artificial examples to generate.
getArtificialSize() - Method in class weka.classifiers.meta.Fable
Factor that determines number of artificial examples to generate.
getAsInstance(Instances) - Method in class weka.clusterers.AlgVector
Gets the elements of the vector as an instance.
getAsInstance(Instances, Random) - Method in class weka.core.AlgVector
Gets the elements of the vector as an instance.
getAsText() - Method in class weka.gui.CostMatrixEditor
Some objects can be represented as text, but a cost matrix cannot.
getAsText() - Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting/setting values as text.
getAsText() - Method in class weka.gui.SelectedTagEditor
Gets the current value as text.
getAssigner() - Method in class weka.clusterers.MPCKMeans
Set/get the assigner
getAttIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the boolean value at the specified index in the Attribute Indexes array
getAttIndexSet() - Method in class weka.filters.unsupervised.attribute.AddNoise
Gets the Index of the Attribute that is to be changed.
getAttList_Irr() - Method in class weka.datagenerators.RDG1
Gets the array that defines which of the attributes are seen to be irrelevant.
getAttrIdxs(Instances) - Method in class weka.core.metrics.Metric
This function takes instances, and returns an array of integers 0..(num_attributes-1)
getAttrIdxs(Instances) - Method in class weka.deduping.metrics.InstanceMetric
This function takes instances, and returns an array of integers 0..(num_attributes-1)
getAttrIdxsWithoutLastClass(Instances) - Method in class weka.core.metrics.Metric
It is often the case that last attribute of the data is the class.
getAttrIdxsWithoutLastClass(Instances) - Method in class weka.deduping.metrics.InstanceMetric
It is often the case that last attribute of the data is the class.
getAttrIndxs() - Method in class weka.core.metrics.Metric
Returns an array of attribute incece which will be used by the metric
getAttrIndxs() - Method in class weka.deduping.metrics.InstanceMetric
Returns an array of attribute incece which will be used by the metric
getAttrInfoForDiffInstance(Instance) - Method in class weka.core.metrics.MetricLearner
Given an instance, return a FastVector of attributes.
getAttribute1() - Method in class weka.gui.visualize.VisualizePanelEvent
 
getAttribute2() - Method in class weka.gui.visualize.VisualizePanelEvent
 
getAttributeEvaluator() - Method in class weka.attributeSelection.RaceSearch
Get the attribute evaluator used to generate the ranking.
getAttributeEvaluator() - Method in class weka.attributeSelection.RankSearch
Get the attribute evaluator used to generate the ranking.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.Add
Get the index where the attribute will be inserted
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.attribute.SwapValues
Get the index of the attribute used.
getAttributeIndex() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Get the attribute to be used for selection (-1 for last)
getAttributeIndices() - Method in class weka.filters.supervised.attribute.Discretize
Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Copy
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Get the current range selection
getAttributeIndices() - Method in class weka.filters.unsupervised.attribute.Remove
Get the current range selection.
getAttributeMax(int) - Method in class weka.classifiers.lazy.IBk
Get an attributes maximum observed value
getAttributeMax(int) - Method in class weka.classifiers.lazy.LWR
Gets an attributes maximum observed value
getAttributeMax(int) - Method in class weka.classifiers.sparse.IBkMetric
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 weka.classifiers.lazy.LWR
Gets an attributes minimum observed value
getAttributeMin(int) - Method in class weka.classifiers.sparse.IBkMetric
Get an attributes minimum observed value
getAttributeName() - Method in class weka.filters.unsupervised.attribute.Add
Get the name of the attribute to be created
getAttributeSelectionMethod() - Method in class weka.classifiers.functions.LinearRegression
Gets the method used to select attributes for use in the linear regression.
getAttributeSelectionMethod() - Method in class weka.classifiers.lazy.LWR
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.
getAttributeTypeString() - Method in class weka.filters.unsupervised.attribute.RemoveType
Gets the attribute type to be deleted by the filter as a string.
getAttsToEliminatePerIteration() - Method in class weka.attributeSelection.SVMAttributeEval
Get the constant rate of attribute elimination per iteration
getAutoBounds() - Method in class weka.classifiers.sparse.SVMlight
Get whether min/max margins are determined automatically
getAutoBuild() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
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.
getBagSizePercent() - Method in class weka.classifiers.meta.QBag
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.
getBaseClassifier(int) - Method in class weka.classifiers.meta.Stacking
Gets the specific classifier from the set of base classifiers.
getBaseClassifierSpec(int) - Method in class weka.classifiers.meta.Stacking
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getBaseClassifiers() - Method in class weka.classifiers.meta.Stacking
Gets the list of possible classifers to choose from.
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
getBeanDescriptor() - Method in class weka.gui.beans.ClassAssignerBeanInfo
 
getBeanDescriptor() - Method in class weka.gui.beans.ClassifierBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo
Return the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.FilterBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.LoaderBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.StripChartBeanInfo
Get the bean descriptor for this bean
getBeanDescriptor() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo
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.
getBias() - Method in class weka.classifiers.BVDecompose
Get the calculated bias squared
getBias() - Method in class weka.classifiers.RegressionBVDecompose
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.
getBiased() - Method in class weka.classifiers.sparse.SVMlight
Get whether the hyperplane is biased (i.e.
getBinPath() - Method in class weka.classifiers.sparse.SVMlight
Get the path for the binaries
getBinValue() - Method in class weka.clusterers.XMeans
Gets value that represents true in a new numeric attribute.
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
get whether numeric attributes are just being binarized.
getBinarizeNumericAttributes() - Method in class weka.attributeSelection.InfoGainAttributeEval
get whether numeric attributes are just being binarized.
getBinaryAttributesNominal() - Method in class weka.filters.supervised.attribute.NominalToBinary
Gets if binary attributes are to be treated as nominal ones.
getBinaryAttributesNominal() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Gets if binary attributes are to be treated as nominal ones.
getBinarySplits() - Method in class weka.classifiers.rules.part.PART
Get the value of binarySplits.
getBinarySplits() - Method in class weka.classifiers.trees.j48.J48
Get the value of binarySplits.
getBins() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets the number of bins numeric attributes will be divided into
getBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Ignored
getBufferedMode() - Method in class weka.classifiers.sparse.SVMlight
See whether SVM-light is operating via in/out bufffers or via temporary files
getBuildLogisticModels() - Method in class weka.classifiers.functions.SMO
Get the value of buildLogisticModels.
getBuildRegressionTree() - Method in class weka.classifiers.trees.m5.M5Base
Get the value of regressionTree.
getC() - Method in class weka.classifiers.functions.SMO
Get the value of C.
getC() - Method in class weka.classifiers.sparse.SVMlight
Get the trade-off between training error and margin (default 0 corresponds to [avg.
getC1() - Method in class weka.classifiers.sparse.SVMlight
Get parameter c in sigmoid/poly kernel
getCIndex() - Method in class weka.gui.visualize.VisualizePanel
Get the index of the attribute selected for coloring
getCVParameter(int) - Method in class weka.classifiers.meta.CVParameterSelection
Gets the scheme paramter with the given index.
getCVPredictions(DistributionClassifier, 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.
getCVisible() - Method in class weka.gui.treevisualizer.Node
Get If this node's childs are visible.
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
getCacheValues(double) - Method in class weka.classifiers.lazy.kstar.KStarCache
Returns the values in the cache mapped by the specified key
getCalculateStdDevs() - Method in class weka.experiment.AveragingResultProducer
Get the value of CalculateStdDevs.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.ForwardSelection
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.RaceSearch
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch
Gets the calculated number of attributes to retain.
getCalculatedNumToSelect() - Method in class weka.attributeSelection.Ranker
Gets the calculated number to select.
getCannotLinkWeight() - Method in class weka.clusterers.MPCKMeans
Return the cannot link constraint weight
getCannotLinkWeight() - Method in class weka.clusterers.PCKMeans
Return the cannot link constraint weight
getCannotLinkWeight() - Method in class weka.clusterers.PCSoftKMeans
Return the cannot link constraint weight
getCaseInsensitive() - Method in class weka.deduping.metrics.Tokenizer
Turn case sensitivity on/off
getCenter() - Method in class weka.gui.treevisualizer.Node
Get the value of center.
getCentroidInstance(Instances, boolean, boolean) - Method in class weka.core.metrics.BarHillelMetric
Given a cluster of instances, return the centroid of that cluster
getCentroidInstance(Instances, boolean, boolean) - Method in class weka.core.metrics.BarHillelMetricMatlab
Given a cluster of instances, return the centroid of that cluster
getCentroidInstance(Instances, boolean, boolean) - Method in class weka.core.metrics.KL
Given a cluster of instances, return the centroid of that cluster
getCentroidInstance(Instances, boolean, boolean) - Method in class weka.core.metrics.LearnableMetric
Given a cluster of instances, return the centroid of that cluster
getCentroidInstance(Instances, boolean, boolean) - Method in class weka.core.metrics.WeightedDotP
Given a cluster of instances, return the centroid of that cluster
getCentroidInstance(Instances, boolean, boolean) - Method in class weka.core.metrics.WeightedEuclidean
Given a cluster of instances, return the centroid of that cluster
getCentroidInstance(Instances, boolean, boolean) - Method in class weka.core.metrics.WeightedMahalanobis
Given a cluster of instances, return the centroid of that cluster
getCentroidInstance(Instances, boolean, boolean) - Method in class weka.core.metrics.XingMetric
Given a cluster of instances, return the centroid of that cluster
getChangeInWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
call this function to get the chnage in weights array.
getCheckErrorRate() - Method in class weka.classifiers.rules.JRip
 
getChild(int) - Method in class weka.gui.treevisualizer.Node
Get the Edge for the child number 'i'.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the child for a branch of the split.
getChildForBranch(int) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the child for a branch of the split.
getChildren() - Method in class weka.classifiers.trees.adtree.PredictionNode
Gets the children of this node.
getChooseClassPopupMenu() - Method in class weka.gui.GenericObjectEditor
Returns a popup menu that allows the user to change the class of object.
getChromosome() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
get the chromosome
getCindex() - Method in class weka.gui.visualize.PlotData2D
Get the currently set colouring index of the data
getClampProb() - Method in class weka.deduping.metrics.AffineProbMetric
Get the clamping probability value
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.
getClassDistribution() - Method in class weka.core.SoftClassifiedFullInstance
Get the class distribution for this instance
getClassDistribution() - Method in interface weka.core.SoftClassifiedInstance
Get the class distribution for this instance
getClassDistribution() - Method in class weka.core.SoftClassifiedSparseInstance
Get the class distribution for this instance
getClassFlag() - Method in class weka.datagenerators.ClusterGenerator
Gets the class flag.
getClassForIRStatistics() - Method in class weka.experiment.ClassifierSplitEvaluator
Get the value of ClassForIRStatistics.
getClassForIRStatistics() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Get the value of ClassForIRStatistics.
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.RegressionBVDecompose
Get the index (starting from 1) of the attribute used as the class.
getClassIndex(int) - Method in class weka.core.metrics.Metric
Get the index of the attribute is the class attribute
getClassIndex(int) - Method in class weka.deduping.metrics.InstanceMetric
Get the index of the attribute is the class attribute
getClassIndex() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the attribute on which misclassifications are based.
getClassName() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Get the class containing the transformation method.
getClassOrder() - Method in class weka.filters.supervised.attribute.ClassOrder
Get the wanted class order
getClassProbability(int) - Method in class weka.core.SoftClassifiedFullInstance
Return the probability the instance is in the given class
getClassProbability(int) - Method in interface weka.core.SoftClassifiedInstance
Return the probability the instance is in the given class
getClassProbability(int) - Method in class weka.core.SoftClassifiedSparseInstance
Return the probability the instance is in the given class
getClassesFromProperties() - Method in class weka.gui.GenericObjectEditor
Called when the class of object being edited changes.
getClassesToClusters() - Method in class weka.clusterers.ClusterEvaluation
Return the array (ordered by cluster number) of minimum error class to cluster mappings
getClassifier() - Method in class weka.attributeSelection.ClassifierSubsetEval
Get the classifier used as the base learner.
getClassifier() - Method in class weka.attributeSelection.WrapperSubsetEval
Get the classifier used as the base learner.
getClassifier() - Method in class weka.classifiers.BVDecompose
Gets the name of the classifier being analysed
getClassifier() - Method in class weka.classifiers.CheckClassifier
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.RegressionBVDecompose
Gets the name of the classifier being analysed
getClassifier() - Method in class weka.classifiers.bayes.SemiSupEM
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.ActiveDecorate
Get the classifier used as the base classifier
getClassifier() - Method in class weka.classifiers.meta.AdaBoostM1
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.Bagging
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.CVParameterSelection
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.ClassificationViaRegression
Get the base classifier (regression scheme) used as the classifier
getClassifier() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the classifier used.
getClassifier() - Method in class weka.classifiers.meta.Crate
Get the classifier used as the base classifier
getClassifier() - Method in class weka.classifiers.meta.DEC
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.Decorate
Get the classifier used as the base classifier
getClassifier() - Method in class weka.classifiers.meta.DistributionMetaClassifier
Gets the classifier being wrapped.
getClassifier() - Method in class weka.classifiers.meta.Fable
Get the classifier used as the base classifier
getClassifier() - Method in class weka.classifiers.meta.FilteredClassifier
Gets the classifier used.
getClassifier() - Method in class weka.classifiers.meta.LogitBoost
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.MetaCost
Gets the distribution classifier used.
getClassifier() - Method in class weka.classifiers.meta.MultiBoostAB
Get the classifier used as the classifier
getClassifier(int) - Method in class weka.classifiers.meta.MultiScheme
Gets a single classifier from the set of available classifiers.
getClassifier() - Method in class weka.classifiers.meta.QBag
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.QBoost
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.RegressionByDiscretization
Get the classifier used as the classifier
getClassifier() - Method in class weka.classifiers.meta.SemiSupDecorate
Get the classifier used as the classifier
getClassifier() - Method in class weka.core.metrics.ClassifierMetricLearner
Get the classifier
getClassifier() - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Get the classifier
getClassifier() - Method in class weka.deduping.metrics.KernelVSMetric
Get the classifier
getClassifier() - Method in class weka.experiment.ClassifierSplitEvaluator
Get the value of Classifier.
getClassifier() - Method in class weka.experiment.RegressionSplitEvaluator
Get the value of Classifier.
getClassifier() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the classifier used by the filter.
getClassifier() - Method in class weka.gui.beans.BatchClassifierEvent
Get the classifier
getClassifier() - Method in class weka.gui.beans.Classifier
Get the classifier currently set for this wrapper
getClassifier() - Method in class weka.gui.beans.IncrementalClassifierEvent
Get the classifier
getClassifierSpec() - Method in class weka.classifiers.meta.AdditiveRegression
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.meta.FilteredClassifier
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.classifiers.meta.MetaCost
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec(int) - Method in class weka.classifiers.meta.MultiScheme
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec(Classifier) - Method in class weka.classifiers.meta.Stacking
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getClassifierSpec() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier.
getClassifiers() - Method in class weka.classifiers.meta.MultiScheme
Gets the list of possible classifers to choose from.
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.
getClusterAssignments() - Method in class weka.clusterers.MPCKMeans
 
getClusterCentroids() - Method in class weka.clusterers.MPCKMeans
Accessor
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
getClusterUsedToGenerateLastInstanceFrom() - Method in class weka.gui.boundaryvisualizer.EMDataGenerator
Return the number of the cluster from which the next instance will be generated from
getClusterer() - Method in class weka.clusterers.DistributionMetaClusterer
Gets the clusterer being wrapped.
getClusterer() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Get the value of Clusterer.
getClusterer() - Method in class weka.extraction.ClusteringExtractor
Get the clusterer
getClusterer() - Method in class weka.filters.unsupervised.attribute.AddCluster
Gets the clusterer used by the filter.
getClustererSpec() - Method in class weka.filters.unsupervised.attribute.AddCluster
Gets the clusterer specification string, which contains the class name of the clusterer and any options to the clusterer.
getClusters() - Method in class weka.clusterers.HAC
Computes the final clusters from the cluster assignments, for external access
getClusters() - Method in class weka.clusterers.MPCKMeans
Computes the clusters from the cluster assignments, for external access
getClusters() - Method in class weka.clusterers.PCKMeans
Computes the clusters from the cluster assignments, for external access
getClusters() - Method in class weka.clusterers.PCSoftKMeans
Computes the clusters from the cluster assignments, for external access
getClusters() - Method in class weka.clusterers.SeededKMeans
Computes the final clusters from the cluster assignments, for external access
getClusters() - Method in interface weka.clusterers.SemiSupClusterer
Returns an ArrayList of clusters
getColor() - Method in class weka.gui.treevisualizer.Node
Get the value of color.
getColumn(int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Return a DoubleVector that stores a column of the matrix
getColumn(int, int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Return a DoubleVector that stores some elements of a column of the matrix
getColumn(int) - Method in class weka.core.Matrix
Gets a column of the matrix and returns it as a double array.
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 class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
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.ActiveLearningCurveCVResultProducer
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.CrossValidationResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.DatabaseResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
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.DedupingPRCurveCVResultProducerSplit
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.ExtractionResultProducer
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.LearningCurveCrossValidationResultProducer
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.NoiseCurveCrossValidationResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.RandomSplitResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in interface weka.experiment.ResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
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.SemiSupIncompleteLabelCurveCVResultProducer
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.SemiSupLearningCurveCVResultProducer
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.SemiSupPairActiveCurveCVResultProducer
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.SemiSupPointActiveCurveCVResultProducer
Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getComplexityParameter() - Method in class weka.attributeSelection.SVMAttributeEval
Get the value of C used with SMO
getConcentration() - Method in class weka.clusterers.SeededKMeans
Return the concentration
getConfidenceFactor() - Method in class weka.classifiers.rules.part.PART
Get the value of CF.
getConfidenceFactor() - Method in class weka.classifiers.trees.j48.J48
Get the value of CF.
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.JRip.RipperRule
 
getConsequent() - Method in class weka.classifiers.rules.Rule
Get the consequent of this rule, i.e.
getConstraintWeight() - Method in class weka.clusterers.assigners.RMNAssigner
 
getConstraintsHash() - Method in class weka.clusterers.MPCKMeans
 
getConversionType() - Method in class weka.core.metrics.BarHillelMetric
return the type of distance to similarity conversion
getConversionType() - Method in class weka.core.metrics.BarHillelMetricMatlab
return the type of distance to similarity conversion
getConversionType() - Method in class weka.core.metrics.KL
return the type of distance to similarity conversion
getConversionType() - Method in class weka.core.metrics.WeightedDotP
return the type of similarity to distance conversion
getConversionType() - Method in class weka.core.metrics.WeightedEuclidean
return the type of distance to similarity conversion
getConversionType() - Method in class weka.core.metrics.WeightedMahalanobis
return the type of distance to similarity conversion
getConversionType() - Method in class weka.core.metrics.XingMetric
return the type of distance to similarity conversion
getConversionType() - Method in class weka.deduping.metrics.AffineProbMetric
return the type of similarity to distance conversion
getConversionType() - Method in class weka.deduping.metrics.JaccardMetric
return the type of similarity to distance conversion
getConversionType() - Method in class weka.deduping.metrics.KernelVSMetric
return the type of similarity to distance conversion
getConversionType() - Method in class weka.deduping.metrics.VectorSpaceMetric
return the type of similarity to distance conversion
getCostFactor() - Method in class weka.classifiers.sparse.SVMlight
Get cost-factor, by which training errors on positive examples outweight errors on negative examples
getCostMatrix() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the misclassification cost matrix.
getCostMatrix() - Method in class weka.classifiers.meta.MetaCost
Gets the misclassification cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the source location method of the cost matrix.
getCostMatrixSource() - Method in class weka.classifiers.meta.MetaCost
Gets the source location method of the cost matrix.
getCount(double) - Method in class weka.classifiers.bayes.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).
getCounts(int[], int[], int[], int, int, boolean) - Method in class weka.classifiers.bayes.ADNode
get counts for specific instantiation of a set of nodes
getCounts(int[], int[], int[], int, int, ADNode, boolean) - Method in class weka.classifiers.bayes.VaryNode
get counts for specific instantiation of a set of nodes
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 weka.classifiers.sparse.IBkMetric
Gets whether hold-one-out cross-validation will be used to select the best k value
getCrossoverProb() - Method in class weka.attributeSelection.GeneticSearch
get the probability of crossover
getCurrAlpha() - Method in class weka.core.metrics.KL
Get the current value of the smoothing parameter alpha in DITC smoothing
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.
getCurrentSize() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get CurrentSize
getCurve(FastVector) - Method in class weka.classifiers.evaluation.CostCurve
Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.CostCurve
Calculates the performance stats for the desired class and return results as a set of Instances.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.MarginCurve
Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances.
getCurve(FastVector) - Method in class weka.classifiers.evaluation.ThresholdCurve
Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector, int) - Method in class weka.classifiers.evaluation.ThresholdCurve
Calculates the performance stats for the desired class and return results as a set of Instances.
getCustomEditor() - Method in class weka.gui.CostMatrixEditor
Gets a GUI component with which the user can edit the cost matrix.
getCustomEditor() - Method in class weka.gui.FileEditor
Gets the custom editor component.
getCustomEditor() - Method in class weka.gui.GenericArrayEditor
Returns the array editing component.
getCustomEditor() - Method in class weka.gui.GenericObjectEditor
Returns the array editing component.
getCustomPanel() - Method in interface weka.gui.CustomPanelSupplier
Gets the custom panel for the object.
getCustomPanel() - Method in class weka.gui.GenericObjectEditor
Gets the custom panel used for editing the object.
getCutOffFactor() - Method in class weka.clusterers.XMeans
Gets the cutoff factor.
getCutPoints(int) - Method in class weka.filters.supervised.attribute.Discretize
Gets the cut points for an attribute
getCutPoints(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Gets the cut points for an attribute
getCutoff() - Method in class weka.clusterers.Cobweb
get the cutoff
getD() - Method in class weka.classifiers.sparse.SVMlight
Get parameter d in polynomial kernel
getData() - Method in class weka.classifiers.rules.RuleStats
Get the data of the stats
getDataCreationMethod() - Method in class weka.classifiers.meta.DEC
Method to use for creating artificial data
getDataCreationMethod() - Method in class weka.classifiers.meta.SemiSupDecorate
Method to use for creating artificial data
getDataFileName() - Method in class weka.classifiers.BVDecompose
Get the name of the data file used for the decomposition
getDataFileName() - Method in class weka.classifiers.RegressionBVDecompose
Get the name of the data file used for the decomposition
getDataPoint() - Method in class weka.gui.beans.ChartEvent
Get the data point
getDataSet() - Method in class weka.core.converters.AbstractLoader
 
getDataSet() - Method in class weka.core.converters.ArffLoader
Return the full data set.
getDataSet() - Method in class weka.core.converters.C45Loader
Return the full data set.
getDataSet() - Method in class weka.core.converters.CSVLoader
Return the full data set.
getDataSet() - Method in interface weka.core.converters.Loader
Return the full data set.
getDataSet() - Method in class weka.core.converters.SerializedInstancesLoader
Return the full data set.
getDataSet() - Method in class weka.gui.beans.DataSetEvent
Return the instances of the data set
getDatabaseURL() - Method in class weka.experiment.DatabaseUtils
Get the value of DatabaseURL.
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.attributeSelection.RaceSearch
Get whether output is to be verbose
getDebug() - Method in class weka.classifiers.BVDecompose
Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.CheckClassifier
Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.RegressionBVDecompose
Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.bayes.SemiSupEM
Get debug mode
getDebug() - Method in class weka.classifiers.functions.LeastMedSq
Returns whether or not debugging output shouild be printed
getDebug() - Method in class weka.classifiers.functions.LinearRegression
Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.functions.pace.PaceRegression
Controls whether debugging output will be printed
getDebug() - Method in class weka.classifiers.lazy.IBk
Get the value of Debug.
getDebug() - Method in class weka.classifiers.lazy.LWR
SGts whether debugging output should be produced
getDebug() - Method in class weka.classifiers.meta.ActiveDecorate
Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.meta.AdaBoostM1
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.CVParameterSelection
Gets whether debugging is turned on
getDebug() - Method in class weka.classifiers.meta.Crate
Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.meta.Decorate
Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.meta.Fable
Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.meta.LogitBoost
Get whether debugging is turned on
getDebug() - Method in class weka.classifiers.meta.MultiBoostAB
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.QBoost
Get 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.RegressionByDiscretization
Gets whether debugging output will be printed
getDebug() - Method in class weka.classifiers.rules.JRip
 
getDebug() - Method in class weka.classifiers.sparse.IBkMetric
Get the value of Debug.
getDebug() - Method in class weka.classifiers.sparse.SVMlight
See whether debugging output is on/off
getDebug() - Method in class weka.classifiers.trees.RandomTree
Get the value of Debug.
getDebug() - Method in class weka.clusterers.EM
Get debug mode
getDebug() - Method in class weka.datagenerators.ClusterGenerator
Gets the debug flag.
getDebug() - Method in class weka.datagenerators.Generator
Gets the debug flag.
getDebug() - Method in class weka.deduping.BasicDeduper
See whether debugging output is on/off
getDebug() - Method in class weka.deduping.PairwiseSelector
See whether debugging output is on/off
getDebug() - Method in class weka.filters.unsupervised.attribute.AddExpression
Gets whether debug is set
getDebug() - Method in class weka.gui.streams.InstanceCounter
 
getDebug() - Method in class weka.gui.streams.InstanceJoiner
 
getDebug() - Method in class weka.gui.streams.InstanceLoader
 
getDebug() - Method in class weka.gui.streams.InstanceSavePanel
 
getDebug() - Method in class weka.gui.streams.InstanceTable
 
getDebug() - Method in class weka.gui.streams.InstanceViewer
 
getDebugLevel() - Method in class weka.clusterers.XMeans
Gets the debug level.
getDebugLevel() - Method in class weka.core.KDTree
Gets the debug level.
getDecay() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getDeduper() - Method in class weka.experiment.DeduperSplitEvaluator
Get the value of Deduper.
getDefaultPerturb() - Method in class weka.clusterers.MPCKMeans
Get default perturbation value
getDefaultPerturb() - Method in class weka.clusterers.PCKMeans
Get default perturbation value
getDefaultPerturb() - Method in class weka.clusterers.PCSoftKMeans
Get default perturbation value
getDefaultPerturb() - Method in class weka.clusterers.SeededKMeans
Get default perturbation value
getDefaultWeight() - Method in class weka.classifiers.functions.Winnow
Get the value of defaultWeight.
getDelimiters() - Method in class weka.deduping.metrics.WordTokenizer
Get the delimiters
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.
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.ActiveDecorate
Gets the desired size of the committee.
getDesiredSize() - Method in class weka.classifiers.meta.Crate
Gets the desired size of the committee.
getDesiredSize() - Method in class weka.classifiers.meta.DEC
Gets the desired size of the committee.
getDesiredSize() - Method in class weka.classifiers.meta.Decorate
Gets the desired size of the committee.
getDesiredSize() - Method in class weka.classifiers.meta.Fable
Gets the desired size of the committee.
getDesiredSize() - Method in class weka.classifiers.meta.SemiSupDecorate
Gets the desired size of the committee.
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
getDistMult() - Method in class weka.datagenerators.BIRCHCluster
Gets the distance multiplier.
getDistance(Instance, Instance) - Method in class weka.core.metrics.AttrEvalMetricLearner
Use the Classifier for an estimation of distance
getDistance(Instance, Instance) - Method in class weka.core.metrics.ClassifierMetricLearner
Use the Classifier for an estimation of distance
getDistance(Instance, Instance) - Method in class weka.core.metrics.GDMetricLearner
Use the Classifier for an estimation of distance
getDistance(Instance, Instance) - Method in class weka.core.metrics.MatlabMetricLearner
Use Matlab for an estimation of distance
getDistance(Instance, Instance) - Method in class weka.core.metrics.MetricLearner
Use the metricLearner's internal model for an estimation of distance, e.g.
getDistanceF() - Method in class weka.clusterers.XMeans
Gets the distance function.
getDistanceFSpec() - Method in class weka.clusterers.XMeans
Gets the distance function specification string, which contains the class name of the distance function class and any options to it
getDistanceFunction() - Method in class weka.core.KDTree
Gets the distance function.
getDistanceFunctionSpec() - Method in class weka.core.KDTree
Gets the distance function specification string, which contains the class name of distance function the filter and any options to the filter
getDistanceWeighting() - Method in class weka.classifiers.lazy.IBk
Gets the distance weighting method used.
getDistanceWeighting() - Method in class weka.classifiers.sparse.IBkMetric
Gets the distance weighting method used.
getDistributionClassifier() - Method in class weka.classifiers.meta.MultiClassClassifier
Get the classifier used as the classifier
getDistributionClassifier() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Get the classifier used as the classifier
getDistributionClassifier() - Method in class weka.classifiers.meta.ThresholdSelector
Get the DistributionClassifier used as the classifier.
getDistributionSpread() - Method in class weka.filters.supervised.instance.SpreadSubsample
Gets the value for the distribution spread
getDistributions(int) - Method in class weka.classifiers.rules.RuleStats
Get the class distribution predicted by the rule in given position
getDoActive() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of m_DoActive.
getDoActive() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the value of m_DoActive.
getEMModel() - Method in class weka.gui.boundaryvisualizer.EMDataGenerator
Return the EM model of the data
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) - Method in class weka.clusterers.AlgVector
Returns the value of a cell in the matrix.
getElement(int) - Method in class weka.core.AlgVector
Returns the value of a cell in the matrix.
getElement(int, int) - Method in class weka.core.Matrix
Returns the value of a cell in the matrix.
getElements(int) - Method in class weka.clusterers.AlgVector
Gets the elements of the vector and returns them as double array.
getElements(int) - Method in class weka.core.AlgVector
Gets the elements of the vector and returns them as double array.
getEliminateColinearAttributes() - Method in class weka.classifiers.functions.LinearRegression
Get the value of EliminateColinearAttributes.
getEliminateColinearAttributes() - Method in class weka.classifiers.lazy.LWR
Get the value of EliminateColinearAttributes.
getEngineType() - Method in class weka.clusterers.assigners.LPAssigner
Get the engine type
getEnsemblePredictions(Instance) - Method in class weka.classifiers.EnsembleClassifier
Returns class predictions of each ensemble member
getEnsemblePredictions(Instance) - Method in class weka.classifiers.meta.AdaBoostM1
Returns class predictions of each ensemble member
getEnsemblePredictions(Instance) - Method in class weka.classifiers.meta.Bagging
Returns class predictions of each ensemble member
getEnsemblePredictions(Instance) - Method in class weka.classifiers.meta.DEC
Returns class predictions of each ensemble member
getEnsemblePredictions(Instance) - Method in class weka.classifiers.meta.Decorate
Returns class predictions of each ensemble member
getEnsemblePredictions(Instance) - Method in class weka.classifiers.meta.QBag
Returns class predictions of each ensemble member
getEnsemblePredictions(Instance) - Method in class weka.classifiers.meta.QBoost
Returns class predictions of each ensemble member
getEnsemblePredictions(Instance) - Method in class weka.classifiers.meta.SemiSupDecorate
Returns class predictions of each ensemble member
getEnsemblePredictions(Instance) - Method in class weka.classifiers.meta.TestEnsembleClassifier
Returns class predictions of each ensemble member
getEnsembleSize() - Method in class weka.classifiers.EnsembleClassifier
Returns size of ensemble
getEnsembleSize() - Method in class weka.classifiers.meta.AdaBoostM1
Returns size of ensemble
getEnsembleSize() - Method in class weka.classifiers.meta.Bagging
Returns size of ensemble
getEnsembleSize() - Method in class weka.classifiers.meta.DEC
Returns size of ensemble
getEnsembleSize() - Method in class weka.classifiers.meta.Decorate
Returns size of ensemble
getEnsembleSize() - Method in class weka.classifiers.meta.QBag
Returns size of ensemble
getEnsembleSize() - Method in class weka.classifiers.meta.QBoost
Returns size of ensemble
getEnsembleSize() - Method in class weka.classifiers.meta.SemiSupDecorate
Returns size of ensemble
getEnsembleSize() - Method in class weka.classifiers.meta.TestEnsembleClassifier
Returns size of ensemble
getEnsembleWts() - Method in class weka.classifiers.EnsembleClassifier
Returns vote weights of ensemble members.
getEnsembleWts() - Method in class weka.classifiers.meta.AdaBoostM1
Returns vote weights of ensemble members.
getEnsembleWts() - Method in class weka.classifiers.meta.Bagging
Returns vote weights of ensemble members.
getEnsembleWts() - Method in class weka.classifiers.meta.DEC
Returns vote weights of ensemble members.
getEnsembleWts() - Method in class weka.classifiers.meta.Decorate
Returns vote weights of ensemble members.
getEnsembleWts() - Method in class weka.classifiers.meta.QBag
Returns vote weights of ensemble members.
getEnsembleWts() - Method in class weka.classifiers.meta.QBoost
Returns vote weights of ensemble members.
getEnsembleWts() - Method in class weka.classifiers.meta.SemiSupDecorate
Returns vote weights of ensemble members.
getEnsembleWts() - Method in class weka.classifiers.meta.TestEnsembleClassifier
Returns vote weights of ensemble members.
getEntropicAutoBlend() - Method in class weka.classifiers.lazy.kstar.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.
getEpsilon() - Method in class weka.classifiers.functions.SMO
Get the value of epsilon.
getEpsilon() - Method in class weka.core.metrics.GDMetricLearner
Get the convergence criterion
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.RegressionBVDecompose
Get the calculated error rate
getErrorMeasure() - Method in class weka.classifiers.meta.Crate
Get the value of errorMeasure.
getEstimatedErrorsForLeaf() - Method in class weka.classifiers.rules.part.C45PruneableDecList
Computes estimated errors for leaf.
getEstimator() - Method in class weka.classifiers.functions.pace.PaceRegression
Gets the estimator
getEstimator(double) - Method in interface weka.estimators.ConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DDConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DKConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.DNConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.KDConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.KKConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NDConditionalEstimator
Get a probability estimator for a value
getEstimator(double) - Method in class weka.estimators.NNConditionalEstimator
Get a probability estimator for a value
getEta() - Method in class weka.clusterers.MPCKMeans
Get the initial value of gradient descent eta
getEtaDecayRate() - Method in class weka.clusterers.MPCKMeans
Get the initial value of the decay rate of gradient descent eta
getEvaluationMode() - Method in class weka.classifiers.meta.ThresholdSelector
Gets the evaluation mode used.
getEvaluator() - Method in class weka.attributeSelection.MatlabICA
Gets the attribute evaluator used
getEvaluator() - Method in class weka.attributeSelection.MatlabNMF
Gets the attribute evaluator used
getEvaluator() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the attribute evaluator used
getEvaluator() - Method in class weka.core.metrics.AttrEvalMetricLearner
Get the evaluator
getEvaluator() - Method in class weka.filters.supervised.attribute.AttributeSelection
Get the name of the attribute/subset evaluator
getEvaluatorSpec() - Method in class weka.attributeSelection.MatlabNMF
Gets the evaluator specification string, which contains the class name of the attribute evaluator and any options to it
getEvaluatorSpec() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the evaluator specification string, which contains the class name of the attribute evaluator and any options to it
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractDataSourceBeanInfo
Get the event set descriptors pertinent to data sources
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTestSetProducerBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.AbstractTrainingSetProducerBeanInfo
Returns event set descriptors for this type of bean
getEventSetDescriptors() - Method in class weka.gui.beans.ClassAssignerBeanInfo
Returns the event set descriptors
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
 
getEventSetDescriptors() - Method in class weka.gui.beans.DataVisualizerBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.FilterBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.GraphViewerBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.StripChartBeanInfo
Get the event set descriptors for this bean
getEventSetDescriptors() - Method in class weka.gui.beans.TextViewerBeanInfo
Get the event set descriptors for this bean
getExclusive() - Method in class weka.classifiers.rules.ConjunctiveRule
 
getExecutionStatus() - Method in class weka.experiment.TaskStatusInfo
Get the execution status of this Task.
getExpScalingFactor() - Method in class weka.clusterers.assigners.RMNAssigner
 
getExpectedResultsPerAverage() - Method in class weka.experiment.AveragingResultProducer
Get the value of ExpectedResultsPerAverage.
getExperiment() - Method in class weka.experiment.RemoteExperimentSubTask
Get the experiment for this sub task
getExperiment() - Method in class weka.gui.experiment.SetupModePanel
Gets the currently configured experiment.
getExperiment() - Method in class weka.gui.experiment.SetupPanel
Gets the currently configured experiment.
getExperiment() - Method in class weka.gui.experiment.SimpleSetupPanel
Gets the currently configured experiment.
getExponent() - Method in class weka.classifiers.functions.SMO
Get the value of exponent.
getExponent() - Method in class weka.classifiers.functions.VotedPerceptron
Get the value of exponent.
getExpression() - Method in class weka.filters.unsupervised.attribute.AddExpression
Get the expression
getExternal() - Method in class weka.core.metrics.LearnableMetric
Get the value of m_external
getExtraPhase1RunFraction() - Method in class weka.clusterers.SeededKMeans
Return the number of extra phase1 runs
getExtractor() - Method in class weka.experiment.ExtractionSplitEvaluator
Get the value of Extractor.
getExtractor() - Method in class weka.extraction.ClusteringExtractor
Get the extractor
getFMeasure() - Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the F-Measure.
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.
getFeatureSpaceNormalization() - Method in class weka.classifiers.functions.SMO
Check whether feature spaces is being normalized.
getFile() - Method in class weka.core.converters.ArffLoader
get the File specified as the source
getFilesRecursively(File, Vector) - Method in class weka.gui.experiment.DatasetListPanel
Gets all the files in the given directory that match the currently selected extension.
getFillWithMissing() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Gets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
getFilter() - Method in class weka.classifiers.meta.FilteredClassifier
Gets the filter used.
getFilter() - Method in class weka.gui.beans.Filter
 
getFilterSpec() - Method in class weka.classifiers.meta.FilteredClassifier
Gets the filter specification string, which contains the class name of the filter and any options to the filter
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.
getFiltered(int) - Method in class weka.classifiers.rules.RuleStats
Get the data after filtering the given rule
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.Discretize
Get the value of FindNumBins.
getFindNumBins() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Get the value of FindNumBins.
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.
getFitness() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
gets the scaled fitness
getFlag(char, String[]) - Static method in class weka.core.Utils
Checks if the given array contains the flag "-Char".
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.
getFoldCreationMode() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
return the fold creation mode
getFoldCreationMode() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
return the fold creation mode
getFolds() - Method in class weka.attributeSelection.WrapperSubsetEval
Get the number of folds used for accuracy estimation
getFolds() - Method in class weka.classifiers.rules.ConjunctiveRule
 
getFolds() - Method in class weka.classifiers.rules.JRip
 
getFolds() - Method in class weka.classifiers.rules.Ridor
 
getFolds() - Method in class weka.gui.beans.CrossValidationFoldMaker
Get the currently set number of folds
getFoldsType() - Method in class weka.attributeSelection.RaceSearch
Get the xfold type
getFormat() - Method in class weka.datagenerators.ClusterGenerator
Gets the format of the dataset that is to be generated.
getFormat() - Method in class weka.datagenerators.Generator
Gets the format of the dataset that is to be generated.
getFraction() - Method in class weka.experiment.PairedTTester
Returns true if fractions are specified for learning curves
getFunctionValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Gets a particular function value
getFunctionValues() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Gets all function values
getGCount(Node, int) - Static method in class weka.gui.treevisualizer.Node
Recursively finds the number of visible groups of siblings there are.
getGUI() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getGamma() - Method in class weka.classifiers.functions.SMO
Get the value of gamma.
getGamma() - Method in class weka.classifiers.sparse.SVMlight
Get parameter gamma in rbf kernel
getGapExtendCost() - Method in class weka.deduping.metrics.AffineMetric
Get the gap extension cost
getGapStartCost() - Method in class weka.deduping.metrics.AffineMetric
Get the gap opening cost
getGenerateRanking() - Method in class weka.attributeSelection.ForwardSelection
Gets whether ranking has been requested.
getGenerateRanking() - Method in class weka.attributeSelection.RaceSearch
Gets whether ranking has been requested.
getGenerateRanking() - Method in interface weka.attributeSelection.RankedOutputSearch
Gets whether the user has opted to see a ranked list of attributes rather than the normal result of the search
getGenerateRanking() - Method in class weka.attributeSelection.Ranker
This is a dummy method.
getGenerateRules() - Method in class weka.classifiers.trees.m5.M5Base
get whether rules are being generated rather than a tree
getGlobalBlend() - Method in class weka.classifiers.lazy.kstar.KStar
Get the value of the global blend parameter
getGradients(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetric
Get the values of the partial derivates for the metric components for a particular instance pair
getGradients(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetricMatlab
Get the values of the partial derivates for the metric components for a particular instance pair
getGradients(Instance, Instance) - Method in class weka.core.metrics.KL
Get the values of the partial derivates for the metric components for a particular instance pair
getGradients(Instance, Instance) - Method in class weka.core.metrics.LearnableMetric
Get the values of the partial derivates for the metric components for a particular instance pair
getGradients(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Get the values of the partial derivates for the metric components for a particular instance pair
getGradients(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Get the values of the partial derivates for the metric components for a particular instance pair
getGradients(Instance, Instance) - Method in class weka.core.metrics.WeightedMahalanobis
Get the values of the partial derivates for the metric components for a particular instance pair
getGradients(Instance, Instance) - Method in class weka.core.metrics.XingMetric
Get the values of the partial derivates for the metric components for a particular instance pair
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).
getGroup() - Method in class weka.attributeSelection.BestFirst.Link2
Get a group
getGroup() - Method in class weka.classifiers.rules.DecisionTable.Link
Gets the group.
getHardVoteAssignment() - Method in class weka.classifiers.meta.QBag
Get the value of m_HardVoteAssignment.
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.
getHiddenLayers() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getHoldOutFile() - Method in class weka.attributeSelection.ClassifierSubsetEval
Gets the file that holds hold out/test instances.
getICAapproach() - Method in class weka.attributeSelection.MatlabICA
get ICA approach
getICAfunction() - Method in class weka.attributeSelection.MatlabICA
get ICA function
getID() - Method in class weka.core.Tag
Gets the numeric ID of the Tag.
getID() - Method in class weka.gui.streams.InstanceEvent
Get the event type
getID() - Method in class weka.gui.treevisualizer.TreeDisplayEvent
 
getId() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getIgnoredAttributeIndices() - Method in class weka.filters.unsupervised.attribute.AddCluster
Gets ranges of attributes to be ignored.
getIndexClusters() - Method in class weka.clusterers.MPCKMeans
Computes the clusters from the cluster assignments, for external access
getIndexClusters() - Method in class weka.clusterers.PCKMeans
Computes the clusters from the cluster assignments, for external access
getIndexClusters() - Method in class weka.clusterers.PCSoftKMeans
Computes the clusters from the cluster assignments, for external access
getIndexClusters() - Method in class weka.clusterers.SeededKMeans
Computes the clusters from the cluster assignments, for external access
getInitAsNaiveBayes() - Method in class weka.classifiers.bayes.BayesNet
Method declaration
getInputCenterFile() - Method in class weka.clusterers.XMeans
Gets the name of the file to read the list of centers from.
getInputFormat() - Method in class weka.filters.Filter
Gets the currently set inputformat instances.
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.
getInputStringIndex() - Method in class weka.filters.Filter
Returns an array containing the indices of all string attributes in the input format.
getInputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
Use this to get easy access to the inputs.
getInstList() - Method in class weka.core.KDTree
Gets instance list used in the tree.
getInstNums() - Method in class weka.datagenerators.BIRCHCluster
Gets the upper and lower boundary for instances per cluster.
getInstance(StreamTokenizer, boolean) - Method in class weka.core.Instances
Reads a single instance using the tokenizer and appends it to the dataset.
getInstance(TextSource.Real) - Method in class weka.datagenerators.TextSource
Tokenizes a document and transforms it into a sparse vector.
getInstance() - Method in class weka.gui.beans.InstanceEvent
Get the instance
getInstanceConstraintsHash() - Method in class weka.clusterers.MPCKMeans
 
getInstanceFull(StreamTokenizer, boolean) - Method in class weka.core.Instances
Reads a single instance using the tokenizer and appends it to the dataset.
getInstanceIndex(int) - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the boolean value at the specified index in the Instance Index array
getInstanceOrdering() - Method in class weka.clusterers.PCKMeans
Get the instance ordering
getInstanceRange() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Gets the number of instances forward to translate values between.
getInstanceSparse(StreamTokenizer, boolean) - Method in class weka.core.Instances
Reads a single instance using the tokenizer and appends it to the dataset.
getInstances() - Method in class weka.clusterers.HAC
Return training instances
getInstances() - Method in class weka.clusterers.MPCKMeans
Return training instances
getInstances() - Method in class weka.clusterers.PCKMeans
Return training instances
getInstances() - Method in class weka.clusterers.PCSoftKMeans
Return training instances
getInstances() - Method in class weka.clusterers.SeededKMeans
Return training instances
getInstances() - Method in interface weka.clusterers.SemiSupClusterer
Return the instances used for clustering
getInstances() - Method in class weka.core.KDTree
Gets instances used in the tree.
getInstances(int[], StringMetric[][], int, int) - Method in class weka.deduping.PairwiseSelector
Generate a training set of diffInstances.
getInstances() - Method in class weka.experiment.PairedTTester
Get the value of Instances.
getInstances() - Method in class weka.gui.SetInstancesPanel
Gets the set of instances currently held by the panel
getInstances() - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Get the training instances
getInstances() - Method in class weka.gui.explorer.PreprocessPanel
Gets the working set of instances.
getInstances() - Method in class weka.gui.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
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.
getIntClusters() - Method in class weka.clusterers.HAC
Computes the clusters from the cluster assignments
getInvert() - Method in class weka.core.Range
Gets whether the range sense is inverted, i.e.
getInvert() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Get whether selection is inverted.
getInvertSelection() - Method in class weka.filters.supervised.attribute.Discretize
Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Copy
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Get whether the supplied columns are to be transformed or not
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.Remove
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.attribute.RemoveType
Get whether the supplied columns are to be removed or kept
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveRange
Gets if selection is to be inverted.
getInvertSelection() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Get whether the supplied columns are to be removed or kept
getIsRandom() - Method in class weka.classifiers.functions.LeastMedSq
Returns whether or not samples are selected randomly
getIsRandomNeighborhoods() - Method in class weka.clusterers.MPCKMeans
Get value of m_IsRandomNeighborhoods
getIsTransductive() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get the value of IsTransductive.
getIsTransductive() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get the value of IsTransductive.
getIsTransductive() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of IsTransductive.
getIsTransductive() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the value of IsTransductive.
getIterations() - Method in class weka.attributeSelection.MatlabNMF
Gets the number of iterations for gradient descent.
getJavaInitializationString() - Method in class weka.gui.CostMatrixEditor
Returns the Java code that generates an object the same as the one being edited.
getJavaInitializationString() - Method in class weka.gui.FileEditor
Returns a representation of the current property value as java source.
getJavaInitializationString() - Method in class weka.gui.GenericArrayEditor
Supposedly returns an initialization string to create a classifier identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString() - Method in class weka.gui.GenericObjectEditor
Supposedly returns an initialization string to create a Object identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString() - Method in class weka.gui.SelectedTagEditor
Returns a description of the property value as java source.
getKDTree() - Method in class weka.classifiers.lazy.IBk
Gets the KDTree class.
getKDTree() - Method in class weka.clusterers.XMeans
Gets the KDTree class.
getKDTreeSpec() - Method in class weka.classifiers.lazy.IBk
Gets the KDTree specification string, which contains the class name of the KDTree class and any options to the KDTree
getKDTreeSpec() - Method in class weka.clusterers.XMeans
Gets the KDTree specification string, which contains the class name of the KDTree class and any options to the KDTree
getKNN() - Method in class weka.classifiers.lazy.IBk
Gets the number of neighbours the learner will use.
getKNN() - Method in class weka.classifiers.lazy.LWR
Gets the number of neighbours used for kernel bandwidth setting.
getKNN() - Method in class weka.classifiers.sparse.IBkMetric
Gets the number of neighbours the learner will use.
getKValue() - Method in class weka.classifiers.trees.RandomTree
Get the value of K.
getKernelType() - Method in class weka.classifiers.sparse.SVMlight
Get the SVM-light kernel type
getKey() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.DeduperSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.ExtractionSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the key describing the current SplitEvaluator.
getKey() - Method in interface weka.experiment.SplitEvaluator
Gets the key describing the current SplitEvaluator.
getKeyFieldName() - Method in class weka.experiment.AveragingResultProducer
Get the value of KeyFieldName.
getKeyNames() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Gets the names of each of the 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.CrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DatabaseResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DeduperSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.ExtractionResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.ExtractionSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.LearningRateResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.RandomSplitResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in interface weka.experiment.ResultProducer
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the names of each of the key columns produced for a single run.
getKeyNames() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getKeyNames() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets the names of each of the 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.
getKeyTypes() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Gets the data types of each of the 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.CrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DatabaseResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DeduperSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.ExtractionResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.ExtractionSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.LearningRateResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.RandomSplitResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in interface weka.experiment.ResultProducer
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the data types of each of the key columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets the data types of each of the columns produced for a single run.
getKeyTypes() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets the data types of each of the 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.
getL() - Method in class weka.core.Matrix
Returns the L part of the matrix.
getLabel() - Method in class weka.gui.treevisualizer.Edge
Get the value of label.
getLabel() - Method in class weka.gui.treevisualizer.Node
Get the value of label.
getLambda() - Method in class weka.classifiers.bayes.SemiSupEM
 
getLambdaJM() - Method in class weka.core.metrics.KL
Get the lambda parameter for Jelinek-Mercer smoothing
getLearningCurve() - Method in class weka.experiment.PairedTTester
Returns true if learning curves have to be analyzed
getLearningRate() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getLearningRate() - Method in class weka.core.metrics.GDMetricLearner
Get the learning rate
getLegendText() - Method in class weka.gui.beans.ChartEvent
Get the legend text vector
getLengthNormalized() - Method in class weka.core.metrics.WeightedDotP
Check whether similarity is normalized by the length of the vectors
getLevel() - Method in class weka.gui.HierarchyPropertyParser
Get the level of current node.
getLikelihoodThreshold() - Method in class weka.classifiers.meta.LogitBoost
Get the value of Precision.
getLine(int) - Method in class weka.gui.treevisualizer.Edge
Returns line number n
getLine(int) - Method in class weka.gui.treevisualizer.Node
Returns the text String for the specfied line.
getLink() - Method in class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd
 
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.
getLinkingType() - Method in class weka.clusterers.HAC
Get the linking type
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
 
getLoadEigenValuesFromFile() - Method in class weka.attributeSelection.MatlabICA
get m_loadEigenValuesFromFile
getLoadEigenVectorsFromFile() - Method in class weka.attributeSelection.MatlabICA
get m_loadEigenVectorsFromFile
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
getLogTermWeight() - Method in class weka.clusterers.MPCKMeans
Get the value of the weight assigned to log term in the objective function
getLogTimestamp() - Static method in class weka.attributeSelection.MatlabICA
Get a timestamp string as a weak uniqueid
getLogTimestamp() - Static method in class weka.attributeSelection.MatlabPCA
Get a timestamp string as a weak uniqueid
getLogTimestamp() - Static method in class weka.core.metrics.BarHillelMetric
Get a timestamp string as a weak uniqueid
getLogTimestamp() - Static method in class weka.core.metrics.BarHillelMetricMatlab
Get a timestamp string as a weak uniqueid
getLogTimestamp() - Static method in class weka.core.metrics.XingMetric
Get a timestamp string as a weak uniqueid
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.
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.
getLowerSize() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Get the value of LowerSize.
getLowerSize() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get the value of LowerSize.
getLowerSize() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get the value of LowerSize.
getLowerSize() - Method in class weka.experiment.LearningRateResultProducer
Get the value of LowerSize.
getLowerSize() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get the value of LowerSize.
getLowerSize() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get the value of LowerSize.
getLowerSize() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get the value of LowerSize.
getLowerSize() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of LowerSize.
getLowerSize() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the value of LowerSize.
getM() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Get Laplace m parameter that controls amouont of smoothing
getM() - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Get Laplace m parameter that controls amouont of smoothing
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
getMatchCost() - Method in class weka.deduping.metrics.AffineMetric
Get the match cost
getMatchMissingValues() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Gets whether missing values are counted as a match.
getMatrix(int, int, int, int) - Method in class weka.classifiers.functions.pace.Matrix
Get a submatrix.
getMatrix(int[], int[]) - Method in class weka.classifiers.functions.pace.Matrix
Get a submatrix.
getMatrix(int, int, int[]) - Method in class weka.classifiers.functions.pace.Matrix
Get a submatrix.
getMatrix(int[], int, int) - Method in class weka.classifiers.functions.pace.Matrix
Get a submatrix.
getMax() - Method in class weka.gui.beans.ChartEvent
Get the max y value
getMaxBlankIterations() - Method in class weka.clusterers.MPCKMeans
Get the maximum number of blank iterations
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
getMaxImplicitCommonTokenFraction() - Method in class weka.deduping.PairwiseSelector
Get the maximum fraction of common tokens that instances can have to be included as implicit negatives
getMaxInstInLeaf() - Method in class weka.core.KDTree
Get the maximum number of instances in a leaf.
getMaxInstNum() - Method in class weka.datagenerators.BIRCHCluster
Gets the upper boundary for instances per cluster.
getMaxIterations() - Method in class weka.classifiers.bayes.SemiSupEM
Get the maximum number of iterations
getMaxIterations() - Method in class weka.classifiers.meta.AdaBoostM1
Get the maximum number of boost iterations
getMaxIterations() - Method in class weka.classifiers.meta.LogitBoost
Get the maximum number of boost iterations
getMaxIterations() - Method in class weka.classifiers.meta.MultiBoostAB
Get the maximum number of boost iterations
getMaxIterations() - Method in class weka.classifiers.meta.QBoost
Get the maximum number of boost iterations
getMaxIterations() - Method in class weka.clusterers.EM
Get the maximum number of iterations
getMaxIterations() - Method in class weka.clusterers.MPCKMeans
Get the maximum number of iterations
getMaxIterations() - Method in class weka.clusterers.XMeans
Gets the maximum number of iterations.
getMaxIterations() - Method in class weka.core.metrics.GDMetricLearner
Get the maximum number of update iterations rate
getMaxIterations() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the maximum number of cleansing iterations performed
getMaxIts() - Method in class weka.classifiers.functions.Logistic
Get the value of MaxIts.
getMaxK() - Method in class weka.classifiers.functions.VotedPerceptron
Get the value of maxK.
getMaxKMeans() - Method in class weka.clusterers.XMeans
Gets the maximum number of iterations in KMeans.
getMaxKMeansForChildren() - Method in class weka.clusterers.XMeans
Gets the maximum number of iterations in KMeans.
getMaxMargin() - Method in class weka.classifiers.sparse.SVMlight
Get the maxMargin that an SVM can return
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
getMaxNumClusters() - Method in class weka.clusterers.XMeans
Gets the maximum number of clusters to generate.
getMaxPoints(HashMap, Instances) - Method in class weka.core.metrics.WeightedMahalanobis
Get the maxPoints instances
getMaxRadius() - Method in class weka.datagenerators.BIRCHCluster
Gets the upper boundary for the radiuses of the clusters.
getMaxRuleSize() - Method in class weka.datagenerators.RDG1
Gets the maximum number of tests in rules.
getMaxSetNumber() - Method in class weka.gui.beans.BatchClassifierEvent
Get the maximum set number (ie the total number of training and testing sets in the series).
getMaxSetNumber() - Method in class weka.gui.beans.TestSetEvent
Get the maximum set number
getMaxSetNumber() - Method in class weka.gui.beans.TrainingSetEvent
Get the maximum set number
getMaxStale() - Method in class weka.classifiers.rules.DecisionTable
Gets the number of non improving decision tables
getMaxTimesPointsMoved() - Method in class weka.clusterers.assigners.RandomAssigner
Get/set the number of times points can be moved
getMaxTimesPointsMoved() - Method in class weka.clusterers.assigners.SimpleAssigner
Get/set the number of times points can be moved
getMaxTimesPointsMoved() - Method in class weka.clusterers.assigners.SortedAssigner
Get/set the number of times points can be moved
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
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.
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 weka.classifiers.sparse.IBkMetric
Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
getMeasure(String) - Method in class weka.classifiers.EnsembleClassifier
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.rules.DecisionTable
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.JRip
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.Ridor
Returns the value of the named measure
getMeasure(String) - Method in class weka.classifiers.rules.part.PART
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.adtree.ADTree
Returns the value of the named measure.
getMeasure(String) - Method in class weka.classifiers.trees.j48.J48
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 interface weka.core.AdditionalMeasureProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.AveragingResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.ClassifierSplitEvaluator
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.CrossValidationResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DatabaseResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.ExtractionResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
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.NoiseCurveCrossValidationResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RandomSplitResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.RegressionSplitEvaluator
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the value of the named measure
getMeasure(String) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the value of the named measure
getMergeThreshold() - Method in class weka.clusterers.HAC
Get the merge threshold
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.functions.neural.NeuralNode
 
getMethod() - Method in class weka.classifiers.meta.MultiClassClassifier
Gets the method used.
getMethodName() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Get the transformation method.
getMetric() - Method in class weka.classifiers.misc.PrototypeMetric
Get the distance metric
getMetric() - Method in class weka.classifiers.sparse.IBkMetric
Get the distance metric
getMetric() - Method in class weka.clusterers.HAC
Get the distance metric
getMetric() - Method in class weka.clusterers.MPCKMeans
get the distance metric
getMetric() - Method in class weka.clusterers.PCKMeans
Get the distance metric
getMetric() - Method in class weka.clusterers.PCSoftKMeans
Get the distance metric
getMetric() - Method in class weka.clusterers.SeededKMeans
Get the distance metric
getMetric() - Method in class weka.deduping.BasicDeduper
Get the InstanceMetric that is used
getMetric() - Method in class weka.deduping.metrics.SumInstanceMetric
Get the baseline metric
getMetricLearner() - Method in class weka.core.metrics.KL
Get the distance metric learner
getMetricLearner() - Method in class weka.core.metrics.WeightedDotP
Get the distance metric learner
getMetricLearner() - Method in class weka.core.metrics.WeightedEuclidean
Get the distance metric learner
getMetricLearnerSpec() - Method in class weka.core.metrics.KL
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getMetricLearnerSpec() - Method in class weka.core.metrics.WeightedEuclidean
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getMetricSpec() - Method in class weka.classifiers.sparse.IBkMetric
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getMetricSpec() - Method in class weka.clusterers.SeededKMeans
Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier
getMetricType() - Method in class weka.associations.Apriori
Get the metric type
getMetrics() - Method in class weka.clusterers.MPCKMeans
get the array of metrics
getMiddle(double[]) - Method in class weka.core.DistanceFunction
Returns value in the middle of the two parameter values.
getMiddle(double[]) - Method in class weka.core.EuclideanDistance
Returns value in the middle of the two parameter values.
getMin() - Method in class weka.gui.beans.ChartEvent
Get the min y value
getMinBoxRelWidth() - Method in class weka.core.KDTree
Gets the minimum relative box width.
getMinBucketSize() - Method in class weka.classifiers.rules.OneR
Get the value of minBucketSize.
getMinC() - Method in class weka.gui.visualize.Plot2D
Return the current min value of the colouring attribute
getMinChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the minimum chunk size
getMinCommonTokens() - Method in class weka.deduping.metrics.SumInstanceMetric
Get the minimum number of common tokens that is required from objects to be considered for distance computation
getMinFunction() - Method in class weka.core.Optimization
Get the minimal function value
getMinInstNum() - Method in class weka.datagenerators.BIRCHCluster
Gets the lower boundary for instances per cluster.
getMinMargin() - Method in class weka.classifiers.sparse.SVMlight
Get the minMargin that an SVM can return
getMinMetric() - Method in class weka.associations.Apriori
Get the value of minConfidence.
getMinNo() - Method in class weka.classifiers.rules.ConjunctiveRule
 
getMinNo() - Method in class weka.classifiers.rules.JRip
 
getMinNo() - Method in class weka.classifiers.rules.Ridor
 
getMinNum() - Method in class weka.classifiers.trees.REPTree
Get the value of MinNum.
getMinNum() - Method in class weka.classifiers.trees.RandomTree
Get the value of MinNum.
getMinNumClusters() - Method in class weka.clusterers.XMeans
Gets the minimum number of clusters to generate.
getMinNumInstances() - Method in class weka.classifiers.trees.m5.M5Base
Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.Rule
Get the minimum number of instances to allow at a leaf node
getMinNumInstances() - Method in class weka.classifiers.trees.m5.RuleNode
Get the minimum number of instances to allow at a leaf node
getMinNumObj() - Method in class weka.classifiers.rules.part.PART
Get the value of minNumObj.
getMinNumObj() - Method in class weka.classifiers.trees.j48.J48
Get the value of minNumObj.
getMinRadius() - Method in class weka.datagenerators.BIRCHCluster
Gets the lower boundary for the radiuses of the clusters.
getMinRuleSize() - Method in class weka.datagenerators.RDG1
Gets the minimum number of tests in rules.
getMinStdDev() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Get the minimum allowable standard deviation.
getMinStdDev() - Method in class weka.clusterers.EM
Get the minimum allowable standard deviation.
getMinTokenLength() - Method in class weka.deduping.metrics.WordTokenizer
Get the minimum token length
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
getMinimizeExpectedCost() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Gets the value of MinimizeExpectedCost.
getMissingMerge() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.GainRatioAttributeEval
get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.InfoGainAttributeEval
get whether missing values are being distributed or not
getMissingMerge() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
get whether missing values are being distributed or not
getMissingMode() - Method in class weka.classifiers.lazy.kstar.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
getMixingDistribution() - Method in class weka.classifiers.functions.pace.MixtureDistribution
Gets the mixing distribution
getMode() - Method in class weka.classifiers.sparse.SVMlight
return the SVM-light mode
getMode() - Method in class weka.extraction.ClusteringExtractor
return the clustering mode
getModel() - Method in class weka.classifiers.trees.m5.RuleNode
Get the linear model 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.neural.NeuralNetwork
 
getMostSimilarPairs(int) - Method in class weka.deduping.blocking.Blocking
Return n most similar pairs
getMovePointsTillAssignmentStabilizes() - Method in class weka.clusterers.PCKMeans
Return m_MovePointsTillAssignmentStabilizes
getMovePointsTillAssignmentStabilizes() - Method in class weka.clusterers.assigners.RandomAssigner
 
getMovePointsTillAssignmentStabilizes() - Method in class weka.clusterers.assigners.SimpleAssigner
 
getMovePointsTillAssignmentStabilizes() - Method in class weka.clusterers.assigners.SortedAssigner
 
getMustLinkWeight() - Method in class weka.clusterers.MPCKMeans
Return the must link constraint weight
getMustLinkWeight() - Method in class weka.clusterers.PCKMeans
Return the must link constraint weight
getMustLinkWeight() - Method in class weka.clusterers.PCSoftKMeans
Return the must link constraint weight
getMutationProb() - Method in class weka.attributeSelection.GeneticSearch
get the probability of mutation
getN() - Method in class weka.deduping.metrics.NGramTokenizer
Get the gram length
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.
getName() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the name of the new attribute
getName() - Method in class weka.gui.visualize.VisualizePanel
Returns the name associated with this plot.
getNameAtIndex(int) - Method in class weka.gui.ResultHistoryPanel
Gets the name of theitem in the list at the specified index
getNamedBuffer(String) - Method in class weka.gui.ResultHistoryPanel
Gets the named buffer
getNamedObject(String) - Method in class weka.gui.ResultHistoryPanel
Get the named object from the list
getNegStringMode() - Method in class weka.deduping.PairwiseSelector
return the selection mode for negative string examples
getNegativesMode() - Method in class weka.core.metrics.HardPairwiseSelector
return the selection mode for negatives
getNegativesMode() - Method in class weka.deduping.PairwiseSelector
return the selection mode for negatives
getNewDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.C45PruneableDecList
Returns a newly created tree.
getNewDecList(Instances, boolean) - Method in class weka.classifiers.rules.part.ClassifierDecList
Returns a newly created tree.
getNewDecList(Instances, Instances, boolean) - Method in class weka.classifiers.rules.part.PruneableDecList
Returns a newly created tree.
getNewTree(Instances) - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Returns a newly created tree.
getNewTree(Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns a newly created tree.
getNewTree(Instances, Instances) - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns a newly created tree.
getNewTree(Instances, Instances) - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
Returns a newly created tree.
getNextDebugVektorsInstance(Instances) - Method in class weka.clusterers.XMeans
Read an instance from debug vektors file.
getNextInstance() - Method in class weka.core.converters.AbstractLoader
 
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.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.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.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.datagenerators.TextSource.Table
 
getNoNormalization() - Method in class weka.classifiers.lazy.IBk
Gets whether normalization is turned off.
getNoPruning() - Method in class weka.classifiers.trees.REPTree
Get the value of NoPruning.
getNoiseRate() - Method in class weka.datagenerators.BIRCHCluster
Gets the percentage of noise set.
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.neural.NeuralNetwork
 
getNormalize() - Method in class weka.attributeSelection.MatlabICA
Gets whether or not input data is to be normalized
getNormalize() - Method in class weka.attributeSelection.MatlabNMF
Gets whether or not input data is to be normalized
getNormalize() - Method in class weka.attributeSelection.MatlabPCA
Gets whether or not input data is to be normalized
getNormalize() - Method in class weka.attributeSelection.PrincipalComponents
Gets whether or not input data is to be normalized
getNormalize() - Method in class weka.core.KDTree
Gets the normalize flag.
getNormalizeAttributes() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getNormalizeAttributes() - Method in class weka.classifiers.misc.Prototype
 
getNormalizeNumericClass() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getNormalized() - Method in class weka.deduping.metrics.AffineMetric
Get whether the distance is normalized by the sum of the string's lengths
getNormalized() - Method in class weka.deduping.metrics.AffineProbMetric
Get whether the distance is normalized by the sum of the string's lengths
getNot() - Method in class weka.datagenerators.Test
Negates the test.
getNotes() - Method in class weka.experiment.Experiment
Get the user notes.
getNumActualNegPairs() - Method in class weka.deduping.metrics.InstanceMetric
Return the actual number of negative training instances used in the last training round
getNumActualPosPairs() - Method in class weka.deduping.metrics.InstanceMetric
Return the actual number of positive training instances used in the last training round
getNumAntds() - Method in class weka.classifiers.rules.ConjunctiveRule
 
getNumAttributes() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of attributes in the dataset
getNumAttributes() - Method in class weka.core.metrics.Metric
Get the number of attributes that the metric uses
getNumAttributes() - Method in class weka.datagenerators.ClusterGenerator
Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.datagenerators.Generator
Gets the number of attributes that should be produced.
getNumAttributes() - Method in class weka.deduping.metrics.InstanceMetric
Get the number of attributes that the metric uses
getNumAttributesSet() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of attributes "in use"
getNumBins() - Method in class weka.classifiers.meta.RegressionByDiscretization
Gets the number of bins the class attribute will be discretized into.
getNumClasses() - Method in class weka.datagenerators.Generator
Gets the number of classes the dataset should have.
getNumClusters() - Method in class weka.clusterers.ClusterEvaluation
Return the number of clusters found for the most recent call to evaluateClusterer
getNumClusters() - Method in class weka.clusterers.EM
Get the number of clusters
getNumClusters() - Method in class weka.clusterers.FarthestFirst
gets the number of clusters to generate
getNumClusters() - Method in class weka.clusterers.HAC
Return the number of clusters
getNumClusters() - Method in class weka.clusterers.MPCKMeans
Return the number of clusters
getNumClusters() - Method in class weka.clusterers.PCKMeans
Return the number of clusters
getNumClusters() - Method in class weka.clusterers.PCSoftKMeans
Return the number of clusters
getNumClusters() - Method in class weka.clusterers.SeededKMeans
Return the number of clusters
getNumClusters() - Method in interface weka.clusterers.SemiSupClusterer
Get the number of clusters.
getNumClusters() - Method in class weka.clusterers.SimpleKMeans
gets the number of clusters to generate
getNumClusters() - Method in class weka.datagenerators.ClusterGenerator
Gets the number of clusters the dataset should have.
getNumCycles() - Method in class weka.datagenerators.BIRCHCluster
Gets the number of cycles.
getNumDatasets() - Method in class weka.experiment.PairedTTester
Gets the number of datasets in the resultsets
getNumExamples() - Method in class weka.datagenerators.Generator
Gets the number of examples, given by option.
getNumExamplesAct() - Method in class weka.datagenerators.ClusterGenerator
Gets the number of examples the dataset should have.
getNumExamplesAct() - Method in class weka.datagenerators.Generator
Gets the number of examples the dataset should have.
getNumFeatures() - Method in class weka.classifiers.trees.RandomForest
Get the number of features used in random selection.
getNumFolds() - Method in class weka.classifiers.functions.SMO
Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.meta.CVParameterSelection
Get the number of folds used for cross-validation.
getNumFolds() - Method in class weka.classifiers.meta.LogitBoost
Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.meta.MultiScheme
Gets the number of folds for cross-validation.
getNumFolds() - Method in class weka.classifiers.meta.Stacking
Gets the number of folds for the cross-validation.
getNumFolds() - Method in class weka.classifiers.rules.part.PART
Get the value of numFolds.
getNumFolds() - Method in class weka.classifiers.trees.REPTree
Get the value of NumFolds.
getNumFolds() - Method in class weka.classifiers.trees.j48.J48
Get the value of numFolds.
getNumFolds() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.CrossValidationResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.ExtractionResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the value of NumFolds.
getNumFolds() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets the number of folds in which dataset is to be split into.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets the number of folds in which dataset is to be split into.
getNumFolds() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the number of cross-validation folds used by the filter.
getNumGeneratingModels() - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Returns the number of generating models used by this DataGenerator
getNumGeneratingModels() - Method in class weka.gui.boundaryvisualizer.EMDataGenerator
Return the number of clusters generated by EM
getNumGeneratingModels() - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Return the number of kernels (there is one per training instance)
getNumIndependentComponents() - Method in class weka.attributeSelection.MatlabICA
get number of Independent Components
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
getNumInstancesSet() - Method in class weka.classifiers.lazy.LBR.Indexes
Returns the number of instances "in use"
getNumIrrelevant() - Method in class weka.datagenerators.RDG1
Gets the number of irrelevant attributes.
getNumIterations() - Method in class weka.classifiers.functions.VotedPerceptron
Get the value of NumIterations.
getNumIterations() - Method in class weka.classifiers.functions.Winnow
Get the value of numIterations.
getNumIterations() - Method in class weka.classifiers.meta.ActiveDecorate
Gets the max number of Decorate iterations to run.
getNumIterations() - Method in class weka.classifiers.meta.Bagging
Gets the number of bagging iterations
getNumIterations() - Method in class weka.classifiers.meta.Crate
Gets the max number of Crate iterations to run.
getNumIterations() - Method in class weka.classifiers.meta.DEC
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.meta.Fable
Gets the max number of Decorate iterations to run.
getNumIterations() - Method in class weka.classifiers.meta.MetaCost
Gets the number of bagging iterations
getNumIterations() - Method in class weka.classifiers.meta.QBag
Gets the number of bagging iterations
getNumIterations() - Method in class weka.classifiers.meta.SemiSupDecorate
Gets the number of bagging iterations
getNumNegPairs() - Method in class weka.core.metrics.ClassifierMetricLearner
Get the number of different-class training pairs
getNumNegPairs() - Method in class weka.core.metrics.GDMetricLearner
Get the number of different-class training pairs
getNumNegPairs() - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Get the number of different-class training pairs
getNumNegPairs() - Method in class weka.deduping.metrics.SumInstanceMetric
Get the number of different-class training pairs
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.ADTree
Gets the number of boosting iterations.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.Splitter
Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the number of branches of the split.
getNumOfBranches() - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the number of branches of the split.
getNumOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getNumPosDiffInstances() - Method in class weka.core.metrics.LearnableMetric
Set the number of positive instances to be used for training
getNumPosPairs() - Method in class weka.core.metrics.ClassifierMetricLearner
Get the number of same-class training pairs
getNumPosPairs() - Method in class weka.core.metrics.GDMetricLearner
Get the number of same-class training pairs
getNumPosPairs() - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Get the number of same-class training pairs
getNumPosPairs() - Method in class weka.deduping.metrics.SumInstanceMetric
Get the number of same-class training pairs
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.
getNumStringParts() - Method in class weka.deduping.metrics.KernelVSMetric
the number of string parts
getNumSubCmtys() - Method in class weka.classifiers.meta.MultiBoostAB
Get the number of sub committees to use
getNumSymbols() - Method in class weka.classifiers.bayes.DiscreteEstimatorBayes
Gets the number of symbols this estimator operates with
getNumSymbols() - Method in class weka.estimators.DiscreteEstimator
Gets the number of symbols this estimator operates with
getNumToSelect() - Method in class weka.attributeSelection.ForwardSelection
Gets the number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.RaceSearch
Gets the number of attributes to be retained.
getNumToSelect() - Method in interface weka.attributeSelection.RankedOutputSearch
Gets the user specified number of attributes to be retained.
getNumToSelect() - Method in class weka.attributeSelection.Ranker
Gets the number of attributes to be retained.
getNumTraining() - Method in class weka.classifiers.lazy.IBk
Get the number of training instances the classifier is currently using
getNumTraining() - Method in class weka.classifiers.sparse.IBkMetric
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.
getNumeric() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Check if new attribute is to be numeric.
getObjFunConvergenceDifference() - Method in class weka.clusterers.MPCKMeans
Get the minimum value of the objective function difference required for convergence
getObjFunConvergenceDifference() - Method in class weka.clusterers.PCKMeans
Get the minimum value of the objective function difference required for convergence
getObjFunConvergenceDifference() - Method in class weka.clusterers.PCSoftKMeans
Get the minimum value of the objective function difference required for convergence
getObjFunConvergenceDifference() - Method in class weka.clusterers.SeededKMeans
Get the minimum value of the objective function difference required for convergence
getObject() - Method in class weka.core.SerializedObject
Returns a serialized object.
getObjective() - Method in class weka.attributeSelection.GeneticSearch.GABitSet
gets the objective merit
getObjectiveFunction() - Method in class weka.attributeSelection.MatlabNMF
Gets the objective function.
getOnDemandDirectory() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.classifiers.meta.MetaCost
Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns the directory that will be searched for cost files when loading on demand.
getOptimizations() - Method in class weka.classifiers.rules.JRip
 
getOptimizeBins() - Method in class weka.classifiers.meta.RegressionByDiscretization
Gets whether the discretizer optimizes the number of bins
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 class weka.associations.Apriori
Gets the current settings of the Apriori object.
getOptions() - Method in class weka.attributeSelection.BestFirst
Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.CfsSubsetEval
Gets the current settings of CfsSubsetEval
getOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval
Gets the current settings of ClassifierSubsetEval
getOptions() - Method in class weka.attributeSelection.ExhaustiveSearch
Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.ForwardSelection
Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.GeneticSearch
Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.MatlabICA
Gets the current settings of MatlabICA
getOptions() - Method in class weka.attributeSelection.MatlabNMF
Gets the current settings of MatlabNMF
getOptions() - Method in class weka.attributeSelection.MatlabPCA
Gets the current settings of MatlabPCA
getOptions() - Method in class weka.attributeSelection.PrincipalComponents
Gets the current settings of PrincipalComponents
getOptions() - Method in class weka.attributeSelection.RaceSearch
Gets the current settings of BestFirst.
getOptions() - Method in class weka.attributeSelection.RandomSearch
Gets the current settings of RandomSearch.
getOptions() - Method in class weka.attributeSelection.RankSearch
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.ReliefFAttributeEval
Gets the current settings of ReliefFAttributeEval.
getOptions() - Method in class weka.attributeSelection.SVMAttributeEval
Gets the current settings of SVMAttributeEval
getOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.attributeSelection.WrapperSubsetEval
Gets the current settings of WrapperSubsetEval.
getOptions() - Method in class weka.classifiers.BVDecompose
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.CheckClassifier
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.RegressionBVDecompose
Gets the current settings of the CheckClassifier.
getOptions() - Method in class weka.classifiers.bayes.BayesNet
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.BayesNetK2
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.NaiveBayesSimple
Gets the current settings.
getOptions() - Method in class weka.classifiers.bayes.SemiSupEM
Gets the current settings of EM.
getOptions() - Method in class weka.classifiers.functions.LeastMedSq
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.classifiers.functions.LinearRegression
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.Logistic
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.SMO
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.VotedPerceptron
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.Winnow
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.functions.neural.NeuralNetwork
Gets the current settings of NeuralNet.
getOptions() - Method in class weka.classifiers.functions.pace.PaceRegression
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.lazy.IBk
Gets the current settings of IBk.
getOptions() - Method in class weka.classifiers.lazy.LWR
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.lazy.kstar.KStar
Gets the current settings of K*.
getOptions() - Method in class weka.classifiers.meta.ActiveDecorate
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AdaBoostM1
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AdditiveRegression
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Bagging
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.CVParameterSelection
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.ClassificationViaRegression
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.Crate
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.DEC
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.DistributionMetaClassifier
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.meta.Fable
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.FilteredClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.LogitBoost
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MetaCost
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiBoostAB
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiClassClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.MultiScheme
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.QBag
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.QBoost
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.RegressionByDiscretization
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.SemiSupDecorate
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.Stacking
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.meta.ThresholdSelector
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.misc.Prototype
Gets the current settings.
getOptions() - Method in class weka.classifiers.misc.PrototypeMetric
Gets the current settings.
getOptions() - Method in class weka.classifiers.misc.VFI
Gets the current settings of VFI
getOptions() - Method in class weka.classifiers.rules.ConjunctiveRule
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.DecisionTable
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.rules.JRip
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.OneR
Gets the current settings of the OneR classifier.
getOptions() - Method in class weka.classifiers.rules.Ridor
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.rules.part.PART
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.sparse.IBkMetric
Gets the current settings of IBkMetric.
getOptions() - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Gets the current settings of NaiveBayesSimpleSparse.
getOptions() - Method in class weka.classifiers.sparse.SVMlight
Gets the current settings
getOptions() - Method in class weka.classifiers.trees.REPTree
Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.RandomForest
Gets the current settings of the forest.
getOptions() - Method in class weka.classifiers.trees.RandomTree
Gets options from this classifier.
getOptions() - Method in class weka.classifiers.trees.adtree.ADTree
Gets the current settings of ADTree.
getOptions() - Method in class weka.classifiers.trees.j48.J48
Gets the current settings of the Classifier.
getOptions() - Method in class weka.classifiers.trees.m5.M5Base
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.trees.m5.M5P
Gets the current settings of the classifier.
getOptions() - Method in class weka.clusterers.Cobweb
Gets the current settings of Cobweb.
getOptions() - Method in class weka.clusterers.DistributionMetaClusterer
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.HAC
Gets the current settings of Greedy Agglomerative Clustering
getOptions() - Method in class weka.clusterers.MPCKMeans
 
getOptions() - Method in class weka.clusterers.PCKMeans
 
getOptions() - Method in class weka.clusterers.PCSoftKMeans
 
getOptions() - Method in class weka.clusterers.SeededKMeans
 
getOptions() - Method in class weka.clusterers.SimpleKMeans
Gets the current settings of SimpleKMeans
getOptions() - Method in class weka.clusterers.XMeans
Gets the current settings of SimpleKMeans.
getOptions() - Method in class weka.clusterers.assigners.LPAssigner
 
getOptions() - Method in class weka.clusterers.assigners.RMNAssigner
 
getOptions() - Method in class weka.clusterers.assigners.RandomAssigner
 
getOptions() - Method in class weka.clusterers.assigners.SimpleAssigner
 
getOptions() - Method in class weka.clusterers.assigners.SortedAssigner
 
getOptions() - Method in class weka.core.KDTree
Gets the current settings of KDtree.
getOptions() - Method in interface weka.core.OptionHandler
Gets the current option settings for the OptionHandler.
getOptions() - Method in class weka.core.metrics.AttrEvalMetricLearner
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.core.metrics.BarHillelMetric
Gets the current settings of WeightedEuclideanP.
getOptions() - Method in class weka.core.metrics.BarHillelMetricMatlab
Gets the current settings of WeightedEuclideanP.
getOptions() - Method in class weka.core.metrics.ClassifierMetricLearner
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.core.metrics.GDMetricLearner
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.core.metrics.HardPairwiseSelector
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.core.metrics.KL
Gets the current settings of KLP.
getOptions() - Method in class weka.core.metrics.RandomPairwiseSelector
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.core.metrics.WeightedDotP
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.core.metrics.WeightedEuclidean
Gets the current settings of WeightedEuclideanP.
getOptions() - Method in class weka.core.metrics.WeightedMahalanobis
Gets the current settings of WeightedMahalanobisP.
getOptions() - Method in class weka.core.metrics.XingMetric
Gets the current settings of WeightedEuclideanP.
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.datagenerators.TextSource
 
getOptions() - Method in class weka.deduping.BasicDeduper
Gets the current settings of Greedy Agglomerative Clustering
getOptions() - Method in class weka.deduping.PairwiseSelector
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.deduping.blocking.Blocking
Gets the current settings of Blocking
getOptions() - Method in class weka.deduping.metrics.AffineMetric
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.deduping.metrics.AffineProbMetric
Gets the current settings of WeightedDotP.
getOptions() - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Gets the current settings of Greedy Agglomerative Clustering
getOptions() - Method in class weka.deduping.metrics.JaccardMetric
Gets the current settings of NGramTokenizer.
getOptions() - Method in class weka.deduping.metrics.KernelVSMetric
Gets the current settings of NGramTokenizer.
getOptions() - Method in class weka.deduping.metrics.NGramTokenizer
Gets the current settings of NGramTokenizer.
getOptions() - Method in class weka.deduping.metrics.SumInstanceMetric
Gets the current settings of Greedy Agglomerative Clustering
getOptions() - Method in class weka.deduping.metrics.VectorSpaceMetric
Gets the current settings of NGramTokenizer.
getOptions() - Method in class weka.deduping.metrics.WordTokenizer
Gets the current settings of WordTokenizer.
getOptions() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.AveragingResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.CSVResultListener
Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.CrossValidationResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.DatabaseResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.DeduperSplitEvaluator
Gets the current settings of the Deduper.
getOptions() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.Experiment
Gets the current settings of the experiment iterator.
getOptions() - Method in class weka.experiment.ExtractionResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.ExtractionSplitEvaluator
Gets the current settings of the Extractor.
getOptions() - Method in class weka.experiment.InstanceQuery
Gets the current settings of InstanceQuery
getOptions() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.LearningRateResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.PairedTTester
Gets current settings of the PairedTTester.
getOptions() - Method in class weka.experiment.RandomSplitResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the current settings of the Classifier.
getOptions() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the current settings of the Clusterer.
getOptions() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets the current settings of the result producer.
getOptions() - Method in class weka.extraction.ClusteringExtractor
Gets the current settings of Greedy Agglomerative Clustering
getOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection
Gets the current settings for the attribute selection (search, evaluator) etc.
getOptions() - Method in class weka.filters.supervised.attribute.ClassOrder
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.Discretize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.Resample
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.SpreadSubsample
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Add
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Copy
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.NumericTransform
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.Remove
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Randomize
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Gets the current settings of the filter.
getOptions() - Method in class weka.filters.unsupervised.instance.Resample
Gets the current settings of the filter.
getOrderedFlag() - Method in class weka.datagenerators.BIRCHCluster
Gets the ordered flag (option O).
getOutput() - Method in class weka.datagenerators.ClusterGenerator
Gets the print writer.
getOutput() - Method in class weka.datagenerators.Generator
Gets the print writer.
getOutputCenterFile() - Method in class weka.clusterers.XMeans
Gets the name of the file to write the list of centers to.
getOutputFile() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get the value of OutputFile.
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.DedupingPRCurveCVResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.ExtractionResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.RandomSplitResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of OutputFile.
getOutputFile() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the value of OutputFile.
getOutputFormat() - Method in class weka.filters.Filter
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.
getOutputStringIndex() - Method in class weka.filters.Filter
Returns an array containing the indices of all string attributes in the output format.
getOutputWordCounts() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Gets whether output instances contain 0 or 1 indicating word presence, or word counts.
getOutputs() - Method in class weka.classifiers.functions.neural.NeuralConnection
Use this to get easy access to the outputs.
getPairs(Instances, int) - Static method in class weka.clusterers.InstancePair
Returns an arraylist of random (both positive and negative) pair objects created from the input
getPairs(Instances, int, boolean) - Static method in class weka.clusterers.InstancePair
Returns an arraylist of pair objects created from the input set of instances
getParent(int) - Method in class weka.gui.treevisualizer.Node
Get the parent edge.
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.
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
getPercentage() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Gets the percentage of instances to select.
getPhaseTwoRandom() - Method in class weka.clusterers.MPCKMeans
Return m_PhaseTwoRandom
getPhaseTwoRandom() - Method in class weka.clusterers.PCKMeans
Return m_PhaseTwoRandom
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
getPlotPoints() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.ExtractionResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of PlotPoints.
getPlotPoints() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the value of PlotPoints.
getPlots() - Method in class weka.gui.visualize.Plot2D
Return the list of plots
getPointValue(int) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Gets a particular point value
getPointValues() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Gets all point values
getPoints() - Method in class weka.experiment.PairedTTester
Get the points on the learning curve
getPopulationSize() - Method in class weka.attributeSelection.GeneticSearch
get the size of the population
getPosNegDiffInstanceRatio() - Method in class weka.core.metrics.LearnableMetric
Get the ratio of positive and negative instances to be used for training
getPosStringMode() - Method in class weka.deduping.PairwiseSelector
return the selection mode for positive string examples
getPositivesMode() - Method in class weka.core.metrics.HardPairwiseSelector
return the selection mode for positives
getPositivesMode() - Method in class weka.deduping.PairwiseSelector
return the selection mode for positives
getPrecision() - Method in class weka.classifiers.evaluation.TwoClassStats
Calculate the precision.
getPrecision() - Method in class weka.classifiers.functions.Logistic
Gets the precision of stopping criterion in Newton method.
getPrecision() - Method in class weka.experiment.PairedTTester
Get the value of m_Precision.
getPrediction(DistributionClassifier, Instance) - Method in class weka.classifiers.evaluation.EvaluationUtils
Generate a single prediction for a test instance given the pre-trained classifier.
getPredictions(Instances, int, int) - Method in class weka.classifiers.meta.ThresholdSelector
Collects the classifier predictions using the specified evaluation method.
getProbability(double) - Method in class weka.classifiers.bayes.DiscreteEstimatorBayes
Get a probability estimate for a value
getProbability(double, double) - Method in interface weka.estimators.ConditionalEstimator
Get a probability for a value conditional on another value
getProbability(double, double) - Method in class weka.estimators.DDConditionalEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.DKConditionalEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.DNConditionalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.DiscreteEstimator
Get a probability estimate for a value
getProbability(double) - Method in interface weka.estimators.Estimator
Get a probability estimate for a value.
getProbability(double, double) - Method in class weka.estimators.KDConditionalEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.KKConditionalEstimator
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.MahalanobisEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NDConditionalEstimator
Get a probability estimate for a value
getProbability(double, double) - Method in class weka.estimators.NNConditionalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.NormalEstimator
Get a probability estimate for a value
getProbability(double) - Method in class weka.estimators.PoissonEstimator
Get a probability estimate for a value
getProduceLatex() - Method in class weka.experiment.PairedTTester
Get whether latex is output
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.StripChartBeanInfo
Get the property descriptors for this bean
getPropertyDescriptors() - Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo
Get the property descriptors for this bean
getPropertyPath() - Method in class weka.experiment.Experiment
Gets the path of properties taken to get to the custom property to iterate over.
getPrune() - Method in class weka.core.KDTree
Gets the pruning flag.
getPruningType() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the pruning type
getPseudoCountDirichlet() - Method in class weka.core.metrics.KL
Get the pseudo-count value for Dirichlet smoothing
getQuery() - Method in class weka.experiment.InstanceQuery
Get the query to execute against the database
getROCArea(Instances) - Static method in class weka.classifiers.evaluation.ThresholdCurve
Calculates the area under the ROC curve.
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(int) - Method in class weka.classifiers.trees.adtree.ADTree
Gets the next random value.
getRandom() - Method in class weka.datagenerators.BIRCHCluster
Gets the random generator.
getRandom() - Method in class weka.datagenerators.RDG1
Gets the random generator.
getRandomOrder() - Method in class weka.classifiers.bayes.BayesNetK2
Get random order flag
getRandomSeed() - Method in class weka.classifiers.functions.LeastMedSq
get the seed for the random number generator
getRandomSeed() - Method in class weka.classifiers.functions.SMO
Get the value of randomSeed.
getRandomSeed() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getRandomSeed() - Method in class weka.classifiers.trees.adtree.ADTree
Gets random seed for a random walk.
getRandomSeed() - Method in class weka.clusterers.HAC
Return the random number seed
getRandomSeed() - Method in class weka.clusterers.MPCKMeans
Return the random number seed
getRandomSeed() - Method in class weka.clusterers.PCKMeans
Return the random number seed
getRandomSeed() - Method in class weka.clusterers.PCSoftKMeans
Return the random number seed
getRandomSeed() - Method in class weka.clusterers.SeededKMeans
Return the random number seed
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.instance.Randomize
Get the random number generator seed value.
getRandomSeed() - Method in class weka.filters.unsupervised.instance.Resample
Gets the random number seed.
getRandomSize() - Method in class weka.classifiers.meta.DEC
Number of random instances to add at each iteration.
getRandomSize() - Method in class weka.classifiers.meta.SemiSupDecorate
Number of random instances to add at each iteration.
getRandomWidthFactor() - Method in class weka.classifiers.meta.MultiClassClassifier
Gets the multiplier when generating random codes.
getRandomizeData() - Method in class weka.experiment.RandomSplitResultProducer
Get if dataset is to be randomized
getRandomized() - Method in class weka.classifiers.rules.ConjunctiveRule
 
getRangeCorrection() - Method in class weka.classifiers.meta.ThresholdSelector
Gets the confidence range correction mode used.
getRanges() - Method in class weka.core.Instances
Method to get the ranges.
getRanges() - Method in class weka.core.Range
Gets the string representing the selected range of values
getRank() - Method in class weka.attributeSelection.MatlabNMF
Gets the rank of the basis matrix W.
getRawOutput() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.CrossValidationResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.ExtractionResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
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
getRawOutput() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get if raw split evaluator output is to be saved
getRawOutput() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get if raw split evaluator output is to be saved
getRawResultOutput() - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the raw output from the classifier
getRawResultOutput() - Method in class weka.experiment.DeduperSplitEvaluator
Gets the raw output from the deduper
getRawResultOutput() - Method in class weka.experiment.ExtractionSplitEvaluator
Gets the raw output from the extractor
getRawResultOutput() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the raw output from the classifier
getRawResultOutput() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the raw output from the clusterer
getRawResultOutput() - Method in interface weka.experiment.SplitEvaluator
Returns the raw output for the most recent call to getResult.
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.PART
Get the value of reducedErrorPruning.
getReducedErrorPruning() - Method in class weka.classifiers.trees.j48.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.ClusterGenerator
Gets the relation name the dataset should have.
getRelationName() - Method in class weka.datagenerators.Generator
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
getRemoveInconsistentExamples() - Method in class weka.classifiers.sparse.SVMlight
Get whether the inconsistent examples are removed and retraining follows
getReportFrequency() - Method in class weka.attributeSelection.GeneticSearch
get how often repports are generated
getReset() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getReset() - Method in class weka.gui.beans.ChartEvent
get the value of the reset flag
getResult(Instances, Instances) - Method in class weka.experiment.ClassifierSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.DeduperSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances, Instances) - Method in class weka.experiment.EnsembleClassifierSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.EnsembleClassifierSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.ExtractionSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.RegressionSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances, Instances) - Method in class weka.experiment.SemiSupClassifierSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Dummy function, exists just for compatibility with SplitEvaluator interface
getResult(ArrayList, Instances, Instances, Instances, Instances) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances, Instances, int) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances, Instances, Instances, int) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances, Instances, int, int) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the results for the supplied train and test datasets.
getResult(Instances, Instances, Instances) - Method in interface weka.experiment.SemiSupSplitEvaluator
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.
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 class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Gets the names of each of the 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.CostSensitiveClassifierSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.CrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.DatabaseResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.DeduperSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.EnsembleClassifierSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.ExtractionResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.ExtractionSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Gets the names of each of the 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.NoiseCurveCrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.RandomSplitResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in interface weka.experiment.ResultProducer
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultNames() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets the names of each of the columns produced for a single run.
getResultNames() - Method in interface weka.experiment.SplitEvaluator
Gets the names of each of the result columns produced for a single run.
getResultProducer() - Method in class weka.experiment.AveragingResultProducer
Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.DatabaseResultProducer
Get the ResultProducer.
getResultProducer() - Method in class weka.experiment.Experiment
Get the result producer used for the current experiment.
getResultProducer() - Method in class weka.experiment.LearningRateResultProducer
Get the ResultProducer.
getResultSet() - Method in class weka.experiment.DatabaseUtils
Gets the results generated by a previous query.
getResultTypes() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Gets the data types of each of the 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.CostSensitiveClassifierSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.CrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DatabaseResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DeduperSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.EnsembleClassifierSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.ExtractionResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.ExtractionSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Gets the data types of each of the 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.NoiseCurveCrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.RandomSplitResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.RegressionSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in interface weka.experiment.ResultProducer
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Gets the data types of each of the result columns produced for a single run.
getResultTypes() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets the data types of each of the columns produced for a single run.
getResultTypes() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets the data types of each of the 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.
getResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseUtils
Gets the name of the experiment table that stores results from a particular ResultProducer.
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.
getRetrieval() - Method in class weka.core.converters.AbstractLoader
Gets the retrieval mode.
getRidge() - Method in class weka.classifiers.functions.LinearRegression
Get the value of Ridge.
getRidge() - Method in class weka.classifiers.functions.Logistic
Gets the ridge parameter.
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.
getRuleStats(int) - Method in class weka.classifiers.rules.JRip
Get the statistics of the ruleset in the given position
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
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.
getS() - Method in class weka.classifiers.sparse.SVMlight
Get parameter s in sigmoid/polynomial kernel
getSIndex() - Method in class weka.gui.visualize.VisualizePanel
Get the index of the shape selected for creating splits.
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.classifiers.trees.adtree.ADTree
Gets whether the tree is to save instance data.
getSaveInstanceData() - Method in class weka.classifiers.trees.j48.J48
Check whether instance data is to be saved.
getSaveInstanceData() - Method in class weka.clusterers.Cobweb
Get the value of saveInstances.
getSaveInstances() - Method in class weka.classifiers.trees.m5.M5P
Get whether instance data is being save.
getScoreType() - Method in class weka.classifiers.bayes.BayesNet
Method declaration
getSearch() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the search method used
getSearch() - Method in class weka.filters.supervised.attribute.AttributeSelection
Get the name of the search method
getSearchPath() - Method in class weka.classifiers.trees.adtree.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
getSearchSpec() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Gets the search specification string, which contains the class name of the search method and any options to it
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.attributeSelection.GeneticSearch
get the value of the random number generator's seed
getSeed() - Method in class weka.attributeSelection.ReliefFAttributeEval
Get the seed used for randomly sampling instances.
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.RegressionBVDecompose
Gets the random number seed
getSeed() - Method in class weka.classifiers.bayes.SemiSupEM
Get the random number seed
getSeed() - Method in class weka.classifiers.evaluation.EvaluationUtils
Gets the seed for randomization during cross-validation
getSeed() - Method in class weka.classifiers.functions.VotedPerceptron
Get the value of Seed.
getSeed() - Method in class weka.classifiers.functions.Winnow
Get the value of Seed.
getSeed() - Method in class weka.classifiers.meta.ActiveDecorate
Gets the seed for the random number generator.
getSeed() - Method in class weka.classifiers.meta.AdaBoostM1
Get seed for resampling.
getSeed() - Method in class weka.classifiers.meta.Bagging
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.meta.CVParameterSelection
Gets the random number seed.
getSeed() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Get seed for resampling.
getSeed() - Method in class weka.classifiers.meta.Crate
Gets the seed for the random number generator.
getSeed() - Method in class weka.classifiers.meta.DEC
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.meta.Decorate
Gets the seed for the random number generator.
getSeed() - Method in class weka.classifiers.meta.Fable
Gets the seed for the random number generator.
getSeed() - Method in class weka.classifiers.meta.LogitBoost
Get seed for resampling.
getSeed() - Method in class weka.classifiers.meta.MetaCost
Get seed for resampling.
getSeed() - Method in class weka.classifiers.meta.MultiBoostAB
Get seed for resampling.
getSeed() - Method in class weka.classifiers.meta.MultiClassClassifier
Gets the random number seed.
getSeed() - Method in class weka.classifiers.meta.MultiScheme
Gets the random number seed.
getSeed() - Method in class weka.classifiers.meta.QBag
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.meta.QBoost
Get seed for resampling.
getSeed() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get seed for resampling.
getSeed() - Method in class weka.classifiers.meta.SemiSupDecorate
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.meta.Stacking
Gets the random number seed.
getSeed() - Method in class weka.classifiers.meta.ThresholdSelector
Gets the random number seed.
getSeed() - Method in class weka.classifiers.rules.ConjunctiveRule
 
getSeed() - Method in class weka.classifiers.rules.JRip
 
getSeed() - Method in class weka.classifiers.rules.Ridor
 
getSeed() - Method in class weka.classifiers.trees.REPTree
Get the value of Seed.
getSeed() - Method in class weka.classifiers.trees.RandomForest
Gets the seed for the random number generations
getSeed() - Method in class weka.classifiers.trees.RandomTree
Gets the seed for the random number generations
getSeed() - Method in class weka.clusterers.EM
Get the random number seed
getSeed() - Method in class weka.clusterers.FarthestFirst
Get the random number seed
getSeed() - Method in class weka.clusterers.SimpleKMeans
Get the random number seed
getSeed() - Method in class weka.clusterers.XMeans
Gets the random number seed.
getSeed() - Method in interface weka.core.Randomizable
Gets the seed for the random number generations
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.gui.beans.CrossValidationFoldMaker
Get the currently set seed
getSeed() - Method in class weka.gui.beans.TrainTestSplitMaker
Get the value of the random seed
getSeedHash() - Method in class weka.clusterers.HAC
returns the SeedHash
getSeedUnseenClasses() - Method in class weka.classifiers.bayes.SemiSupEM
 
getSeedable() - Method in class weka.clusterers.HAC
Turn seeding on and off
getSeedable() - Method in class weka.clusterers.MPCKMeans
Is seeding performed?
getSeedable() - Method in class weka.clusterers.PCKMeans
Is seeding performed?
getSeedable() - Method in class weka.clusterers.PCSoftKMeans
Is seeding performed?
getSeedable() - Method in class weka.clusterers.SeededKMeans
Turn seeding on and off
getSeedingMethod() - Method in class weka.clusterers.SeededKMeans
Get the seeding method used.
getSeeds() - Method in class weka.clusterers.Seeder
Returns a hashMap with the instance to cluster assignment mapping for the current seeds
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.AttributeSelectionPanel
Gets the selection model used by the table.
getSelectionModel() - Method in class weka.gui.ResultHistoryPanel
Gets the selection model used by the results list.
getSelectionScheme() - Method in class weka.classifiers.meta.ActiveDecorate
Get the value of m_SelectionScheme.
getSelectionScheme() - Method in class weka.classifiers.meta.Fable
Get the value of m_SelectionScheme.
getSelectionThreshold() - Method in class weka.attributeSelection.RaceSearch
Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getSelector() - Method in class weka.core.metrics.ClassifierMetricLearner
Get the pairwise selector
getSelector() - Method in class weka.core.metrics.GDMetricLearner
Get the pairwise selector
getSelector() - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Get the pairwise selector for this metric
getSelector() - Method in class weka.deduping.metrics.SumInstanceMetric
Get the pairwise selector for this metric
getSeparateTrainingFile() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Get the value of separate training file
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.BatchClassifierEvent
Get the set number (ie which fold this is)
getSetNumber() - Method in class weka.gui.beans.TestSetEvent
Get the test set number (eg.
getSetNumber() - Method in class weka.gui.beans.TrainingSetEvent
Get the set number (eg.
getShape() - Method in class weka.gui.treevisualizer.Node
Get the value of shape.
getShapes() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
 
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.associations.Apriori
Get the value of significanceLevel.
getSignificanceLevel() - Method in class weka.attributeSelection.RaceSearch
Get the significance level
getSignificanceLevel() - Method in class weka.experiment.PairedTTester
Get the value of SignificanceLevel.
getSimilarity(Instance, Instance) - Method in class weka.core.metrics.AttrEvalMetricLearner
Use the Classifier for an estimation of similarity
getSimilarity(Instance, Instance) - Method in class weka.core.metrics.ClassifierMetricLearner
Use the Classifier for an estimation of similarity
getSimilarity(Instance, Instance) - Method in class weka.core.metrics.GDMetricLearner
Use the Classifier for an estimation of similarity
getSimilarity(Instance, Instance) - Method in class weka.core.metrics.MatlabMetricLearner
Use Matlab for an estimation of similarity
getSimilarity(Instance, Instance) - Method in class weka.core.metrics.MetricLearner
Use the metricLearner's internal model for an estimation of similarity, e.g.
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
getSineFlag() - Method in class weka.datagenerators.BIRCHCluster
Gets the sine flag (option S).
getSingleModeFlag() - Method in class weka.datagenerators.BIRCHCluster
Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.RDG1
Gets the single mode flag.
getSingleModeFlag() - Method in class weka.datagenerators.TextSource
 
getSinglePass() - Method in class weka.clusterers.assigners.RMNAssigner
 
getSizeOfBranch() - Method in class weka.classifiers.rules.part.ClassifierDecList
Returns the number of instances covered by a branch
getSmoothing() - Method in class weka.classifiers.trees.m5.Rule
Get whether or not smoothing has been turned on
getSmoothing() - Method in class weka.classifiers.trees.m5.RuleNode
Method declaration
getSmoothingType() - Method in class weka.core.metrics.KL
return the type of smoothing
getSolution() - Method in class weka.clusterers.assigners.LPAssigner
Read the solution from the output file of Octave
getSource() - Method in class weka.gui.beans.BeanConnection
returns the source BeanInstance for this connection
getSource() - Method in class weka.gui.treevisualizer.Edge
Get the value of source.
getSourceEventSetDescriptor() - Method in class weka.gui.beans.BeanConnection
Returns the event set descriptor for the event generated by the source for this connection
getSpaceEquivalents() - Method in class weka.deduping.metrics.NGramTokenizer
Get the haracters that should be treated as spaces
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.ActiveFeatureAcquisitionCVResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.CrossValidationResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.ExtractionResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.RandomSplitResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the SplitEvaluator.
getSplitEvaluator() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the SplitEvaluator.
getSplitPoint() - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Get the split point used for numeric selection
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.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 class weka.attributeSelection.GeneticSearch
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.RandomSearch
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in class weka.attributeSelection.Ranker
Returns a list of attributes (and or attribute ranges) as a String
getStartSet() - Method in interface weka.attributeSelection.StartSetHandler
Returns a list of attributes (and or attribute ranges) as a String
getStaticIcon() - Method in class weka.gui.beans.BeanVisual
Returns the static icon
getStatistics() - Method in class weka.deduping.Deduper
Return the list of statistics collected during deduping
getStatistics() - Method in class weka.extraction.Extractor
Return the list of statistics collected during extraction
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.
getStemming() - Method in class weka.deduping.metrics.Tokenizer
Find out whether stemming is on/off
getStepSize() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Get the value of StepSize.
getStepSize() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get the value of StepSize.
getStepSize() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get the value of StepSize.
getStepSize() - Method in class weka.experiment.LearningRateResultProducer
Get the value of StepSize.
getStepSize() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get the value of StepSize.
getStepSize() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get the value of StepSize.
getStepSize() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get the value of StepSize.
getStepSize() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of StepSize.
getStepSize() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the value of StepSize.
getStopwordRemoval() - Method in class weka.deduping.metrics.Tokenizer
Get whether stopword removal is on or off
getStringIndices(Instances) - Method in class weka.filters.Filter
Gets an array containing the indices of all string attributes.
getStringList(Instances, Instances, int) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
An internal method for creating a list of strings for a particular attribute from two sets of instances: trianing and test data
getStringList(Instances, Instances, int) - Method in class weka.deduping.metrics.SumInstanceMetric
An internal method for creating a list of strings for a particular attribute from two sets of instances: trianing and test data
getStringMetrics() - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Get the baseline string metrics
getStringPairList(Instances, int, int, int, StringMetric) - Method in class weka.deduping.PairwiseSelector
Provide an array of string pairs metric using given training instances
getStructure() - Method in class weka.core.converters.AbstractLoader
 
getStructure() - Method in class weka.core.converters.ArffLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.C45Loader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure() - Method in class weka.core.converters.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.SerializedInstancesLoader
Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getSubCost() - Method in class weka.deduping.metrics.AffineMetric
Get the substitution cost
getSubtreeRaising() - Method in class weka.classifiers.trees.j48.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.core.SelectedTag
Gets the set of all valid Tags.
getTags() - Method in class weka.gui.CostMatrixEditor
Some objects can return tags, but a cost matrix cannot.
getTags() - Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting values as tags.
getTags() - Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting values as tags.
getTags() - Method in class weka.gui.SelectedTagEditor
Gets the list of tags that can be selected from.
getTarget() - Method in class weka.gui.beans.BeanConnection
Returns the target BeanInstance for this connection
getTarget() - Method in class weka.gui.treevisualizer.Edge
Get the value of target.
getTaskResult() - Method in class weka.experiment.TaskStatusInfo
Get the returnable result of this task.
getTempDirPath() - Method in class weka.classifiers.sparse.SVMlight
Get the path for the temporary files
getTestPredictions(DistributionClassifier, 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.BeanVisual
Get the visual's label
getText() - Method in class weka.gui.beans.TextEvent
Describe getText method here.
getTextTitle() - Method in class weka.gui.beans.TextEvent
Describe getTextTitle method here.
getThisClusterer() - Method in class weka.clusterers.HAC
We always want to implement SemiSupClusterer from a class extending Clusterer.
getThisClusterer() - Method in class weka.clusterers.MPCKMeans
We always want to implement SemiSupClusterer from a class extending Clusterer.
getThisClusterer() - Method in class weka.clusterers.PCKMeans
We always want to implement SemiSupClusterer from a class extending Clusterer.
getThisClusterer() - Method in class weka.clusterers.PCSoftKMeans
We always want to implement SemiSupClusterer from a class extending Clusterer.
getThisClusterer() - Method in class weka.clusterers.SeededKMeans
We always want to implement SemiSupClusterer from a class extending Clusterer.
getThisClusterer() - Method in interface weka.clusterers.SemiSupClusterer
We always want to implement SemiSupClusterer from a class extending Clusterer.
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.RaceSearch
Get the threshold
getThreshold() - Method in interface weka.attributeSelection.RankedOutputSearch
Gets the threshold by which attributes can be discarded.
getThreshold() - Method in class weka.attributeSelection.Ranker
Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getThreshold() - Method in class weka.attributeSelection.WrapperSubsetEval
Get the value of the threshold
getThreshold() - Method in class weka.classifiers.functions.Winnow
Get the value of Threshold.
getThreshold() - Method in class weka.classifiers.functions.pace.PaceRegression
Gets the threshold for olsc estimator
getThreshold() - Method in class weka.classifiers.meta.DEC
Get the value of threshold.
getThreshold() - Method in class weka.classifiers.meta.SemiSupDecorate
Get the value of threshold.
getThreshold() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Gets the threshold for the max error when predicting a numeric class.
getThresholdInstance(Instances, double) - Static method in class weka.classifiers.evaluation.ThresholdCurve
Gets the index of the instance with the closest threshold value to the desired target
getTimeStamp() - Static method in class weka.clusterers.MPCKMeans
Gets a Double representing the current date and time.
getTimeStamp() - Static method in class weka.clusterers.PCKMeans
Gets a Double representing the current date and time.
getTimeStamp() - Static method in class weka.clusterers.PCSoftKMeans
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.core.metrics.AttrEvalMetricLearner
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.core.metrics.ClassifierMetricLearner
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.core.metrics.GDMetricLearner
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.core.metrics.MatlabMetricLearner
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.deduping.blocking.Blocking
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.deduping.metrics.ClassifierInstanceMetric
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.deduping.metrics.SumInstanceMetric
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.ActiveLearningCurveCVResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.CrossValidationResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.ExtractionResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.LearningCurveCrossValidationResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.NoiseCurveCrossValidationResultProducer
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.
getTimestamp() - Static method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets a Double representing the current date and time.
getTimestamp() - Static method in class weka.gui.LogPanel
Gets a string containing current date and time.
getTimestamp() - Static method in class weka.gui.SysErrLog
Gets a string containing current date and time.
getToken(StreamTokenizer) - Static method in class weka.core.converters.ConverterUtils
Gets token.
getTokenizer() - Method in class weka.deduping.blocking.Blocking
Get the tokenizer to use
getTokenizer() - Method in class weka.deduping.metrics.JaccardMetric
Get the tokenizer to use
getTokenizer() - Method in class weka.deduping.metrics.KernelVSMetric
Get the tokenizer to use
getTokenizer() - Method in class weka.deduping.metrics.VectorSpaceMetric
Get the tokenizer to use
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.
getToolTipText(MouseEvent) - Method in class weka.gui.AttributeVisualizationPanel
Returns " []" in case 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.
getTrainIterations() - Method in class weka.classifiers.BVDecompose
Gets the maximum number of boost iterations
getTrainIterations() - Method in class weka.classifiers.RegressionBVDecompose
Gets the maximum number of boost iterations
getTrainPercent() - Method in class weka.experiment.RandomSplitResultProducer
Get the value of TrainPercent.
getTrainPercent() - Method in class weka.gui.beans.TrainTestSplitMaker
Get the percentage of the data that will be in the training portion of the split
getTrainPoolSize() - Method in class weka.classifiers.BVDecompose
Get the number of instances in the training pool.
getTrainPoolSize() - Method in class weka.classifiers.RegressionBVDecompose
Get the number of instances in the training pool.
getTrainProportion() - Method in class weka.deduping.BasicDeduper
Get the amount of training
getTrainTestPredictions(DistributionClassifier, 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.
getTrainable() - Method in class weka.clusterers.MPCKMeans
Is metric learning performed?
getTrainable() - Method in class weka.core.metrics.LearnableMetric
Get the value of metricTraining
getTrainingFold(ArrayList, int) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Given a list of folds, merge together all but the test fold with the specified index and return the resulting training fold
getTrainingFold(ArrayList, int) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Given a list of folds, merge together all but the test fold with the specified index and return the resulting training fold
getTrainingFold(ArrayList, int) - Method in class weka.experiment.ExtractionResultProducer
Given a list of folds, merge together all except for the test fold with the specified index and return the resulting training fold
getTrainingSet() - Method in class weka.gui.beans.TrainingSetEvent
Get the training instances
getTrainingTime() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getTransformBackToOriginal() - Method in class weka.attributeSelection.MatlabICA
Gets whether the data is to be transformed back to the original space.
getTransformBackToOriginal() - Method in class weka.attributeSelection.MatlabPCA
Gets whether the data is to be transformed back to the original space.
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.classifiers.functions.neural.NeuralConnection
 
getU() - Method in class weka.core.Matrix
Returns the U part of the matrix.
getUniquePairs(TreeSet, Metric, int) - Method in class weka.core.metrics.HardPairwiseSelector
This helper method goes through a TreeSet containing sorted TrainingPairs and returns a list of unique pairs
getUnpruned() - Method in class weka.classifiers.rules.part.PART
Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.j48.J48
Get the value of unpruned.
getUnpruned() - Method in class weka.classifiers.trees.m5.M5Base
Get whether unpruned tree/rules are being generated
getUnpruned() - Method in class weka.classifiers.trees.m5.Rule
Get whether unpruned tree/rules are being generated
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.ActiveFeatureAcquisitionCVResultProducer
Get the value of UpperSize.
getUpperSize() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get the value of UpperSize.
getUpperSize() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get the value of UpperSize.
getUpperSize() - Method in class weka.experiment.LearningRateResultProducer
Get the value of UpperSize.
getUpperSize() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get the value of UpperSize.
getUpperSize() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Get the value of UpperSize.
getUpperSize() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Get the value of UpperSize.
getUpperSize() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of UpperSize.
getUpperSize() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Get the value of UpperSize.
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.
getUseBlocking() - Method in class weka.deduping.BasicDeduper
See whether blocking is on/off
getUseCombinedObjectiveFunction() - Method in class weka.clusterers.MPCKMeans
Is combined objective function being used
getUseDITCSmoothing() - Method in class weka.core.metrics.KL
Check whether DITC smoothing is used
getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.Discretize
Get the value of UseEqualFrequency.
getUseEqualFrequency() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Get the value of UseEqualFrequency.
getUseFalseImplicitNegatives() - Method in class weka.deduping.PairwiseSelector
Check whether using false implicit negatives is on/off
getUseGenerativeModel() - Method in class weka.deduping.metrics.AffineProbMetric
Do we use the generative model or convert back to the additive model?
getUseIBk() - Method in class weka.classifiers.rules.DecisionTable
Gets whether IBk is being used instead of the majority class
getUseIDF() - Method in class weka.deduping.blocking.Blocking
check whether IDF weighting is on/off
getUseIDF() - Method in class weka.deduping.metrics.KernelVSMetric
check whether IDF weighting is on/off
getUseIDF() - Method in class weka.deduping.metrics.VectorSpaceMetric
check whether IDF weighting is on/off
getUseIDivergence() - Method in class weka.core.metrics.KL
Check whether regular KL divergence or I-divergence is used
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.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.
getUseMultipleMetrics() - Method in class weka.clusterers.MPCKMeans
See if individual per-cluster metrics are used
getUseMustLinkPairsOnly() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the value of useMustLinkPairsOnly.
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.
getUseRejectedPositives() - Method in class weka.deduping.PairwiseSelector
Check whether using rejected positives as negatives is on or off
getUseResampling() - Method in class weka.classifiers.meta.AdaBoostM1
Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.LogitBoost
Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.MultiBoostAB
Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.QBoost
Get whether resampling is turned on
getUseResampling() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get whether resampling is turned on
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
getUseWeights() - Method in class weka.classifiers.meta.DEC
Get flag for using weights for committee votes.
getUseWeights() - Method in class weka.classifiers.meta.SemiSupDecorate
Get flag for using weights for committee votes.
getValidationChunkSize() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Get the validation chunk size
getValidationSetSize() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getValidationThreshold() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
getValue() - Method in class weka.classifiers.trees.adtree.PredictionNode
Gets the prediction value of the node.
getValue() - Method in class weka.deduping.metrics.Weight
Get the current count
getValue() - Method in class weka.gui.CostMatrixEditor
Gets the cost matrix that is being edited.
getValue() - Method in class weka.gui.GenericArrayEditor
Gets the current object array.
getValue() - Method in class weka.gui.GenericObjectEditor
Gets the current Object.
getValue() - Method in class weka.gui.HierarchyPropertyParser
Get the value of current node
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
 
getVarbValues() - Method in class weka.core.Optimization
Get the variable values.
getVariance() - Method in class weka.classifiers.BVDecompose
Get the calculated variance
getVariance() - Method in class weka.classifiers.RegressionBVDecompose
Get the calculated variance
getVarianceCovered() - Method in class weka.attributeSelection.MatlabPCA
Gets the proportion of total variance to account for when retaining principal components
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
getVerbose() - Method in class weka.clusterers.HAC
get the verbosity level of the clusterer
getVerbose() - Method in class weka.clusterers.MPCKMeans
get the verbosity level of the clusterer
getVerbose() - Method in class weka.clusterers.PCKMeans
get the verbosity level of the clusterer
getVerbose() - Method in class weka.clusterers.PCSoftKMeans
get the verbosity level of the clusterer
getVerbose() - Method in class weka.clusterers.SeededKMeans
get the verbosity level of the clusterer
getVerbose() - Method in class weka.extraction.ClusteringExtractor
get the verbosity level of the clusterer
getVerbosityLevel() - Method in class weka.classifiers.sparse.SVMlight
Get verbosity level, can be anything between 0 and 3
getVisible() - Method in class weka.gui.treevisualizer.Node
Get the value of visible.
getVisual() - Method in class weka.gui.beans.AbstractDataSource
Get the visual being used by this data source.
getVisual() - Method in class weka.gui.beans.AbstractEvaluator
Get the visual
getVisual() - Method in class weka.gui.beans.AbstractTestSetProducer
Get the visual for this bean
getVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Get the visual for this bean
getVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer
Get the visual for this bean
getVisual() - Method in class weka.gui.beans.ClassAssigner
 
getVisual() - Method in class weka.gui.beans.Classifier
Gets the visual appearance of this wrapper bean
getVisual() - Method in class weka.gui.beans.DataVisualizer
Return the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.Filter
Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.GraphViewer
Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.StripChart
Get the visual appearance of this bean
getVisual() - Method in class weka.gui.beans.TextViewer
Get the visual appearance of this bean
getVisual() - Method in interface weka.gui.beans.Visible
Get the visual representation
getVoteFlag() - Method in class weka.datagenerators.RDG1
Gets the vote flag.
getWeight(String) - Method in class weka.deduping.metrics.HashMapVector
Return the weight of the given token in the vector
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
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
getWeightThreshold() - Method in class weka.classifiers.meta.MultiBoostAB
Get the degree of weight thresholding
getWeightThreshold() - Method in class weka.classifiers.meta.QBoost
Get the degree of weight thresholding
getWeightingKernel() - Method in class weka.classifiers.lazy.LWR
Gets the kernel weighting method to use.
getWeights() - Method in class weka.classifiers.functions.neural.NeuralNode
call this function to get the weights array.
getWeights() - Method in class weka.core.metrics.LearnableMetric
Get the feature weights
getWeights() - Method in class weka.core.metrics.WeightedMahalanobis
override the parent class methods
getWeightsMatrix() - Method in class weka.core.metrics.WeightedMahalanobis
override the parent class methods
getWholeDataErr() - Method in class weka.classifiers.rules.Ridor
 
getWidth() - Method in class weka.classifiers.sparse.SVMlight
Get the epsilon width of tube for regression
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 weka.classifiers.sparse.IBkMetric
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.Classifier
Returns the wrapped classifier
getWrappedAlgorithm() - Method in class weka.gui.beans.Filter
Get the filter wrapped by this bean
getWrappedAlgorithm() - Method in class weka.gui.beans.Loader
Get the loader
getWrappedAlgorithm() - Method in interface weka.gui.beans.WekaWrapper
Get the algorithm
getX() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getX() - Method in class weka.gui.beans.BeanInstance
Gets the x coordinate of this bean
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
getXindex() - Method in class weka.gui.visualize.PlotData2D
Get the currently set x index of the data
getY() - Method in class weka.classifiers.functions.neural.NeuralConnection
 
getY() - Method in class weka.gui.beans.BeanInstance
Gets the y coordinate of this bean
getYIndex() - Method in class weka.gui.visualize.VisualizePanel
Get the index of the attribute on the y axis
getYindex() - Method in class weka.gui.visualize.PlotData2D
Get the currently set y index of the data
getclassNoise() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get if Noise is to be added to Class
getclassNoiseTest() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get if Noise is to be added to Class in Testing Set
getfeatureMiss() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get if Features are to be set Missing
getfeatureMissTest() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get if Features are to be set Missing in Testing Set
getfeatureNoise() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get if Noise to be added in Features
getfeatureNoiseTest() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get if Noise is to be added to Feature in Testing Set
globalInfo() - Method in class weka.associations.Apriori
Returns a string describing this associator
globalInfo() - Method in class weka.attributeSelection.BestFirst
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.CfsSubsetEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.ConsistencySubsetEval
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ExhaustiveSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ForwardSelection
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.GainRatioAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.GeneticSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.MatlabICA
Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.MatlabNMF
Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.MatlabPCA
Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.OneRAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.PrincipalComponents
Returns a string describing this attribute transformer
globalInfo() - Method in class weka.attributeSelection.RaceSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.RandomSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.RankSearch
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.Ranker
Returns a string describing this search method
globalInfo() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.bayes.BayesNetB
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.BayesNetK2
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Returns a string describing this clusterer
globalInfo() - Method in class weka.classifiers.bayes.SemiSupEM
Returns a string describing this clusterer
globalInfo() - Method in class weka.classifiers.functions.neural.NeuralNetwork
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.lazy.LBR
 
globalInfo() - Method in class weka.classifiers.meta.AdditiveRegression
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
globalInfo() - Method in class weka.classifiers.meta.Decorate
Returns a string describing classifier
globalInfo() - Method in class weka.classifiers.meta.MultiClassClassifier
 
globalInfo() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
globalInfo() - Method in class weka.classifiers.meta.ThresholdSelector
 
globalInfo() - Method in class weka.classifiers.misc.Prototype
Returns a string describing this clusterer
globalInfo() - Method in class weka.classifiers.misc.PrototypeMetric
Returns a string describing this clusterer
globalInfo() - Method in class weka.classifiers.misc.VFI
Returns a string describing this search method
globalInfo() - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
 
globalInfo() - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparseSoft
 
globalInfo() - Method in class weka.classifiers.trees.UserClassifier
This will return a string describing the classifier.
globalInfo() - Method in class weka.classifiers.trees.adtree.ADTree
 
globalInfo() - Method in class weka.clusterers.EM
Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.FarthestFirst
Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.SemiSupClustererEvaluation
Returns a string describing this evaluator
globalInfo() - Method in class weka.clusterers.SimpleKMeans
Returns a string describing this clusterer
globalInfo() - Method in class weka.clusterers.XMeans
Returns a string describing this clusterer
globalInfo() - Method in class weka.core.converters.C45Loader
Returns a string describing this attribute evaluator
globalInfo() - Method in class weka.core.converters.CSVLoader
Returns a string describing this attribute evaluator
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.datagenerators.TextSource
 
globalInfo() - Method in class weka.deduping.DedupingEvaluation
Returns a string describing this evaluator
globalInfo() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.AveragingResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.CSVResultListener
Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.CrossValidationResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.DatabaseResultListener
Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.DatabaseResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.DeduperSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.EnsembleClassifierSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.ExtractionResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.ExtractionSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.InstancesResultListener
Returns a string describing this result listener
globalInfo() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.LearningRateResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RandomSplitResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.RegressionSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Returns a string describing this split evaluator
globalInfo() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns a string describing this result producer
globalInfo() - Method in class weka.extraction.ExtractionEvaluation
Returns a string describing this evaluator
globalInfo() - Method in class weka.filters.AllFilter
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.StratifiedRemoveFolds
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Add
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Copy
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
 
globalInfo() - Method in class weka.filters.unsupervised.attribute.Obfuscate
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.Remove
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.NonSparseToSparse
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.RemoveRange
Returns a string describing this filter
globalInfo() - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
Returns a string describing this filter
gnuplot() - Method in class weka.experiment.Grapher
Generate gnuplot files for plotting a learning curve for the current dataset and metric.
gnuplot() - Method in class weka.experiment.NoiseGrapher
Generate gnuplot files for plotting a learning curve for the current dataset and metric.
gnuplotAllDatasets() - Method in class weka.experiment.Grapher
Produce a gnuplot for each dataset in the result file
gnuplotAllDatasets() - Method in class weka.experiment.NoiseGrapher
Produce a gnuplot for each dataset in the result file
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(String) - Method in class weka.gui.HierarchyPropertyParser
Go to one child node from the current position in the tree according to the given value
If the child node with the given value cannot be found it returns false, true otherwise.
goToChild(int) - Method in class weka.gui.HierarchyPropertyParser
Go to one child node from the current position in the tree according to the given position
goToParent() - Method in class weka.gui.HierarchyPropertyParser
Go to the parent from the current position in the tree If the current position is the root, it stays there and does not move
goToRoot() - Method in class weka.gui.HierarchyPropertyParser
Go to the root of the tree
gr(double, double) - Static method in class weka.core.Utils
Tests if a is smaller than b.
grOrEq(double, double) - Static method in class weka.core.Utils
Tests if a is greater or equal to b.
graph() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Returns graph describing the classifier (if possible).
graph() - Method in class weka.classifiers.trees.REPTree
Outputs the decision tree as a graph
graph() - Method in class weka.classifiers.trees.UserClassifier
 
graph() - Method in class weka.classifiers.trees.adtree.ADTree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.j48.J48
Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.m5.M5P
Return a dot style String describing the tree.
graph(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode
Assign a unique identifier to each node in the tree and then calls graphTree
graph() - Method in class weka.clusterers.Cobweb
Generates the graph string of the Cobweb tree
graph() - Method in interface weka.core.Drawable
Returns a string that describes a graph representing the object.
graphTraverse(PredictionNode, StringBuffer, int, int, Instances) - Method in class weka.classifiers.trees.adtree.ADTree
Traverses the tree, graphing each node.
graphTree(StringBuffer) - Method in class weka.classifiers.trees.m5.RuleNode
Return a dotty style string describing the tree
grouping(boolean) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
grow(Instances) - Method in class weka.classifiers.rules.JRip.RipperRule
Build one rule using the growing data
grow(Instances) - Method in class weka.classifiers.rules.Rule
Build this rule

H

HAC - class weka.clusterers.HAC.
 
HAC() - Constructor for class weka.clusterers.HAC
empty constructor, required to call using Class.forName
HAC(Metric) - Constructor for class weka.clusterers.HAC
 
HLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
HardPairwiseSelector - class weka.core.metrics.HardPairwiseSelector.
HardPairwiseSelector class.
HardPairwiseSelector() - Constructor for class weka.core.metrics.HardPairwiseSelector
A default constructor
HardPairwiseSelector.ReverseComparator - class weka.core.metrics.HardPairwiseSelector.ReverseComparator.
We will need this reverse comparator class to get hardest pairs (those with the largest distance
HardPairwiseSelector.ReverseComparator() - Constructor for class weka.core.metrics.HardPairwiseSelector.ReverseComparator
 
HashMapVector - class weka.deduping.metrics.HashMapVector.
A data structure for a term vector for a document stored as a HashMap that maps tokens to Weight's that store the weight of that token in the document.
HashMapVector() - Constructor for class weka.deduping.metrics.HashMapVector
 
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
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
 
HostListPanel - class weka.gui.experiment.HostListPanel.
This panel controls setting a list of hosts for a RemoteExperiment to use.
HostListPanel(RemoteExperiment) - Constructor for class weka.gui.experiment.HostListPanel
Creates the host list panel with the given experiment.
HostListPanel() - Constructor for class weka.gui.experiment.HostListPanel
Create the host list panel initially disabled.
HyperPipes - class weka.classifiers.misc.HyperPipes.
Class implementing a HyperPipe classifier.
HyperPipes() - Constructor for class weka.classifiers.misc.HyperPipes
 
h(double) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of h(x) given the mixture.
h(DoubleVector) - Method in class weka.classifiers.functions.pace.ChisqMixture
Computes the value of h(x) given the mixture, where x is a vector.
h(double) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of h(x) given the mixture.
h(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Computes the value of h(x) given the mixture, where x is a vector.
h1(int, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Constructs single Householder transformation for a column
h2(int, int, double, PaceMatrix, int) - Method in class weka.classifiers.functions.pace.PaceMatrix
Performs single Householder transformation on one column of a matrix
hasAntds() - Method in class weka.classifiers.rules.ConjunctiveRule
Whether this rule has antecedents, i.e.
hasAntds() - Method in class weka.classifiers.rules.JRip.RipperRule
Whether this rule has antecedents, i.e.
hasAntds() - Method in class weka.classifiers.rules.Rule
Whether this rule has antecedents, i.e.
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.
hasZeropoint() - Method in class weka.core.Attribute
Returns whether the attribute has a zeropoint and may be added meaningfully.
hash - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
attribute value hash code
hashCode() - Method in class weka.associations.ItemSet
Produces a hash code for a item set.
hashCode() - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
Calculates a hash code
hashCode() - Method in class weka.classifiers.rules.DecisionTable.hashKey
Calculates a hash code
hashCode() - Method in class weka.clusterers.InstancePair
hashCode
hashCode() - Method in class weka.core.SerializedObject
Returns a hashcode for this object.
hashCode() - Method in class weka.datagenerators.TextSource.Int
 
hashInstances(Instances) - Method in class weka.clusterers.HAC
Create the hashtable from given Instances; keys are numeric indeces, values are actual Instances
hashInstances(Instances) - Method in class weka.deduping.BasicDeduper
Create the hashtable from given Instances; keys are numeric indeces, values are actual Instances
hashMap - Variable in class weka.deduping.metrics.HashMapVector
The HashMap that stores the mapping of tokens to Weight's
haveCommonTokens(String, String) - Static method in class weka.deduping.PairwiseSelector
return true if two strings have commmon tokens
header(int) - Method in class weka.experiment.PairedTTester
Creates a "header" string describing the current resultsets.
header(String) - Method in class weka.experiment.PairedTTester
Creates a "header" string describing the current resultsets.
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.neural.NeuralNetwork
 
highProbLabel(double[]) - Method in class weka.classifiers.meta.DEC
Probabilisticly select class label - (high probability).
highProbLabel(double[]) - Method in class weka.classifiers.meta.SemiSupDecorate
Probabilisticly select class label - (high probability).
historyInput(Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Adds an instance to the history buffer.
historyRightClickPopup(String, int, int) - Method in class weka.gui.explorer.AssociationsPanel
Handles constructing a popup menu with visualization options.
holdOutFileTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
hypot(double, double) - Static method in class weka.classifiers.functions.pace.Maths
sqrt(a^2 + b^2) without under/overflow.
hypot(double, double) - Static method in class weka.core.Matrix
Returns 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 neighbour classifier.
IBk(int) - Constructor for class weka.classifiers.lazy.IBk
IBk classifier.
IBk() - Constructor for class weka.classifiers.lazy.IBk
IB1 classifer.
IBkMetric - class weka.classifiers.sparse.IBkMetric.
K-nearest neighbour classifier specialized for SparseInstance's.
IBkMetric(int) - Constructor for class weka.classifiers.sparse.IBkMetric
IBk classifier.
IBkMetric() - Constructor for class weka.classifiers.sparse.IBkMetric
IB1 classifer.
IBkMetric.NeighborList - class weka.classifiers.sparse.IBkMetric.NeighborList.
 
IBkMetric.NeighborList(int) - Constructor for class weka.classifiers.sparse.IBkMetric.NeighborList
Creates the neighborlist with a desired length
IBkMetric.NeighborNode - class weka.classifiers.sparse.IBkMetric.NeighborNode.
 
IBkMetric.NeighborNode(double, Instance, IBkMetric.NeighborNode) - Constructor for class weka.classifiers.sparse.IBkMetric.NeighborNode
Create a new neighbor node.
IBkMetric.NeighborNode(double, Instance) - Constructor for class weka.classifiers.sparse.IBkMetric.NeighborNode
Create a new neighbor node that doesn't link to any other nodes.
ICON_PATH - Static variable in class weka.gui.beans.BeanVisual
 
IDLE - Static variable in class weka.gui.beans.BeanInstance
 
INCREMENTAL - Static variable in class weka.core.converters.AbstractLoader
 
INITIAL_STEP - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
INPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This unit is an input unit.
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
INVERSE - Static variable in class weka.classifiers.lazy.LWR
 
IN_USE - Static variable in class weka.experiment.RemoteExperiment
 
Id3 - class weka.classifiers.trees.Id3.
Class implementing an Id3 decision tree classifier.
Id3() - Constructor for class weka.classifiers.trees.Id3
 
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
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, Classifier, Instance, int) - Constructor for class weka.gui.beans.IncrementalClassifierEvent
Creates a new IncrementalClassifierEvent instance.
IncrementalClassifierEvent(Object) - Constructor for class weka.gui.beans.IncrementalClassifierEvent
 
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
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
 
Instance - class weka.core.Instance.
Class for handling an instance.
Instance(Instance) - Constructor for class weka.core.Instance
Constructor that copies the attribute values and the weight from the given instance.
Instance(double, double[]) - Constructor for class weka.core.Instance
Constructor that inititalizes instance variable with given values.
Instance(int) - Constructor for class weka.core.Instance
Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
Instance() - Constructor for class weka.core.Instance
Private constructor for subclasses.
InstanceConverter - interface weka.core.metrics.InstanceConverter.
InstanceConverter interface Certain metrics may require converting instances before performing computations
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(Object, Instance, int) - Constructor for class weka.gui.beans.InstanceEvent
Creates a new InstanceEvent instance.
InstanceEvent(Object) - Constructor for class weka.gui.beans.InstanceEvent
 
InstanceEvent - class weka.gui.streams.InstanceEvent.
An event encapsulating an instance stream event.
InstanceEvent(Object, int) - Constructor for class weka.gui.streams.InstanceEvent
Constructs an InstanceEvent with the specified source object and event type
InstanceJoiner - class 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
 
InstanceMetric - class weka.deduping.metrics.InstanceMetric.
Abstract InstanceMetric class for writing metrics that calculate distance between instances describing database records
InstanceMetric() - Constructor for class weka.deduping.metrics.InstanceMetric
 
InstancePair - class weka.clusterers.InstancePair.
Class for handling a pair of instances, in terms of indices of instances in an Instances set
InstancePair - class weka.deduping.InstancePair.
This is a basic class for a training pair
InstancePair(Instance, Instance, boolean, double) - Constructor for class weka.deduping.InstancePair
 
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
InstanceReference - class weka.deduping.blocking.InstanceReference.
 
InstanceReference(Instance, int, String, HashMapVector, double) - Constructor for class weka.deduping.blocking.InstanceReference
 
InstanceReference(Instance, int, String, HashMapVector) - Constructor for class weka.deduping.blocking.InstanceReference
Create a reference to this record, initializing its length to 0
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
 
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
 
Instances - class weka.core.Instances.
Class for handling an ordered set of weighted instances.
Instances(Reader) - Constructor for class weka.core.Instances
Reads an ARFF file from a reader, and assigns a weight of one to each instance.
Instances(Reader, int) - Constructor for class weka.core.Instances
Reads the header of an ARFF file from a reader and reserves space for the given number of 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(String, FastVector, int) - Constructor for class weka.core.Instances
Creates an empty set of instances.
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.
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, int) - Constructor for class weka.classifiers.functions.pace.IntVector
Constructs an n-vector of a constant
IntVector(int[]) - Constructor for class weka.classifiers.functions.pace.IntVector
Constructs a vector given an int array
ItemSet - class weka.associations.ItemSet.
Class for storing a set of items.
ItemSet(int) - Constructor for class weka.associations.ItemSet
Constructor
IterativeClassifier - interface weka.classifiers.IterativeClassifier.
Interface for classifiers that can induce models of growing complexity one step at a time.
identity(int, int) - Static method in class weka.classifiers.functions.pace.Matrix
Generate identity matrix
idf - Variable in class weka.deduping.metrics.TokenInfo
The IDF (inverse document frequency) factor for this token which indicates how much to weight an occurence.
idx - Variable in class weka.deduping.blocking.InstanceReference
The index of the instance in the dataset
ignoredAttributeIndicesTipText() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns the tip text for this property
inRanges(Instance, double[][]) - Static method in class weka.core.Instances
Test if an instance is within the given ranges.
inSplit(Instance) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
This will check if an instance is inside or outside of the current shapes.
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.EnsembleEvaluation
Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incorrect() - Method in class weka.classifiers.Evaluation
Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
incorrect() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of incorrect classifications (that is, for which an incorrect prediction was made).
increment(String, double) - Method in class weka.deduping.metrics.HashMapVector
Increment the weight for the given token in the vector by the given amount.
increment(String) - Method in class weka.deduping.metrics.HashMapVector
Increment the weight for the given token in the vector by 1.
increment(String, int) - Method in class weka.deduping.metrics.HashMapVector
Increment the weight for the given token in the vector by the given int
increment() - Method in class weka.deduping.metrics.Weight
Increment and return the new count
increment(int) - Method in class weka.deduping.metrics.Weight
Increment by n and return the new count
increment(double) - Method in class weka.deduping.metrics.Weight
Increment by n and return the new count
incrementFailed(int) - Method in class weka.experiment.RemoteExperiment
Increment the overall number of failures and the number of failures for a particular host
incrementFinished() - Method in class weka.experiment.RemoteExperiment
Increment the number of successfully completed sub experiments
incremental(double, int) - Method in class weka.classifiers.trees.m5.Impurity
Incrementally computes the impurirty values
indeX - Variable in class weka.classifiers.rules.part.ClassifierDecList
Which son to expand?
index() - Method in class weka.core.Attribute
Returns the index of this attribute.
index(int) - Method in class weka.core.Instance
Returns the index of the attribute stored at the given position.
index(int) - Method in class weka.core.SparseInstance
Returns the index of the attribute stored at the given position.
indexInstance(Instance, int, String, HashMapVector) - Method in class weka.deduping.blocking.Blocking
Index a given Instance using its corresponding vector
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.
indexString(String, HashMapVector) - Method in class weka.deduping.metrics.JaccardMetric
Index a given string using its corresponding vector
indexString(String, HashMapVector) - Method in class weka.deduping.metrics.KernelVSMetric
Index a given string using its corresponding vector
indexString(String, HashMapVector) - Method in class weka.deduping.metrics.VectorSpaceMetric
Index a given string using its corresponding vector
indexToken(String, int, InstanceReference) - Method in class weka.deduping.blocking.Blocking
Add a token occurrence to the index.
indexToken(String, int, StringReference) - Method in class weka.deduping.metrics.JaccardMetric
Add a token occurrence to the index.
indexToken(String, int, StringReference) - Method in class weka.deduping.metrics.KernelVSMetric
Add a token occurrence to the index.
indexToken(String, int, StringReference) - Method in class weka.deduping.metrics.VectorSpaceMetric
Add a token occurrence to the index.
indicesToRangeList(int[]) - Static method in class weka.core.Range
Creates a string representation of the indices in the supplied array.
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.BinC45Split
Returns (C4.5-type) information gain for the generated split.
infoGain() - Method in class weka.classifiers.trees.j48.C45Split
Returns (C4.5-type) information gain for the generated split.
init() - Method in class weka.classifiers.sparse.IBkMetric
Initialise scheme variables.
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.ADTree
Sets up the tree ready to be trained, using two-class optimized method.
initClusterAssignments() - Method in class weka.clusterers.HAC
Update the clusterAssignments for all points in two clusters that are about to be merged
initConstraints() - Method in class weka.clusterers.HAC
Internal method that initializes distances between seed clusters to POSITIVE_INFINITY
initCosts() - Method in class weka.deduping.metrics.AffineProbMetric
initialize the costs using current values of the probabilities
initDebugVektorsInput() - Method in class weka.clusterers.XMeans
Initialises the debug vektor input.
initIntClusters() - Method in class weka.deduping.BasicDeduper
Computes the clusters from the cluster assignments
initInternalFields() - Method in class weka.gui.visualize.MatrixPanel
Initializes internal data fields, i.e.
initKernel() - Method in class weka.deduping.metrics.KernelVSMetric
Provided that all features are known, initialize the feature space for the kernel
initLength() - Method in class weka.deduping.metrics.HashMapVector
 
initMeasures() - Method in class weka.classifiers.EnsembleClassifier
Initialize measures
initMinMax(Instances) - Method in class weka.clusterers.FarthestFirst
 
initModel() - Method in class weka.classifiers.bayes.SemiSupEM
Intialize model using appropriate set of data
initProbs() - Method in class weka.deduping.metrics.AffineProbMetric
initialize the probabilities to some startup values
initSelector(Instances) - Method in class weka.core.metrics.PairwiseSelector
Initialize m_classInstanceMap and m_classValueList using a given set of instances
initSelector(Instances) - Method in class weka.deduping.PairwiseSelector
Initialize m_classInstanceMap and m_classValueList using a given set of instances
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).
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
initialize() - Method in class weka.clusterers.AlgVector
Creates and returns a clone of this object.
initialize(Random) - Method in class weka.clusterers.AlgVector
Resets the elements to the default value which is 0.0.
initialize() - Method in class weka.clusterers.assigners.LPAssigner
Initialize fields from the current clustererer
initialize() - Method in class weka.core.AlgVector
Writes out a matrix.
initialize(Random) - Method in class weka.core.AlgVector
Resets the elements to the default value which is 0.0.
initialize() - Method in class weka.core.Matrix
Resets the elements to default values (i.e.
initialize() - Method in class weka.experiment.Experiment
Prepares an experiment for running, initializing current iterator settings.
initialize() - Method in class weka.experiment.RemoteExperiment
Prepares a remote experiment for running, creates sub experiments
initialize() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set up the off screen bitmap for rendering to
initializeClusterer() - Method in class weka.clusterers.SeededKMeans
Initializes the cluster centroids - initial M step
initializeRanges(Instances, int[]) - Static method in class weka.clusterers.XMeans
Function should be in the Instances class!! Initializes the minimum and maximum values based on all instances.
initializeRanges() - Method in class weka.core.Instances
Initializes the ranges using all instances of the dataset.
initializeRanges(int[]) - Method in class weka.core.Instances
Initializes the ranges of a subset of the instances of this dataset.
initializeRangesEmpty(int, double[][]) - Method in class weka.core.Instances
Used to initialize the ranges.
innerProduct(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector
Returns the inner product of two DoubleVectors
input(Instance) - Method in class weka.filters.AllFilter
Input an instance for filtering.
input(Instance) - Method in class weka.filters.Filter
Input an instance for filtering.
input(Instance) - Method in class weka.filters.NullFilter
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.AttributeSelection
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.ClassOrder
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.Discretize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.attribute.NominalToBinary
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.Resample
Input an instance for filtering.
input(Instance) - Method in class weka.filters.supervised.instance.SpreadSubsample
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Add
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.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.Copy
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Discretize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.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.Normalize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NumericToBinary
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Obfuscate
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Remove
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RemoveType
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.Standardize
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.StringToNominal
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.SwapValues
Input an instance for filtering.
input(Instance) - Method in class weka.filters.unsupervised.attribute.UnitVector
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.
input(Instance) - Method in class weka.filters.unsupervised.instance.Resample
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.gui.streams.InstanceCounter
 
input(Instance) - Method in class weka.gui.streams.InstanceJoiner
 
input(Instance) - Method in class weka.gui.streams.InstanceSavePanel
 
input(Instance) - Method in class weka.gui.streams.InstanceTable
 
input(Instance) - Method in class weka.gui.streams.InstanceViewer
 
inputFormat(Instances) - Method in class weka.filters.Filter
Deprecated. use setInputFormat(Instances) instead.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceCounter
 
inputFormat(Instances) - Method in class weka.gui.streams.InstanceJoiner
Sets the format of the input instances.
inputFormat(Instances) - Method in class weka.gui.streams.InstanceSavePanel
 
inputFormat(Instances) - Method in class weka.gui.streams.InstanceTable
 
inputFormat(Instances) - Method in class weka.gui.streams.InstanceViewer
 
insert(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Inserts a new entry in the hashtable using the specified key.
insertAttributeAt(int) - Method in class weka.core.Instance
Inserts an attribute at the given position (0 to numAttributes()).
insertAttributeAt(Attribute, int) - Method in class weka.core.Instances
Inserts an attribute at the given position (0 to numAttributes()) and sets all values to be missing.
insertElementAt(Object, int) - Method in class weka.core.FastVector
Inserts an element at the given position.
insertHoldOutInstance(Instance, double, REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Inserts an instance from the hold-out set into the tree.
insertHoldOutSet(Instances) - Method in class weka.classifiers.trees.REPTree.Tree
Inserts hold-out set into tree.
insertSorted(double, Instance) - Method in class weka.classifiers.sparse.IBkMetric.NeighborList
Inserts an instance neighbor into the list, maintaining the list sorted by distance.
installLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode
Traverses the tree and installs linear models at each node.
instance(int) - Method in class weka.core.Instances
Returns the instance at the given position.
instance - Variable in class weka.deduping.blocking.InstanceReference
The referenced instance.
instance1 - Variable in class weka.core.metrics.TrainingPair
 
instance1 - Variable in class weka.deduping.InstancePair
 
instance2 - Variable in class weka.core.metrics.TrainingPair
 
instance2 - Variable in class weka.deduping.InstancePair
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceCounter
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
 
instanceProduced(InstanceEvent) - Method in interface weka.gui.streams.InstanceListener
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceSavePanel
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceTable
 
instanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceViewer
 
instanceRef - Variable in class weka.deduping.blocking.TokenInstanceOccurrence
A reference to the Document where it occurs
instanceWeights(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Checks whether the classifier can handle instance weights.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.Splitter
Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit
Gets the subset of instances that apply to a particluar branch of the split.
instancesDownBranch(int, Instances) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Gets the subset of instances that apply to a particluar branch of the split.
instancesIndicesTipText() - Method in class weka.filters.unsupervised.instance.RemoveRange
Returns the tip text for this property
intCount - Variable in class weka.core.AttributeStats
The number of int-like values
interleave(Instances, int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Interleave a set of training examples, which have class attributes sorted
interpolateRecall(Object[], double) - Method in class weka.experiment.ExtractionResultProducer
Given an array containing the overall results of a extraction experiment, produce an array containing results for a specific recall level
inverseLabel(double[]) - Method in class weka.classifiers.meta.ActiveDecorate
Select class label such that the probability of selection is inversely proportional to the ensemble's predictions.
inverseLabel(double[]) - Method in class weka.classifiers.meta.Decorate
Select class label such that the probability of selection is inversely proportional to the ensemble's predictions.
inverseLabel(double[]) - Method in class weka.classifiers.meta.Fable
Select class label such that the probability of selection is inversely proportional to the ensemble's predictions.
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.Copy
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.Remove
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns the tip text for this property
invertSelectionTipText() - Method in class weka.filters.unsupervised.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
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.
isCacheValid(Object[]) - Method in class weka.experiment.DatabaseResultListener
Checks whether the current cache contents are valid for the supplied key.
isClassAttributeString() - Method in class weka.clusterers.MPCKMeans
 
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
isDataDependent() - Method in class weka.deduping.metrics.AffineMetric
A metric can be data-dependent (e.g.
isDate() - Method in class weka.core.Attribute
Tests if the attribute is a date type.
isDistanceBased() - Method in class weka.core.metrics.BarHillelMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.core.metrics.BarHillelMetricMatlab
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.core.metrics.KL
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.core.metrics.Metric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.core.metrics.WeightedDotP
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.core.metrics.WeightedEuclidean
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.core.metrics.WeightedMahalanobis
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.core.metrics.XingMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.AffineMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.AffineProbMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.ClassifierInstanceMetric
The computation can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.InstanceMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.JaccardMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.KernelVSMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.StringMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.SumInstanceMetric
The computation of a metric can be either based on distance, or on similarity
isDistanceBased() - Method in class weka.deduping.metrics.VectorSpaceMetric
The computation of a metric can be either based on distance, or on similarity
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.IntVector
Returns true if the vector is empty
isEmpty() - Method in class weka.classifiers.functions.pace.PaceMatrix
Check if the matrix is empty
isEmpty() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Tests if this hashtable maps no keys to values.
isEmpty() - Method in class weka.classifiers.sparse.IBkMetric.NeighborList
Gets whether the list is empty.
isHierachic(String) - Method in class weka.gui.HierarchyPropertyParser
Whether the given string has a hierachy structure with the seperators
isInRange(double) - Method in class weka.core.Attribute
Determines whether a value lies within the bounds of the attribute.
isInRange(int) - Method in class weka.core.Range
Gets whether the supplied cardinal number is included in the current range.
isInteger(double) - Static method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Return true if the given double represents an integer value
isInteger(double) - Static method in class weka.experiment.ActiveLearningCurveCVResultProducer
Return true if the given double represents an integer value
isInteger(double) - Static method in class weka.experiment.LearningCurveCrossValidationResultProducer
Return true if the given double represents an integer value
isInteger(double) - Static method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Return true if the given double represents an integer value
isInteger(double) - Static method in class weka.experiment.SemiSupCrossValidationResultProducer
Return true if the given double represents an integer value
isInteger(double) - Static method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Return true if the given double represents an integer value
isInteger(double) - Static method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Return true if the given double represents an integer value
isInteger(double) - Static method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Return true if the given double represents an integer value
isInteger(double) - Static method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Return true if the given double represents an integer value
isKeyInCache(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener
Returns true if the supplied key is in the key cache (and thus we do not need to execute a database query).
isKeyInTable(String, ResultProducer, Object[]) - Method in class weka.experiment.DatabaseUtils
Executes a database query to see whether a result for the supplied key is already in the database.
isLeafReached() - Method in class weka.gui.HierarchyPropertyParser
Whether the current position is a leaf
isMissing(int) - Method in class weka.core.Instance
Tests if a specific value is "missing".
isMissing(Attribute) - Method in class weka.core.Instance
Tests if a specific value is "missing".
isMissing(int) - Method in class weka.core.SparseInstance
Tests if a specific value is "missing".
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.
isObjFunDecreasing() - Method in class weka.clusterers.MPCKMeans
Is the objective function decreasing or increasing?
isOutputFormatDefined() - Method in class weka.filters.Filter
Returns whether the output format is ready to be collected
isOutputFormatDefined() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns whether the output format is ready to be collected
isPaintable() - Method in class weka.gui.CostMatrixEditor
Indicates whether the object can be represented graphically.
isPaintable() - Method in class weka.gui.FileEditor
Returns true since this editor is paintable.
isPaintable() - Method in class weka.gui.GenericArrayEditor
Returns true to indicate that we can paint a representation of the string array
isPaintable() - Method in class weka.gui.GenericObjectEditor
Returns true to indicate that we can paint a representation of the Object.
isRegular() - Method in class weka.core.Attribute
Returns whether the attribute values are equally spaced.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.AveragingResultProducer
Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.CSVResultListener
Always says a result is required.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener
Always says a result is required.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultProducer
Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in class weka.experiment.LearningRateResultProducer
Determines whether the results for a specified key must be generated.
isResultRequired(ResultProducer, Object[]) - Method in interface weka.experiment.ResultListener
Determines whether the results for a specified key must be generated.
isRootReached() - Method in class weka.gui.HierarchyPropertyParser
Whether the current position is the root
isSequential() - Method in class weka.clusterers.assigners.LPAssigner
This is a sequential assignment method
isSequential() - Method in class weka.clusterers.assigners.MPCKMeansAssigner
Assigners can be sequential or collective
isSequential() - Method in class weka.clusterers.assigners.RMNAssigner
This is a sequential assignment method
isSequential() - Method in class weka.clusterers.assigners.RandomAssigner
This is a sequential assignment method
isSequential() - Method in class weka.clusterers.assigners.SimpleAssigner
This is a sequential assignment method
isSequential() - Method in class weka.clusterers.assigners.SortedAssigner
This is a sequential assignment method
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
isSparseInstance - Variable in class weka.clusterers.SeededKMeans
indicates whether instances are sparse
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.
isTransductiveTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
isTransductiveTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
isTransductiveTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
isTransductiveTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the tip text for this property
isUniqueInstance(Instance, HashMap, double[]) - Method in class weka.deduping.PairwiseSelector
Check whether an instance is unique
isValid() - Method in class weka.core.KDTree
Returns true if valid flag is true.
isValidRange(String) - Method in class weka.core.Range
Determines if a string represents a valid index or simple range.
itemStateChanged(ItemEvent) - Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the action associated with the ItemEvent.
iterate() - Method in class weka.classifiers.bayes.SemiSupEM
Run EM iterations until likelihood stops increasing significantly or max iterations exhausted
iterationsTipText() - Method in class weka.attributeSelection.MatlabNMF
Returns the tip text for this property
iterator() - Method in class weka.deduping.metrics.HashMapVector
Returns an iterator over the MapEntries in the hashMap

J

J48 - class weka.classifiers.trees.j48.J48.
Class for generating an unpruned or a pruned C4.5 decision tree.
J48() - Constructor for class weka.classifiers.trees.j48.J48
 
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
 
JRip.RipperRule - class weka.classifiers.rules.JRip.RipperRule.
This class implements a single rule that predicts specified class.
JRip.RipperRule() - Constructor for class weka.classifiers.rules.JRip.RipperRule
Constructor
JaccardMetric - class weka.deduping.metrics.JaccardMetric.
This class claculates similarity between two strings using the Jaccard metric Some code borrowed from ir.vsr package by Raymond J.
JaccardMetric() - Constructor for class weka.deduping.metrics.JaccardMetric
Construct a vector space from a given set of examples
jaccardSimilarityOfClassStrings(Instance, Instance) - Static method in class weka.clusterers.InstancePair
 
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.
jp - Variable in class weka.gui.visualize.MatrixPanel
Split pane for splitting the matrix and the buttons and bars

K

KBInformation() - Method in class weka.classifiers.EnsembleEvaluation
Return the total Kononenko & Bratko Information score in bits
KBInformation() - Method in class weka.classifiers.Evaluation
Return the total Kononenko & Bratko Information score in bits
KBMeanInformation() - Method in class weka.classifiers.EnsembleEvaluation
Return the Kononenko & Bratko Information score in bits per instance.
KBMeanInformation() - Method in class weka.classifiers.Evaluation
Return the Kononenko & Bratko Information score in bits per instance.
KBRelativeInformation() - Method in class weka.classifiers.EnsembleEvaluation
Return the Kononenko & Bratko Relative Information score
KBRelativeInformation() - Method in class weka.classifiers.Evaluation
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
 
KDTree - class weka.core.KDTree.
This is a KD-Tree structure that stores instances using a divide and conquer method.
KDTree() - Constructor for class weka.core.KDTree
Default Constructor
KDTree(KDTree) - Constructor for class weka.core.KDTree
Constructor, copies all options from an existing KDTree.
KERNEL_LINEAR - Static variable in class weka.classifiers.sparse.SVMlight
Kernel type
KERNEL_POLYNOMIAL - Static variable in class weka.classifiers.sparse.SVMlight
 
KERNEL_RBF - Static variable in class weka.classifiers.sparse.SVMlight
 
KERNEL_SIGMOID_TANH - Static variable in class weka.classifiers.sparse.SVMlight
 
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
KL - class weka.core.metrics.KL.
KL class Implements weighted Kullback-Leibler divergence
KL(int) - Constructor for class weka.core.metrics.KL
Create a new metric.
KL() - Constructor for class weka.core.metrics.KL
Create a default new metric
KL(int[]) - Constructor for class weka.core.metrics.KL
Creates a new metric which takes specified attributes.
KStar - class weka.classifiers.lazy.kstar.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.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() - Constructor for class weka.classifiers.lazy.kstar.KStarCache
 
KStarCache.CacheTable - class weka.classifiers.lazy.kstar.KStarCache.CacheTable.
A custom hashtable class to support the caching system.
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.CacheTable() - 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
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
 
KernelEstimator - class weka.estimators.KernelEstimator.
Simple kernel density estimator.
KernelEstimator(double) - Constructor for class weka.estimators.KernelEstimator
Constructor that takes a precision argument.
KernelVSMetric - class weka.deduping.metrics.KernelVSMetric.
This class defines a basic string kernel based on vector space Some code borrowed from ir.vsr package by Raymond J.
KernelVSMetric() - Constructor for class weka.deduping.metrics.KernelVSMetric
Construct a vector space from a given set of examples
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.
KnowledgeFlow.BeanLayout - class weka.gui.beans.KnowledgeFlow.BeanLayout.
Used for displaying the bean components and their visible connections
KnowledgeFlow.BeanLayout() - Constructor for class weka.gui.beans.KnowledgeFlow.BeanLayout
 
kappa() - Method in class weka.classifiers.EnsembleEvaluation
Returns value of kappa statistic if class is nominal.
kappa() - Method in class weka.classifiers.Evaluation
Returns value of kappa statistic if class is nominal.
keepLastModel() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
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
klDivergence() - Method in class weka.clusterers.SemiSupClustererEvaluation
 

L

LBR - class weka.classifiers.lazy.LBR.
Class for building and using a Lazy Bayesian classifier.
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world.
LBR() - Constructor for class weka.classifiers.lazy.LBR
 
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
LEFT - Static variable in class weka.classifiers.trees.m5.Rule
 
LEVERAGE - Static variable in class weka.associations.Apriori
 
LIFT - Static variable in class weka.associations.Apriori
 
LINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
LINEAR - Static variable in class weka.classifiers.lazy.LWR
The available kernel weighting methods
LOG2 - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
LOG2 - Variable in class weka.core.metrics.KL
 
LOGPI - Static variable in class weka.core.Statistics
 
LPAssigner - class weka.clusterers.assigners.LPAssigner.
 
LPAssigner() - Constructor for class weka.clusterers.assigners.LPAssigner
 
LUDecomposition() - Method in class weka.core.Matrix
Performs a LUDecomposition on the matrix.
LWR - class weka.classifiers.lazy.LWR.
Locally-weighted regression.
LWR() - Constructor for class weka.classifiers.lazy.LWR
 
LearnableMetric - class weka.core.metrics.LearnableMetric.
Interface to distance metrics that can be learned
LearnableMetric() - Constructor for class weka.core.metrics.LearnableMetric
 
LearnableStringMetric - interface weka.deduping.metrics.LearnableStringMetric.
An interface for learnable string metrics
LearningCurveCrossValidationResultProducer - class weka.experiment.LearningCurveCrossValidationResultProducer.
Does a N-fold cross-validation, but generates a learning curve by also varying the number of training examples.
LearningCurveCrossValidationResultProducer() - Constructor for class weka.experiment.LearningCurveCrossValidationResultProducer
 
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
 
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
 
LegendPanel - class weka.gui.visualize.LegendPanel.
This panel displays legends for a list of plots.
LegendPanel() - Constructor for class weka.gui.visualize.LegendPanel
Constructor
LegendPanel.LegendEntry - class weka.gui.visualize.LegendPanel.LegendEntry.
Inner class for handling legend entries
LegendPanel.LegendEntry(PlotData2D, int) - Constructor for class weka.gui.visualize.LegendPanel.LegendEntry
 
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
 
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.
Loader - interface weka.core.converters.Loader.
Interface to something that can load Instances from an input source in some format.
Loader - class weka.gui.beans.Loader.
Loads data sets using weka.core.converter classes
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
 
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
LogPanel(WekaTaskMonitor) - Constructor for class weka.gui.LogPanel
Creates the log panel
Logger - interface weka.gui.Logger.
Interface for objects that display log (permanent historical) and status (transient) messages.
Logistic - class weka.classifiers.functions.Logistic.
Implements linear logistic regression using LogitBoost and LinearRegression.
Logistic() - Constructor for class weka.classifiers.functions.Logistic
 
LogitBoost - class weka.classifiers.meta.LogitBoost.
Class for boosting any classifier that can handle weighted instances.
LogitBoost() - Constructor for class weka.classifiers.meta.LogitBoost
 
labelData(Instances) - Method in class weka.classifiers.meta.ActiveDecorate
Labels the artificially generated data.
labelData(Instances) - Method in class weka.classifiers.meta.Crate
Labels the artificially generated data.
labelData(Instances, double) - Method in class weka.classifiers.meta.DEC
Labels the randomly generated data.
labelData(Instances) - Method in class weka.classifiers.meta.Decorate
Labels the artificially generated data.
labelData(Instances) - Method in class weka.classifiers.meta.Fable
Labels the artificially generated data.
labelData(Instances, double) - Method in class weka.classifiers.meta.SemiSupDecorate
Labels the randomly generated data.
labeling_method - Variable in class weka.classifiers.meta.DEC
Method to use for labeling randomly generated instances.
labeling_method - Variable in class weka.classifiers.meta.SemiSupDecorate
Method to use for labeling randomly generated instances.
lambdaTipText() - Method in class weka.classifiers.bayes.SemiSupEM
 
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
leafString(REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Outputs description of a leaf node.
leafString() - Method in class weka.classifiers.trees.RandomTree
Outputs a leaf.
learnMetric(Instances) - Method in class weka.core.metrics.BarHillelMetric
Train the distance metric.
learnMetric(Instances) - Method in class weka.core.metrics.BarHillelMetricMatlab
Train the distance metric.
learnMetric(Instances) - Method in class weka.core.metrics.KL
Train the metric
learnMetric(Instances) - Method in class weka.core.metrics.LearnableMetric
Train the distance metric.
learnMetric(Instances) - Method in class weka.core.metrics.WeightedDotP
Updates the weights
learnMetric(Instances) - Method in class weka.core.metrics.WeightedEuclidean
Train the metric
learnMetric(Instances) - Method in class weka.core.metrics.WeightedMahalanobis
Train the metric
learnMetric(Instances) - Method in class weka.core.metrics.XingMetric
Train the distance metric.
learningRateTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
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.
leaveOneOut(LBR.Indexes, int[][][], int[], boolean[]) - Method in class weka.classifiers.lazy.LBR
Leave-one-out strategy.
leftHand - Variable in class weka.classifiers.lazy.LBR
 
leftNode() - Method in class weka.classifiers.trees.m5.RuleNode
Get the left child of this node
leftSide(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.trees.j48.C45Split
Prints left side of condition..
leftSide(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Prints left side of condition satisfied by instances.
leftSide(Instances) - Method in class weka.classifiers.trees.j48.NoSplit
Does nothing because no condition has to be satisfied.
legend() - Method in class weka.classifiers.trees.adtree.ADTree
Returns the legend of the tree, describing how results are to be interpreted.
length() - Method in class weka.core.DynamicArrayOfPosInt
 
length(Instance) - Static method in class weka.core.metrics.Metric
Get the norm-2 length of an instance assuming all attributes are numeric
length - Variable in class weka.deduping.blocking.InstanceReference
The length of the corresponding instance vector.
length() - Method in class weka.deduping.metrics.HashMapVector
Compute Euclidian length (sqrt of sum of squares) of vector
lengthWeighted(Instance, double[]) - Method in class weka.core.metrics.GDMetricLearner
Get the norm-2 length of an instance assuming all attributes are numeric and utilizing the attribute weights
lengthWeighted(Instance) - Method in class weka.core.metrics.WeightedDotP
Get the norm-2 length of an instance assuming all attributes are numeric and utilizing the attribute weights
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.
linkType - Variable in class weka.clusterers.InstancePair
MUST_LINK, CANNOT_LINK or DONT_CARE_LINK
listOptions() - Method in class weka.associations.Apriori
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.BestFirst
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.CfsSubsetEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns an enumeration describing the available options
listOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.ExhaustiveSearch
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.GainRatioAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.GeneticSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.MatlabICA
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.MatlabNMF
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.MatlabPCA
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.PrincipalComponents
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RaceSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RandomSearch
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.RankSearch
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.ReliefFAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.SVMAttributeEval
Returns an enumeration describing all the available options
listOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.BVDecompose
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.RegressionBVDecompose
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.BayesNetK2
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.NaiveBayesSimple
Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.bayes.SemiSupEM
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.LinearRegression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.Logistic
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.SMO
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.VotedPerceptron
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.Winnow
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.functions.neural.NeuralNetwork
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.functions.pace.PaceRegression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.IBk
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.LWR
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.lazy.kstar.KStar
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.ActiveDecorate
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.AdaBoostM1
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AdditiveRegression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.Bagging
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.CVParameterSelection
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.ClassificationViaRegression
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.Crate
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.DEC
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.DistributionMetaClassifier
Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.meta.Fable
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.LogitBoost
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MetaCost
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.MultiBoostAB
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.MultiClassClassifier
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.MultiScheme
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.QBag
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.QBoost
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.RegressionByDiscretization
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.SemiSupDecorate
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.meta.Stacking
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.meta.ThresholdSelector
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.misc.Prototype
Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.misc.PrototypeMetric
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.rules.ConjunctiveRule
Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.DecisionTable
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.rules.JRip
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.Ridor
Returns an enumeration describing the available options Valid options are:
listOptions() - Method in class weka.classifiers.rules.part.PART
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.sparse.IBkMetric
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.sparse.SVMlight
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.RandomForest
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.RandomTree
Lists the command-line options for this classifier.
listOptions() - Method in class weka.classifiers.trees.adtree.ADTree
Returns an enumeration describing the available options..
listOptions() - Method in class weka.classifiers.trees.j48.J48
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.trees.m5.M5Base
Returns an enumeration describing the available options
listOptions() - Method in class weka.classifiers.trees.m5.M5P
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.clusterers.DistributionMetaClusterer
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.HAC
Returns an enumeration describing the available options
listOptions() - Method in class weka.clusterers.MPCKMeans
 
listOptions() - Method in class weka.clusterers.PCKMeans
 
listOptions() - Method in class weka.clusterers.PCSoftKMeans
 
listOptions() - Method in class weka.clusterers.SeededKMeans
 
listOptions() - Method in class weka.clusterers.SimpleKMeans
Returns an enumeration describing the available options..
listOptions() - Method in class weka.clusterers.XMeans
Returns an enumeration describing the available options.
listOptions() - Method in class weka.clusterers.assigners.LPAssigner
 
listOptions() - Method in class weka.clusterers.assigners.RMNAssigner
 
listOptions() - Method in class weka.clusterers.assigners.RandomAssigner
 
listOptions() - Method in class weka.clusterers.assigners.SimpleAssigner
 
listOptions() - Method in class weka.clusterers.assigners.SortedAssigner
 
listOptions() - Method in class weka.core.KDTree
Returns an enumeration describing the available options.
listOptions() - Method in interface weka.core.OptionHandler
Returns an enumeration of all the available options..
listOptions() - Method in class weka.core.metrics.AttrEvalMetricLearner
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.BarHillelMetric
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.BarHillelMetricMatlab
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.ClassifierMetricLearner
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.GDMetricLearner
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.HardPairwiseSelector
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.KL
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.RandomPairwiseSelector
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.WeightedDotP
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.WeightedEuclidean
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.WeightedMahalanobis
Returns an enumeration describing the available options.
listOptions() - Method in class weka.core.metrics.XingMetric
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.datagenerators.TextSource
 
listOptions() - Method in class weka.deduping.BasicDeduper
Returns an enumeration describing the available options
listOptions() - Method in class weka.deduping.PairwiseSelector
Returns an enumeration describing the available options.
listOptions() - Method in class weka.deduping.blocking.Blocking
Returns an enumeration describing the available options.
listOptions() - Method in class weka.deduping.metrics.AffineMetric
Returns an enumeration describing the available options.
listOptions() - Method in class weka.deduping.metrics.AffineProbMetric
Returns an enumeration describing the available options.
listOptions() - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Returns an enumeration describing the available options
listOptions() - Method in class weka.deduping.metrics.JaccardMetric
Returns an enumeration describing the available options.
listOptions() - Method in class weka.deduping.metrics.KernelVSMetric
Returns an enumeration describing the available options.
listOptions() - Method in class weka.deduping.metrics.NGramTokenizer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.deduping.metrics.SumInstanceMetric
Returns an enumeration describing the available options
listOptions() - Method in class weka.deduping.metrics.VectorSpaceMetric
Returns an enumeration describing the available options.
listOptions() - Method in class weka.deduping.metrics.WordTokenizer
Returns an enumeration describing the available options.
listOptions() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
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.CSVResultListener
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.CrossValidationResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DatabaseResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DeduperSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
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.ExtractionResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.ExtractionSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.InstanceQuery
Returns an enumeration describing the available options
listOptions() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
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.NoiseCurveCrossValidationResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.PairedTTester
Lists options understood by this object.
listOptions() - Method in class weka.experiment.RandomSplitResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.RegressionSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns an enumeration describing the available options..
listOptions() - Method in class weka.extraction.ClusteringExtractor
Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.ClassOrder
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.Discretize
Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.attribute.NominalToBinary
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.supervised.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.AbstractTimeSeries
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Add
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddCluster
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.Copy
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.Discretize
Gets an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.FirstOrder
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
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.NumericTransform
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.Remove
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.StringToNominal
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Returns an enumeration describing the available options
listOptions() - Method in class weka.filters.unsupervised.attribute.SwapValues
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.Randomize
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns an enumeration describing the available options.
listOptions() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Gets an enumeration describing the available options..
listOptions() - Method in class weka.filters.unsupervised.instance.RemoveRange
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.filters.unsupervised.instance.Resample
Returns an enumeration describing the available options.
listener - Variable in class weka.gui.visualize.VisualizePanel
An optional listener that we will inform when ComboBox selections change
lnFactorial(double) - Static method in class weka.core.SpecialFunctions
Returns natural logarithm of factorial using gamma function.
lnGamma(double) - Static method in class weka.core.Statistics
Returns natural logarithm of gamma function.
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.
loadCache(ResultProducer, Object[]) - Method in class weka.experiment.DatabaseResultListener
Executes a database query to fill the key cache
loadClassifier() - Method in class weka.gui.explorer.ClassifierPanel
Loads a classifier
loadClusterer() - Method in class weka.gui.explorer.ClustererPanel
Loads a clusterer
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.
localModel() - Method in class weka.classifiers.rules.part.ClassifierDecList
Method just exists to make program easier to read.
localNaiveBayes(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR
Class for building and using a simple Naive Bayes classifier.
locallyPredictiveTipText() - Method in class weka.attributeSelection.CfsSubsetEval
Returns the tip text for this property
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.classifiers.trees.j48.EntropyBasedSplitCrit
The log of 2.
log2 - Static variable in class weka.core.Utils
The natural logarithm of 2.
log2(double) - Static method in class weka.core.Utils
Returns the logarithm of a for base 2.
log2Binomial(double, double) - Static method in class weka.core.SpecialFunctions
Returns base 2 logarithm of binomial coefficient using gamma function.
log2Multinomial(double, double[]) - Static method in class weka.core.SpecialFunctions
Returns base 2 logarithm of multinomial using gamma function.
log2MultipleHypergeometric(double[][]) - Static method in class weka.core.ContingencyTables
Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.
logFunc(double) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
Help method for computing entropy.
logLikelihood() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
logLikelihoodAfter() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
logMessage(String) - Method in class weka.gui.LogPanel
Sends the supplied message to the log area.
logMessage(String) - Method in interface weka.gui.Logger
Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.SysErrLog
Sends the supplied message to the log area.
logMessage(String) - Method in class weka.gui.experiment.RunPanel
Sends the supplied message to the log panel log area.
logPSI - Static variable in class weka.classifiers.functions.pace.Maths
The constant - log( sqrt(2 pi) )
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 class weka.classifiers.bayes.DiscreteEstimatorBayes
Gets the log score contribution of this distribution
logScore(int) - Method in interface weka.classifiers.bayes.Scoreable
Returns log-score
logSum(double[]) - Method in class weka.classifiers.bayes.SemiSupEM
Sums log of probabilities using special method for summing in log space
logSum(double, double) - Method in class weka.deduping.metrics.AffineProbMetric
Calculation of log(a+b) with a correction for machine precision
lookupInstanceCluster(Instance) - Method in class weka.clusterers.MPCKMeans
lookup the instance in the checksum hash
lookupInstanceCluster(Instance) - Method in class weka.clusterers.PCKMeans
lookup the instance in the checksum hash
lookupInstanceCluster(Instance) - Method in class weka.clusterers.PCSoftKMeans
lookup the instance in the checksum hash
lowProbLabel(double[]) - Method in class weka.classifiers.meta.DEC
Probabilisticly select class label - (low probability).
lowProbLabel(double[]) - Method in class weka.classifiers.meta.SemiSupDecorate
Probabilisticly select class label - (low probability).
lowerBoundMinSupportTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
lowerNumericBoundIsOpen() - Method in class weka.core.Attribute
Returns whether the lower numeric bound of the attribute is open.
lowerSizeTipText() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
lowerSizeTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the tip text for this property
lr - Variable in class weka.classifiers.lazy.LWR
The linear regression object
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

M

M5Base - class weka.classifiers.trees.m5.M5Base.
M5Base.
M5Base() - Constructor for class weka.classifiers.trees.m5.M5Base
Constructor
M5P - class weka.classifiers.trees.m5.M5P.
M5P.
M5P() - Constructor for class weka.classifiers.trees.m5.M5P
Creates a new M5P instance.
M5Rules - class weka.classifiers.rules.M5Rules.
M5 Rules.
M5Rules() - Constructor for class weka.classifiers.rules.M5Rules
 
MACHEP - Static variable in class weka.core.Statistics
Some constants
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
 
MATRIX_ON_DEMAND - Static variable in class weka.classifiers.meta.MetaCost
 
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
 
MATRIX_SUPPLIED - Static variable in class weka.classifiers.meta.MetaCost
 
MAXGAM - Static variable in class weka.core.Statistics
 
MAXLOG - Static variable in class weka.core.Statistics
 
MAX_FAILURES - Static variable in class weka.experiment.RemoteExperiment
allow at most 3 failures on a host before it is removed from the list of usable hosts
MAX_PRECISION - Static variable in class weka.gui.visualize.VisualizeUtils
Default maximum precision for the display of numeric values
MAX_SHAPES - Static variable in class weka.gui.visualize.Plot2D
 
MDL - Static variable in interface weka.classifiers.bayes.Scoreable
 
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
 
MINLOG - Static variable in class weka.core.Statistics
 
MIN_MAX - Static variable in class weka.experiment.Grapher
errorBar value for error bars using min and max values
MIN_MAX - Static variable in class weka.experiment.NoiseGrapher
errorBar value for error bars using min and max values
MIN_VALUE - Static variable in class weka.classifiers.meta.ThresholdSelector
The minimum value for the criterion.
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.
MISSING_VALUE - Static variable in class weka.core.Instance
Constant representing a missing value.
MODE_DOCUMENT_CLUSTERS - Static variable in class weka.extraction.ClusteringExtractor
Two fundamental modes.
MODE_MIXED - Static variable in class weka.extraction.ClusteringExtractor
 
MODE_SEGMENT_CLUSTERS - Static variable in class weka.extraction.ClusteringExtractor
 
MOVING - Static variable in class weka.gui.beans.KnowledgeFlow
 
MPCKMeans - class weka.clusterers.MPCKMeans.
Pairwise constrained k means clustering class.
MPCKMeans() - Constructor for class weka.clusterers.MPCKMeans
 
MPCKMeans(Metric) - Constructor for class weka.clusterers.MPCKMeans
 
MPCKMeansAssigner - class weka.clusterers.assigners.MPCKMeansAssigner.
 
MPCKMeansAssigner() - Constructor for class weka.clusterers.assigners.MPCKMeansAssigner
Default constructors
MPCKMeansAssigner(MPCKMeans) - Constructor for class weka.clusterers.assigners.MPCKMeansAssigner
Initialize with a clusterer
MUST_LINK - Static variable in class weka.clusterers.InstancePair
must-link
M_AVERAGE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
M_DELETE - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
Missing value handling mode
M_MAXDIFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
M_NORMAL - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
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
MakeADTree(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.ADNode
create sub tree
MakeADTree(Instances) - Static method in class weka.classifiers.bayes.ADNode
create AD tree from set of instances
MakeDecList - class weka.classifiers.rules.part.MakeDecList.
Class for handling a decision list.
MakeDecList(ModelSelection, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for unpruned dec list.
MakeDecList(ModelSelection, double, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for dec list pruned using C4.5 pruning.
MakeDecList(ModelSelection, int, int) - Constructor for class weka.classifiers.rules.part.MakeDecList
Constructor for dec list pruned using hold-out pruning.
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
 
MakeVaryNode(int, FastVector, Instances) - Static method in class weka.classifiers.bayes.ADNode
create sub tree
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.
Maths - class weka.classifiers.functions.pace.Maths.
Class for some utility mathematical or statistical functions.
Maths() - Constructor for class weka.classifiers.functions.pace.Maths
 
MatlabICA - class weka.attributeSelection.MatlabICA.
Class for performing independent components analysis/transformation.
MatlabICA() - Constructor for class weka.attributeSelection.MatlabICA
 
MatlabMetricLearner - class weka.core.metrics.MatlabMetricLearner.
MatlabMetricLearner - learns metric parameters by constructing "difference instances" and then learning weights that classify same-class instances as positive, and different-class instances as negative using an external Matlab program.
MatlabMetricLearner() - Constructor for class weka.core.metrics.MatlabMetricLearner
Create a new matlab metric learner
MatlabNMF - class weka.attributeSelection.MatlabNMF.
Class for performing non-negative matrix factorization/transformation.
MatlabNMF() - Constructor for class weka.attributeSelection.MatlabNMF
 
MatlabPCA - class weka.attributeSelection.MatlabPCA.
Class for performing principal components analysis/transformation.
MatlabPCA() - Constructor for class weka.attributeSelection.MatlabPCA
 
Matrix - class weka.classifiers.functions.pace.Matrix.
Jama = Java Matrix class.
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(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 - class weka.core.Matrix.
Class for performing operations on a matrix of floating-point values.
Matrix(int, int) - Constructor for class weka.core.Matrix
Constructs a matrix and initializes it with default values.
Matrix(double[][]) - Constructor for class weka.core.Matrix
Constructs a matrix using a given array.
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
MaxParentSetSize(int) - Method in class weka.classifiers.bayes.ParentSet
reserve memory for parent set
MergeTwoValues - class weka.filters.unsupervised.attribute.MergeTwoValues.
Merges two values of a nominal attribute.
MergeTwoValues() - Constructor for class weka.filters.unsupervised.attribute.MergeTwoValues
 
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
 
Metric - class weka.core.metrics.Metric.
Abstract Metric class
Metric() - Constructor for class weka.core.metrics.Metric
 
MetricLearner - class weka.core.metrics.MetricLearner.
Abstract MetricLearner interface.
MetricLearner() - Constructor for class weka.core.metrics.MetricLearner
 
MixtureDistribution - class weka.classifiers.functions.pace.MixtureDistribution.
Abtract class for manipulating mixture distributions.
MixtureDistribution() - Constructor for class weka.classifiers.functions.pace.MixtureDistribution
 
ModelSelection - class weka.classifiers.trees.j48.ModelSelection.
Abstract class for model selection criteria.
ModelSelection() - Constructor for class weka.classifiers.trees.j48.ModelSelection
 
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
 
MultiScheme - class weka.classifiers.meta.MultiScheme.
Class for selecting a classifier from among several using cross validation on the training data.
MultiScheme() - Constructor for class weka.classifiers.meta.MultiScheme
 
m - Variable in class weka.classifiers.functions.pace.Matrix
Row and column dimensions.
mStep() - Method in class weka.classifiers.bayes.SemiSupEM
 
mTipText() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Returns the tip text for this property
mTipText() - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
 
m_ADNodes - Variable in class weka.classifiers.bayes.VaryNode
list of ADNode children
m_AEEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel
The panel showing the current attribute evaluation method
m_ALF - Variable in class weka.core.Optimization
 
m_ASEPanel - Variable in class weka.gui.explorer.AttributeSelectionPanel
The panel showing the current search method
m_AblationLevel - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Ablational level
m_Active - Variable in class weka.clusterers.MPCKMeans
active mode?
m_Active - Variable in class weka.clusterers.PCKMeans
active mode?
m_ActualCount - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of train instances with no missing attribute values
m_AddBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to add an algorithm
m_AddBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to add a dataset
m_AdditionalMeasures - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.AveragingResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.CrossValidationResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.DatabaseResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.Experiment
Method names of additional measures of objects contained in the custom property iterator.
m_AdditionalMeasures - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.LearningRateResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.RandomSplitResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.RegressionSplitEvaluator
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdditionalMeasures - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The names of any additional measures to look for in SplitEvaluators
m_AdjacencyList - Variable in class weka.clusterers.MPCKMeans
adjacency list for random
m_AdjacencyList - Variable in class weka.clusterers.PCKMeans
adjacency list for neighborhoods
m_AdjacencyList - Variable in class weka.clusterers.PCSoftKMeans
adjacency list for neighborhoods
m_AdvanceDataSetFirst - Variable in class weka.experiment.Experiment
If true an experiment will advance the current data set befor any custom itererator
m_AdvancedSetupRBut - Variable in class weka.gui.experiment.SetupModePanel
The button for choosing advanced setup mode
m_Algorithm - Variable in class weka.clusterers.MPCKMeans
algorithm, by default spherical
m_Algorithm - Variable in class weka.clusterers.PCKMeans
algorithm, by default spherical
m_Algorithm - Variable in class weka.clusterers.PCSoftKMeans
algorithm, by default spherical
m_Algorithm - Variable in class weka.clusterers.SeededKMeans
algorithm, by default spherical
m_AlgorithmListModel - Variable in class weka.gui.experiment.AlgorithmListPanel
The list model used
m_AlgorithmListPanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel for configuring selected algorithms
m_AllExplore - Variable in class weka.clusterers.PCKMeans
Two-phase active learning or All Explore
m_AllInstances - Variable in class weka.classifiers.bayes.SemiSupEM
Complete set of labeled and unlabeled instances for EM
m_Alpha - Variable in class weka.classifiers.functions.Winnow
The promotion coefficient
m_Alpha - Variable in class weka.classifiers.meta.Crate
Factor specifying desired amount of diversity
m_AnalysisResults - Variable in class weka.classifiers.CheckClassifier
The results of the analysis as a string
m_Antds - Variable in class weka.classifiers.rules.ConjunctiveRule
The vector of antecedents of this rule
m_Antds - Variable in class weka.classifiers.rules.JRip.RipperRule
The vector of antecedents of this rule
m_ApplyFilterBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to apply filters and save the results
m_ArffFilter - Variable in class weka.gui.SetInstancesPanel
Filter to ensure only arff files are selected
m_ArffFilter - Variable in class weka.gui.experiment.DatasetListPanel
A filter to ensure only arff files get selected
m_ArffFilter - Variable in class weka.gui.experiment.ResultsPanel
Filter to ensure only arff files are selected for result files
m_ArffFilter - Variable in class weka.gui.explorer.PreprocessPanel
Filter to ensure only arff files are selected
m_ArffFilter - Variable in class weka.gui.visualize.VisualizePanel
Filter to ensure only arff files are selected
m_ArrayEditor - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Allows editing of the custom property values
m_ArtSize - Variable in class weka.classifiers.meta.ActiveDecorate
Amount of artificial/random instances to use - specified as a fraction of the training data size.
m_ArtSize - Variable in class weka.classifiers.meta.Crate
Amount of artificial/random instances to use - specified as a fraction of the training data size.
m_ArtSize - Variable in class weka.classifiers.meta.Decorate
Amount of artificial/random instances to use - specified as a fraction of the training data size.
m_ArtSize - Variable in class weka.classifiers.meta.Fable
Amount of artificial/random instances to use - specified as a fraction of the training data size.
m_Assigner - Variable in class weka.clusterers.MPCKMeans
Define possible assignment strategies
m_AssociationPanel - Variable in class weka.gui.explorer.Explorer
Label for a panel that still need to be implemented
m_AssociatorEditor - Variable in class weka.gui.explorer.AssociationsPanel
Lets the user configure the associator
m_AttIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
the array attribute indexes
m_AttPanel - Variable in class weka.gui.explorer.PreprocessPanel
Panel to let the user toggle attributes
m_AttSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel
Displays summary stats on the selected attribute
m_AttValues - Variable in class weka.core.Instance
The instance's attribute values.
m_AttVisualizePanel - Variable in class weka.gui.explorer.PreprocessPanel
The visualization of the attribute values
m_AttrIndex - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The index of the nominal attribute in the test and train instances
m_AttrIndex - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The index of the attribute in the test and train instances
m_Attribute - Variable in class weka.classifiers.trees.REPTree.Tree
The attribute to split on.
m_Attribute - Variable in class weka.classifiers.trees.RandomTree
The attribute to split on.
m_Attribute - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Stores which attribute to be used for filtering
m_AttributeEvaluatorEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user configure the attribute evaluator
m_AttributeNameLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the name of the relation
m_AttributeSearchEditor - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user configure the search method
m_AttributeSelection - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The attribute selection object
m_AttributeSelectionPanel - Variable in class weka.gui.explorer.Explorer
Label for a panel that still need to be implemented
m_AttributeSet - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Stores the attribute setting
m_AttributeStats - Variable in class weka.classifiers.meta.ActiveDecorate
Attribute statistics - used for generating artificial examples.
m_AttributeStats - Variable in class weka.classifiers.meta.Crate
Attribute statistics - used for generating artificial examples.
m_AttributeStats - Variable in class weka.classifiers.meta.Decorate
Attribute statistics - used for generating artificial examples.
m_AttributeStats - Variable in class weka.classifiers.meta.Fable
Attribute statistics - used for generating artificial examples.
m_AttributeStats - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Store Statistics of Attributes
m_AttributeStats - Variable in class weka.gui.AttributeSummaryPanel
Cached stats on the attributes we've summarized so far
m_AttributeType - Variable in class weka.filters.unsupervised.attribute.Add
Record the type of attribute to insert
m_AttributeTypeLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the type of attribute
m_AttributeTypes - Variable in class weka.experiment.InstancesResultListener
Stores the attribute types for each column
m_Attributes - Variable in class weka.core.Instances
The attribute information.
m_AverageProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Average probability of test attribute transforming into train attribute
m_AverageProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Average probability of test attribute transforming into train attribute
m_BETA - Variable in class weka.core.Optimization
 
m_Backup - Variable in class weka.gui.GenericObjectEditor
Holds a copy of the current object that can be reverted to if the user decides to cancel
m_BagSizePercent - Variable in class weka.classifiers.meta.Bagging
The size of each bag sample, as a percentage of the training size
m_BagSizePercent - Variable in class weka.classifiers.meta.MetaCost
The size of each bag sample, as a percentage of the training size
m_BagSizePercent - Variable in class weka.classifiers.meta.QBag
The size of each bag sample, as a percentage of the training size
m_Balanced - Variable in class weka.classifiers.functions.Winnow
Use the balanced variant?
m_BaseClassifiers - Variable in class weka.classifiers.meta.Stacking
The base classifiers.
m_BaseFormat - Variable in class weka.classifiers.meta.Stacking
Format for base data
m_BestClassifierOptions - Variable in class weka.classifiers.meta.CVParameterSelection
The set of all classifier options as determined by cross-validation
m_BestPerformance - Variable in class weka.classifiers.meta.CVParameterSelection
The cross-validated performance of the best options
m_BestThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The threshold that lead to the best performance
m_BestValue - Variable in class weka.classifiers.meta.ThresholdSelector
The best value that has been observed
m_Beta - Variable in class weka.classifiers.functions.Winnow
The demotion coefficient
m_Betas - Variable in class weka.classifiers.meta.AdaBoostM1
Array for storing the weights for the votes.
m_Betas - Variable in class weka.classifiers.meta.MultiBoostAB
Array for storing the weights for the votes.
m_Betas - Variable in class weka.classifiers.meta.QBoost
Array for storing the weights for the votes.
m_Bias - Variable in class weka.classifiers.BVDecompose
The calculated bias (squared)
m_Bias - Variable in class weka.classifiers.RegressionBVDecompose
The calculated bias (squared)
m_BlendFactor - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
default sphere of influence blend setting
m_BlendFactor - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
default sphere of influence blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStar
0 = use specified blend, 1 = entropic blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
B_SPHERE = use specified blend, B_ENTROPY = entropic blend setting
m_BlendMethod - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
0 = use specified blend, 1 = entropic blend setting
m_BrowseDestinationButton - Variable in class weka.gui.experiment.SimpleSetupPanel
Button for browsing destination files
m_C - Variable in class weka.classifiers.sparse.SVMlight
trade-off between training error and margin (default 0 corresponds to [avg.
m_CEPanel - Variable in class weka.gui.explorer.AssociationsPanel
The panel showing the current associator selection
m_CEPanel - Variable in class weka.gui.explorer.ClassifierPanel
The panel showing the current classifier selection
m_CLPanel - Variable in class weka.gui.explorer.ClustererPanel
The panel showing the current clusterer selection
m_CVBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to set evaluation mode to cross-validation
m_CVBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to cross-validation
m_CVLab - Variable in class weka.gui.explorer.AttributeSelectionPanel
Label by where the cv folds are entered
m_CVLab - Variable in class weka.gui.explorer.ClassifierPanel
Label by where the cv folds are entered
m_CVParams - Variable in class weka.classifiers.meta.CVParameterSelection
The set of parameters to cross-validate over
m_CVText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The field where the cv folds are entered
m_CVText - Variable in class weka.gui.explorer.ClassifierPanel
The field where the cv folds are entered
m_Cache - Variable in class weka.classifiers.lazy.kstar.KStar
A custom data structure for caching distinct attribute values and their scale factor or stop parameter.
m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
A cache for storing attribute values and their corresponding stop parameters
m_Cache - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
A cache for storing attribute values and their corresponding scale parameters
m_Cache - Variable in class weka.experiment.DatabaseResultListener
Stores the cached values
m_CacheKey - Variable in class weka.experiment.DatabaseResultListener
Stores the key for which the cache is valid
m_CacheKeyIndex - Variable in class weka.experiment.DatabaseResultListener
Stores the index of the key column holding the cache key data
m_CacheKeyName - Variable in class weka.experiment.DatabaseResultListener
Holds the name of the key field to cache upon, or null if no caching
m_CalculateStdDevs - Variable in class weka.experiment.AveragingResultProducer
True if standard deviation fields should be produced
m_CancelBut - Variable in class weka.gui.ListSelectorDialog
Click to cancel the property selection
m_CancelBut - Variable in class weka.gui.PropertySelectorDialog
Click to cancel the property selection
m_CannotLinkWeight - Variable in class weka.clusterers.MPCKMeans
weight to be given to each constraint
m_CannotLinkWeight - Variable in class weka.clusterers.PCKMeans
weight to be given to each constraint
m_CannotLinkWeight - Variable in class weka.clusterers.PCSoftKMeans
weight to be given to each constraint
m_ChildPropertySheet - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The component that performs classifier customization
m_ClassAttribute - Variable in class weka.classifiers.meta.LogitBoost
The actual class attribute (for getting class names)
m_ClassAttribute - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The actual class attribute (for getting class names)
m_ClassCombo - Variable in class weka.gui.explorer.AttributeSelectionPanel
Lets the user select the class column
m_ClassCombo - Variable in class weka.gui.explorer.ClassifierPanel
Lets the user select the class column
m_ClassCombo - Variable in class weka.gui.explorer.ClustererPanel
Lets the user select the class column for classes to clusters based evaluation
m_ClassDistribution - Variable in class weka.classifiers.bayes.NaiveBayes
The class estimator.
m_ClassDistribution - Variable in class weka.core.SoftClassifiedFullInstance
An array of probabilities giving the probability of each class for this Instance
m_ClassDistribution - Variable in class weka.core.SoftClassifiedSparseInstance
An array of probabilities giving the probability of each class for this Instance
m_ClassFirst - Variable in class weka.experiment.Experiment
True if the class attribute is the first attribute for all datasets involved in this experiment.
m_ClassFirst - Variable in class weka.gui.experiment.Experimenter
True if the class attribute is the first attribute for all datasets involved in this experiment.
m_ClassIndex - Variable in class weka.classifiers.BVDecompose
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.RegressionBVDecompose
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.lazy.LBR.Indexes
the Class Index for the data set
m_ClassIndex - Variable in class weka.classifiers.misc.HyperPipes
The index of the class attribute
m_ClassIndex - Variable in class weka.classifiers.misc.VFI
The index of the class attribute
m_ClassIndex - Variable in class weka.core.Instances
The class attribute's index
m_ClassIsNominal - Variable in class weka.clusterers.SemiSupClustererEvaluation
Is the class nominal or numeric?
m_ClassMeans - Variable in class weka.classifiers.meta.RegressionByDiscretization
The mean values for each Discretized class interval.
m_ClassMode - Variable in class weka.classifiers.meta.ThresholdSelector
Method to determine which class to optimize for
m_ClassNameLabel - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The name of the current class
m_ClassNames - Variable in class weka.classifiers.evaluation.ConfusionMatrix
Stores the names of the classes
m_ClassNames - Variable in class weka.clusterers.SemiSupClustererEvaluation
The names of the classes.
m_ClassProbs - Variable in class weka.classifiers.trees.REPTree.Tree
Class probabilities from the training data in the nominal case.
m_ClassProbs - Variable in class weka.classifiers.trees.RandomTree
Class probabilities from the training data.
m_ClassType - Variable in class weka.classifiers.lazy.IBk
The class attribute type
m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStar
The class attribute type
m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The class attribute type
m_ClassType - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The class attribute type
m_ClassType - Variable in class weka.classifiers.sparse.IBkMetric
The class attribute type
m_ClassType - Variable in class weka.gui.GenericObjectEditor
The Class of objects being edited
m_ClassesToClustersBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to classes to clusters based evaluation
m_Classifier - Variable in class weka.classifiers.BVDecompose
An instantiated base classifier used for getting and testing options.
m_Classifier - Variable in class weka.classifiers.CheckClassifier
The classifier to be examined
m_Classifier - Variable in class weka.classifiers.RegressionBVDecompose
An instantiated base classifier used for getting and testing options.
m_Classifier - Variable in class weka.classifiers.bayes.SemiSupEM
Base classifier that supports soft classified instances
m_Classifier - Variable in class weka.classifiers.meta.ActiveDecorate
The model base classifier to use.
m_Classifier - Variable in class weka.classifiers.meta.AdaBoostM1
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.AdditiveRegression
Base classifier.
m_Classifier - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The classifier
m_Classifier - Variable in class weka.classifiers.meta.Bagging
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.CVParameterSelection
The generated base classifier
m_Classifier - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The classifier
m_Classifier - Variable in class weka.classifiers.meta.Crate
The model base classifier to use.
m_Classifier - Variable in class weka.classifiers.meta.DEC
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.Decorate
The model base classifier to use.
m_Classifier - Variable in class weka.classifiers.meta.Fable
The model base classifier to use.
m_Classifier - Variable in class weka.classifiers.meta.FilteredClassifier
The classifier
m_Classifier - Variable in class weka.classifiers.meta.LogitBoost
An instantiated base classifier used for getting and testing options
m_Classifier - Variable in class weka.classifiers.meta.MetaCost
The classifier
m_Classifier - Variable in class weka.classifiers.meta.MultiBoostAB
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.MultiScheme
The classifier that had the best performance on training data.
m_Classifier - Variable in class weka.classifiers.meta.QBag
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.QBoost
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.RegressionByDiscretization
The subclassifier.
m_Classifier - Variable in class weka.classifiers.meta.SemiSupDecorate
The model base classifier to use
m_Classifier - Variable in class weka.classifiers.meta.ThresholdSelector
The generated base classifier
m_Classifier - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier used for evaluation
m_Classifier - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier used for evaluation
m_ClassifierEditor - Variable in class weka.gui.experiment.AlgorithmListPanel
Lets the user configure the classifier
m_ClassifierEditor - Variable in class weka.gui.explorer.ClassifierPanel
Lets the user configure the classifier
m_ClassifierIndex - Variable in class weka.classifiers.meta.MultiScheme
The index into the vector for the selected scheme
m_ClassifierOptions - Variable in class weka.classifiers.BVDecompose
The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.CheckClassifier
The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.RegressionBVDecompose
The options to be passed to the base classifier.
m_ClassifierOptions - Variable in class weka.classifiers.meta.CVParameterSelection
The base classifier options (not including those being set by cross-validation)
m_ClassifierOptions - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier options (if any)
m_ClassifierOptions - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier options (if any)
m_ClassifierPanel - Variable in class weka.gui.explorer.Explorer
The panel for running classifiers
m_ClassifierVersion - Variable in class weka.experiment.ClassifierSplitEvaluator
The classifier version
m_ClassifierVersion - Variable in class weka.experiment.RegressionSplitEvaluator
The classifier version
m_Classifiers - Variable in class weka.classifiers.meta.AdaBoostM1
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.Bagging
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.LogitBoost
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.MultiBoostAB
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.MultiScheme
The list of classifiers
m_Classifiers - Variable in class weka.classifiers.meta.QBag
Array for storing the generated base classifiers.
m_Classifiers - Variable in class weka.classifiers.meta.QBoost
Array for storing the generated base classifiers.
m_ClusterAssignments - Variable in class weka.clusterers.MPCKMeans
temporary variable holding cluster assignments while iterating
m_ClusterAssignments - Variable in class weka.clusterers.PCKMeans
temporary variable holding cluster assignments while iterating
m_ClusterAssignments - Variable in class weka.clusterers.PCSoftKMeans
temporary variable holding cluster assignments while iterating
m_ClusterAssignments - Variable in class weka.clusterers.SeededKMeans
temporary variable holding cluster assignments while iterating
m_ClusterCentroids - Variable in class weka.clusterers.FarthestFirst
holds the cluster centroids
m_ClusterCentroids - Variable in class weka.clusterers.MPCKMeans
holds the cluster centroids
m_ClusterCentroids - Variable in class weka.clusterers.PCKMeans
holds the cluster centroids
m_ClusterCentroids - Variable in class weka.clusterers.PCSoftKMeans
holds the cluster centroids
m_ClusterCentroids - Variable in class weka.clusterers.SeededKMeans
holds the cluster centroids
m_ClusterDistribution - Variable in class weka.clusterers.PCSoftKMeans
temporary variable holding posterior cluster distribution of points while iterating
m_Clusterer - Variable in class weka.experiment.SemiSupClustererSplitEvaluator
The semi-supervised clusterer used for evaluation
m_Clusterer - Variable in class weka.filters.unsupervised.attribute.AddCluster
The clusterer used to do the cleansing
m_ClustererEditor - Variable in class weka.gui.explorer.ClustererPanel
Lets the user configure the clusterer
m_ClustererOptions - Variable in class weka.experiment.SemiSupClustererSplitEvaluator
The clusterer options (if any)
m_ClustererPanel - Variable in class weka.gui.explorer.Explorer
Label for a panel that still need to be implemented
m_ClustererVersion - Variable in class weka.experiment.SemiSupClustererSplitEvaluator
The clusterer version
m_Clusters - Variable in class weka.clusterers.MPCKMeans
holds the instances in the clusters
m_Clusters - Variable in class weka.clusterers.PCKMeans
holds the instances in the clusters
m_Clusters - Variable in class weka.clusterers.PCSoftKMeans
holds the instances in the clusters
m_Cnsqt - Variable in class weka.classifiers.rules.ConjunctiveRule
The consequent of this rule
m_ColourCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute to use for colouring
m_CommandHistory - Variable in class weka.gui.SimpleCLI
The history of commands entered interactively
m_Committee - Variable in class weka.classifiers.meta.ActiveDecorate
Vector of classifiers that make up the committee/ensemble.
m_Committee - Variable in class weka.classifiers.meta.Crate
Vector of classifiers that make up the committee/ensemble.
m_Committee - Variable in class weka.classifiers.meta.Decorate
Vector of classifiers that make up the committee/ensemble.
m_Committee - Variable in class weka.classifiers.meta.Fable
Vector of classifiers that make up the committee/ensemble.
m_CompareCombo - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which performance measure to analyze
m_CompareModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_CompareCombo
m_ComputeRandomCols - Variable in class weka.classifiers.lazy.kstar.KStar
Flag turning on and off the computation of random class colomns
m_Concentration - Variable in class weka.clusterers.SeededKMeans
weight of the concentration
m_ConfigureBut - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Click to select the property to iterate over
m_ConfigureListener - Variable in class weka.gui.experiment.ResultsPanel
An actionlisteners that updates ttest settings
m_ConfusionMatrix - Variable in class weka.clusterers.SemiSupClustererEvaluation
Array for storing the confusion matrix.
m_ConfusionMatrix - Variable in class weka.deduping.DedupingEvaluation
Array for storing the confusion matrix.
m_Connection - Variable in class weka.experiment.DatabaseUtils
The database connection
m_ConstraintsHash - Variable in class weka.clusterers.MPCKMeans
holds the ([instance pair] -> [type of constraint]) mapping
m_ConstraintsHash - Variable in class weka.clusterers.PCKMeans
holds the ([instance pair] -> [type of constraint]) mapping.
m_ConstraintsHash - Variable in class weka.clusterers.PCSoftKMeans
holds the ([instance pair] -> [type of constraint]) mapping.
m_Contents - Variable in class weka.core.Queue.QueueNode
The nodes contents
m_CopyCols - Variable in class weka.filters.unsupervised.attribute.Copy
Stores which columns to copy
m_CostFile - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The name of the cost file, for command line options
m_CostFile - Variable in class weka.classifiers.meta.MetaCost
The name of the cost file, for command line options
m_CostMatrix - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The cost matrix
m_CostMatrix - Variable in class weka.classifiers.meta.MetaCost
The cost matrix
m_CostMatrixEditor - Variable in class weka.gui.explorer.ClassifierPanel
The cost matrix editor for evaluation costs
m_CountFieldName - Variable in class weka.experiment.AveragingResultProducer
The name of the field that will contain the number of results averaged over.
m_Counts - Variable in class weka.classifiers.bayes.NaiveBayesSimple
All the counts for nominal attributes.
m_Counts - Variable in class weka.classifiers.lazy.LBR
All the counts for nominal attributes.
m_Counts - Variable in class weka.classifiers.misc.Prototype
All the counts for nominal attributes.
m_CrossValidate - Variable in class weka.classifiers.lazy.IBk
Whether to select k by cross validation
m_CrossValidate - Variable in class weka.classifiers.sparse.IBkMetric
Whether to select k by cross validation
m_CurrDebugFlag - Variable in class weka.clusterers.XMeans
 
m_CurrentInstances - Variable in class weka.experiment.Experiment
The dataset currently being used
m_CurrentProperty - Variable in class weka.experiment.Experiment
The custom property value that has actually been set
m_CurrentSeedInstances - Variable in class weka.clusterers.Seeder
Stores the current instances which are set as seeds
m_CurrentSize - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The current dataset size during stepping
m_CurrentSize - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The current dataset size during stepping
m_CurrentSize - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The current dataset size during stepping
m_CurrentSize - Variable in class weka.experiment.LearningRateResultProducer
The current dataset size during stepping
m_CurrentSize - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Dataset size for the runs, we take the full dataset
m_CurrentSize - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The current dataset size during stepping
m_CurrentSize - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The current labeled training dataset size (as fraction of totalSize)
m_CurrentSize - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The current dataset size during stepping
m_CurrentSize - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The current dataset size during stepping
m_CurrentSize - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The current dataset size during stepping
m_CurrentVis - Variable in class weka.gui.explorer.AttributeSelectionPanel
The current visualization object
m_CurrentVis - Variable in class weka.gui.explorer.ClassifierPanel
The current visualization object
m_CurrentVis - Variable in class weka.gui.explorer.ClustererPanel
The current visualization object
m_CutPoints - Variable in class weka.filters.supervised.attribute.Discretize
Store the current cutpoints
m_CutPoints - Variable in class weka.filters.unsupervised.attribute.Discretize
Store the current cutpoints
m_DataCreationMethod - Variable in class weka.classifiers.meta.DEC
Method to use for creation of artificial data
m_DataCreationMethod - Variable in class weka.classifiers.meta.SemiSupDecorate
Method to use for creation of artificial data
m_DataFileName - Variable in class weka.classifiers.BVDecompose
The name of the data file used for the decomposition
m_DataFileName - Variable in class weka.classifiers.RegressionBVDecompose
The name of the data file used for the decomposition
m_DatabaseQueryEditor - Variable in class weka.gui.explorer.PreprocessPanel
Lets the user enter a DB query
m_DatabaseURL - Variable in class weka.experiment.DatabaseUtils
Database URL
m_Dataset - Variable in class weka.core.Instance
The dataset the instance has access to.
m_Dataset - Variable in class weka.core.converters.SerializedInstancesLoader
Holds the structure (header) of the data set.
m_DatasetKeyBut - Variable in class weka.gui.experiment.ResultsPanel
Click to edit the columns used to determine the scheme
m_DatasetKeyColumns - Variable in class weka.experiment.PairedTTester
An array containing the indexes of just the selected columns
m_DatasetKeyColumnsRange - Variable in class weka.experiment.PairedTTester
The range of columns that specify a unique "dataset" (eg: scheme plus configuration)
m_DatasetKeyLabel - Variable in class weka.gui.experiment.ResultsPanel
Displays the currently selected column names for the scheme & options
m_DatasetKeyList - Variable in class weka.gui.experiment.ResultsPanel
Displays the list of selected columns determining the scheme
m_DatasetKeyModel - Variable in class weka.gui.experiment.ResultsPanel
Stores the list of attributes for selecting the scheme columns
m_DatasetListPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for configuring selected datasets
m_DatasetListPanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel for configuring selected datasets
m_DatasetModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_DatasetCombo
m_DatasetNumber - Variable in class weka.experiment.Experiment
The current dataset number when the experiment is running
m_DatasetSpecifiers - Variable in class weka.experiment.PairedTTester
The list of dataset specifiers
m_Datasets - Variable in class weka.experiment.Experiment
An array of dataset files
m_Debug - Variable in class weka.classifiers.BVDecompose
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.CheckClassifier
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.RegressionBVDecompose
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.lazy.LWR
True if debugging output should be printed
m_Debug - Variable in class weka.classifiers.meta.ActiveDecorate
Set to true to get debugging output.
m_Debug - Variable in class weka.classifiers.meta.AdaBoostM1
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.meta.CVParameterSelection
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.meta.Crate
Set to true to get debugging output.
m_Debug - Variable in class weka.classifiers.meta.Decorate
Set to true to get debugging output.
m_Debug - Variable in class weka.classifiers.meta.Fable
Set to true to get debugging output.
m_Debug - Variable in class weka.classifiers.meta.LogitBoost
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.meta.MultiBoostAB
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.meta.MultiScheme
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.meta.QBag
Set to true to get debugging output.
m_Debug - Variable in class weka.classifiers.meta.QBoost
Debugging mode, gives extra output if true
m_Debug - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Whether to output debug messages
m_Debug - Variable in class weka.classifiers.meta.RegressionByDiscretization
Whether debugging output will be printed
m_Debug - Variable in class weka.classifiers.trees.RandomTree
Debug info
m_Debug - Static variable in class weka.core.Optimization
 
m_Debug - Variable in class weka.experiment.DatabaseResultListener
True if debugging output should be printed
m_Debug - Variable in class weka.experiment.DatabaseUtils
True if debugging output should be printed
m_DefDstr - Variable in class weka.classifiers.rules.ConjunctiveRule
The default rule distribution of the data not covered
m_DefaultColors - Variable in class weka.gui.visualize.AttributePanel
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.ClassPanel
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.Plot2D
default colours for colouring discrete class
m_DefaultColors - Variable in class weka.gui.visualize.VisualizePanel
default colours for colouring discrete class
m_DefaultPerturb - Variable in class weka.clusterers.MPCKMeans
holds the default perturbation value for randomPerturbInit
m_DefaultPerturb - Variable in class weka.clusterers.PCKMeans
holds the default perturbation value for randomPerturbInit
m_DefaultPerturb - Variable in class weka.clusterers.PCSoftKMeans
holds the default perturbation value for randomPerturbInit
m_DefaultPerturb - Variable in class weka.clusterers.SeededKMeans
holds the default perturbation value for randomPerturbInit
m_DeleteBut - Variable in class weka.gui.experiment.AlgorithmListPanel
Click to remove the selected dataset from the list
m_DeleteBut - Variable in class weka.gui.experiment.DatasetListPanel
Click to remove the selected dataset from the list
m_DeleteBut - Variable in class weka.gui.experiment.HostListPanel
Click to remove the selected host from the list
m_DeltaCols - Variable in class weka.filters.unsupervised.attribute.FirstOrder
Stores which columns to take differences between
m_Description - Variable in class weka.gui.ExtensionFileFilter
The text description of the types of files accepted
m_DesignatedClass - Variable in class weka.classifiers.meta.ThresholdSelector
Designated class value, determined during building
m_DesiredSize - Variable in class weka.classifiers.meta.ActiveDecorate
The desired ensemble size.
m_DesiredSize - Variable in class weka.classifiers.meta.Crate
The desired ensemble size.
m_DesiredSize - Variable in class weka.classifiers.meta.DEC
The number of iterations.
m_DesiredSize - Variable in class weka.classifiers.meta.Decorate
The desired ensemble size.
m_DesiredSize - Variable in class weka.classifiers.meta.Fable
The desired ensemble size.
m_DesiredSize - Variable in class weka.classifiers.meta.SemiSupDecorate
The number of iterations.
m_DestFileChooser - Variable in class weka.gui.experiment.SimpleSetupPanel
The file chooser for selecting result destinations
m_Devs - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The standard deviations for numeric attributes.
m_DiscretizeCols - Variable in class weka.filters.supervised.attribute.Discretize
Stores which columns to Discretize
m_DiscretizeCols - Variable in class weka.filters.unsupervised.attribute.Discretize
Stores which columns to Discretize
m_Discretizer - Variable in class weka.classifiers.meta.RegressionByDiscretization
The discretization filter.
m_Distance - Variable in class weka.classifiers.sparse.IBkMetric.NeighborNode
The distance from the current instance to this neighbor
m_DistanceF - Variable in class weka.classifiers.lazy.IBk
Distance functions
m_DistanceWeighting - Variable in class weka.classifiers.lazy.IBk
Whether the neighbours should be distance-weighted
m_DistanceWeighting - Variable in class weka.classifiers.sparse.IBkMetric
Whether the neighbours should be distance-weighted
m_Distances - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The set of disctances from the test attribute to the set of train attributes
m_DistinctLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of distinct values
m_DistributeExperimentPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for enabling a distributed experiment
m_Distribution - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Distribution of the attribute value in the train dataset
m_Distribution - Variable in class weka.classifiers.trees.REPTree.Tree
The (unnormalized) class distribution in the nominal case.
m_Distribution - Variable in class weka.classifiers.trees.RandomTree
The class distribution from the training data.
m_Distributions - Variable in class weka.classifiers.bayes.BayesNet
The attribute estimators containing CPTs.
m_Distributions - Variable in class weka.classifiers.bayes.NaiveBayes
The attribute estimators.
m_DoActive - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Whether active learning is to be performed
m_DoActive - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Whether active learning is to be performed
m_DontNormalize - Variable in class weka.classifiers.lazy.IBk
True if normalization is turned off
m_EPSILON - Variable in class weka.classifiers.sparse.IBkMetric
Small value to be used instead of 0 in converting from distances to similarities
m_EditorComponent - Variable in class weka.gui.GenericObjectEditor
The GUI component for editing values, created when needed
m_Elements - Variable in class weka.clusterers.AlgVector
The values of the matrix
m_Elements - Variable in class weka.core.AlgVector
The values of the matrix
m_Elements - Variable in class weka.core.Matrix
The values of the matrix
m_Enabled - Variable in class weka.gui.GenericObjectEditor
True if the GUI component is needed
m_EnsembleWts - Variable in class weka.classifiers.EnsembleClassifier
Vote weights of ensemble members
m_Entropy - Variable in class weka.clusterers.SemiSupClustererEvaluation
Entropy of the clustering
m_Epsilon - Variable in class weka.classifiers.meta.ActiveDecorate
Smoothing parameter for 0-values in distributions
m_Epsilon - Variable in class weka.classifiers.meta.Fable
Smoothing parameter for 0-values in distributions
m_Epsilon - Static variable in class weka.core.Optimization
 
m_ErrRedirector - Variable in class weka.gui.SimpleCLI
The thread that sends output from m_POE to the output box
m_Error - Variable in class weka.classifiers.BVDecompose
The error rate
m_Error - Variable in class weka.classifiers.RegressionBVDecompose
The error rate
m_ErrorCompareCol - Variable in class weka.gui.experiment.ResultsPanel
 
m_ErrorFlags - Variable in class weka.classifiers.lazy.LBR
 
m_ErrorMeasure - Variable in class weka.classifiers.meta.Crate
Error measure to optimize for
m_Errors - Variable in class weka.classifiers.lazy.LBR
 
m_EvalMode - Variable in class weka.classifiers.meta.ThresholdSelector
The evaluation mode
m_EvalWRTCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to evaluate w.r.t a cost matrix
m_Evaluation - Variable in class weka.classifiers.meta.Crate
Evaluator
m_Evaluator - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The attribute evaluator to use
m_Exp - Variable in class weka.gui.experiment.AlgorithmListPanel
The experiment to set the algorithm list of
m_Exp - Variable in class weka.gui.experiment.DatasetListPanel
The experiment to set the dataset list of
m_Exp - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
The experiment this all applies to
m_Exp - Variable in class weka.gui.experiment.HostListPanel
The remote experiment to set the host list of
m_Exp - Variable in class weka.gui.experiment.ResultsPanel
An experiment (used for identifying a result source) -- optional
m_Exp - Variable in class weka.gui.experiment.RunNumberPanel
The experiment being configured
m_Exp - Variable in class weka.gui.experiment.RunPanel
The experiment to run
m_Exp - Variable in class weka.gui.experiment.SetupPanel
The experiment being configured
m_Exp - Variable in class weka.gui.experiment.SimpleSetupPanel
The experiment being configured
m_ExpClassificationRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing classification experiment
m_ExpFilter - Variable in class weka.gui.experiment.SetupPanel
A filter to ensure only experiment files get shown in the chooser
m_ExpFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
A filter to ensure only experiment files get shown in the chooser
m_ExpRegressionRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing regression experiment
m_ExpectedResultsPerAverage - Variable in class weka.experiment.AveragingResultProducer
The number of results expected to average over for each run
m_ExperimentParameterLabel - Variable in class weka.gui.experiment.SimpleSetupPanel
Label for parameter field
m_ExperimentParameterTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for experiment parameter
m_ExperimentTypeCBox - Variable in class weka.gui.experiment.SimpleSetupPanel
Combo box for choosing experiment type
m_ExperimenterBut - Variable in class weka.gui.GUIChooser
Click to open the Explorer
m_ExperimenterFrame - Variable in class weka.gui.GUIChooser
The frame containing the experiment interface
m_ExplorerBut - Variable in class weka.gui.GUIChooser
Click to open the Explorer
m_ExplorerFrame - Variable in class weka.gui.GUIChooser
The frame containing the explorer interface
m_Extension - Variable in class weka.gui.ExtensionFileFilter
The filename extension of accepted files
m_ExtraPhase1RunFraction - Variable in class weka.clusterers.SeededKMeans
number of extra phase1 runs
m_FastMode - Variable in class weka.clusterers.MPCKMeans
m_FastMode = true => fast computation of meanOrMode in centroid calculation, useful for high-D data sets m_FastMode = false => usual computation of meanOrMode in centroid calculation
m_FastMode - Variable in class weka.clusterers.PCKMeans
m_FastMode = true => fast computation of meanOrMode in centroid calculation, useful for high-D data sets m_FastMode = false => usual computation of meanOrMode in centroid calculation
m_FastMode - Variable in class weka.clusterers.PCSoftKMeans
m_FastMode = true => fast computation of meanOrMode in centroid calculation, useful for high-D data sets m_FastMode = false => usual computation of meanOrMode in centroid calculation
m_FastMode - Variable in class weka.clusterers.SeededKMeans
m_FastMode = true => fast computation of meanOrMode in centroid calculation, useful for high-D data sets m_FastMode = false => usual computation of meanOrMode in centroid calculation
m_File - Variable in class weka.core.converters.ArffLoader
 
m_FileChooser - Variable in class weka.gui.FileEditor
The file chooser used for selecting files
m_FileChooser - Variable in class weka.gui.GenericObjectEditor.GOEPanel
The filechooser for opening and saving object files
m_FileChooser - Variable in class weka.gui.SetInstancesPanel
The file chooser for selecting arff files
m_FileChooser - Variable in class weka.gui.beans.KnowledgeFlow
The file chooser for selecting layout files
m_FileChooser - Variable in class weka.gui.experiment.DatasetListPanel
The file chooser component
m_FileChooser - Variable in class weka.gui.experiment.ResultsPanel
The file chooser for selecting result files
m_FileChooser - Variable in class weka.gui.experiment.SetupPanel
The file chooser for selecting experiments
m_FileChooser - Variable in class weka.gui.experiment.SimpleSetupPanel
The file chooser for selecting experiments
m_FileChooser - Variable in class weka.gui.explorer.ClassifierPanel
The file chooser for selecting model files
m_FileChooser - Variable in class weka.gui.explorer.ClustererPanel
The file chooser for selecting model files
m_FileChooser - Variable in class weka.gui.explorer.PreprocessPanel
The file chooser for selecting arff files
m_FileChooser - Variable in class weka.gui.visualize.VisualizePanel
file chooser for saving instances
m_FillWithMissing - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
True if missing values should be used rather than removing instances where the translated value is not known (due to border effects).
m_Filter - Variable in class weka.classifiers.meta.FilteredClassifier
The filter
m_FilterEditor - Variable in class weka.gui.explorer.PreprocessPanel
Lets the user configure the filter
m_FilterPanel - Variable in class weka.gui.explorer.PreprocessPanel
Filter configuration
m_FilteredInstances - Variable in class weka.classifiers.meta.FilteredClassifier
The instance structure of the filtered instances
m_FinalClusters - Variable in class weka.clusterers.SeededKMeans
holds the clusters
m_FindNumBins - Variable in class weka.filters.unsupervised.attribute.Discretize
Find the number of bins using cross-validated entropy.
m_Finished - Variable in class weka.experiment.Experiment
True if the experiment has finished running
m_First - Variable in class weka.classifiers.sparse.IBkMetric.NeighborList
The first node in the list
m_First - Variable in class weka.gui.LogPanel
An indicator for whether text has been output yet
m_Fraction - Variable in class weka.experiment.PairedTTester
Flag to indicate whether learning curves are specified by fraction
m_FramedOutput - Variable in class weka.gui.ResultHistoryPanel
A Hashtable mapping names to output text components
m_FromDBaseBut - Variable in class weka.gui.experiment.ResultsPanel
Click to load results from a database
m_FromExpBut - Variable in class weka.gui.experiment.ResultsPanel
Click to get results from the destination given in the experiment
m_FromFileBut - Variable in class weka.gui.experiment.ResultsPanel
Click to load results from a file
m_FromLab - Variable in class weka.gui.experiment.ResultsPanel
Displays a message about the current result set
m_GeneratorPropertyPanel - Variable in class weka.gui.experiment.SetupPanel
The panel that configures iteration on custom resultproducer property
m_GlobalBlend - Variable in class weka.classifiers.lazy.kstar.KStar
default sphere of influence blend setting
m_GlobalCentroid - Variable in class weka.clusterers.MPCKMeans
holds the global centroids
m_GlobalCentroid - Variable in class weka.clusterers.PCKMeans
holds the global centroids
m_GlobalCentroid - Variable in class weka.clusterers.PCSoftKMeans
holds the global centroids
m_GlobalCentroid - Variable in class weka.clusterers.SeededKMeans
holds the global centroids
m_HandleRightClicks - Variable in class weka.gui.ResultHistoryPanel
Let the result history list handle right clicks in the default manner---ie, pop up a window displaying the buffer
m_HardVoteAssignment - Variable in class weka.classifiers.meta.QBag
Set true to use hard assignment for ensemble member votes
m_Head - Variable in class weka.core.Queue
Store a reference to the head of the queue
m_HighThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The upper threshold used as the basis of correction
m_History - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Stores the historical instances to copy values between
m_History - Variable in class weka.gui.experiment.ResultsPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.AssociationsPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.AttributeSelectionPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.ClassifierPanel
A panel controlling results viewing
m_History - Variable in class weka.gui.explorer.ClustererPanel
A panel controlling results viewing
m_HistoryPos - Variable in class weka.gui.SimpleCLI
The current position in the command history
m_HoldOutDist - Variable in class weka.classifiers.trees.REPTree.Tree
Class distribution of hold-out set at node in the nominal case.
m_HoldOutError - Variable in class weka.classifiers.trees.REPTree.Tree
The hold-out error of the node.
m_HostField - Variable in class weka.gui.experiment.HostListPanel
The field with which to enter host names
m_HyperPipes - Variable in class weka.classifiers.misc.HyperPipes
Stores the HyperPipe for each class
m_ICAMFile - Variable in class weka.attributeSelection.MatlabICA
Name of the Matlab program file that computes ICA
m_ICAapproach - Variable in class weka.attributeSelection.MatlabICA
 
m_ICAfunction - Variable in class weka.attributeSelection.MatlabICA
 
m_ID - Variable in class weka.core.Tag
The ID
m_IOThread - Variable in class weka.gui.SetInstancesPanel
The thread we do loading in
m_IOThread - Variable in class weka.gui.explorer.PreprocessPanel
A thread for loading/saving instances from a file or URL
m_IgnoreAttributesRange - Variable in class weka.filters.unsupervised.attribute.AddCluster
Range of attributes to ignore
m_IncludeAll - Variable in class weka.gui.AttributeSelectionPanel
Press to select all attributes
m_IncrementalIndex - Variable in class weka.core.converters.SerializedInstancesLoader
The current index position for incremental reading
m_IndexClusters - Variable in class weka.clusterers.MPCKMeans
holds the instance indices in the clusters
m_IndexClusters - Variable in class weka.clusterers.PCKMeans
holds the instance indices in the clusters
m_IndexClusters - Variable in class weka.clusterers.PCSoftKMeans
holds the instance indices in the clusters, mapped to their probabilities
m_IndexClusters - Variable in class weka.clusterers.SeededKMeans
holds the instance indices in the clusters
m_Indices - Variable in class weka.core.SparseInstance
The index of the attribute associated with each stored value.
m_IndicesBuffer - Variable in class weka.core.Instances
Buffer of indices for sparse instance
m_Info - Variable in class weka.classifiers.trees.REPTree.Tree
The header information (for printing the tree).
m_Info - Variable in class weka.classifiers.trees.RandomTree
The header information.
m_InitFlag - Variable in class weka.classifiers.lazy.kstar.KStar
Flag turning on and off the initialisation of config variables
m_Input - Variable in class weka.gui.SimpleCLI
The command input area
m_InputFormat - Variable in class weka.gui.streams.InstanceJoiner
The input format for instances
m_InputStringIndex - Variable in class weka.filters.unsupervised.attribute.Copy
Contains an index of string attributes in the input format that survive the filtering process -- some entries may be duplicated
m_InputStringIndex - Variable in class weka.filters.unsupervised.attribute.Remove
Contains an index of string attributes in the input format that will survive the filtering process
m_Insert - Variable in class weka.filters.unsupervised.attribute.Add
The location to insert the new attribute
m_InstIndexes - Variable in class weka.classifiers.lazy.LBR.Indexes
the array instance indexes
m_InstSummaryPanel - Variable in class weka.gui.explorer.PreprocessPanel
Displays simple stats on the working instances
m_Instance - Variable in class weka.classifiers.sparse.IBkMetric.NeighborNode
The neighbor instance
m_InstanceOrdering - Variable in class weka.clusterers.PCKMeans
 
m_InstanceQuery - Variable in class weka.gui.experiment.ResultsPanel
Does any database querying for us
m_InstanceRange - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
The number of instances forward to translate values between.
m_Instances - Variable in class weka.classifiers.bayes.ADNode
list of Instance children (either m_Instances or m_VaryNodes is instantiated)
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.NaiveBayes
The dataset header for the purposes of printing out a semi-intelligible model
m_Instances - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The instances used for training.
m_Instances - Variable in class weka.classifiers.lazy.LBR
The set of instances used for current training.
m_Instances - Variable in class weka.classifiers.misc.HyperPipes
The structure of the training data
m_Instances - Variable in class weka.classifiers.misc.Prototype
The instances used for training.
m_Instances - Variable in class weka.classifiers.misc.PrototypeMetric
The instances used for training.
m_Instances - Variable in class weka.classifiers.misc.VFI
The training data
m_Instances - Variable in class weka.clusterers.MPCKMeans
training instances
m_Instances - Variable in class weka.clusterers.PCKMeans
training instances
m_Instances - Variable in class weka.clusterers.PCSoftKMeans
training instances
m_Instances - Variable in class weka.clusterers.SeededKMeans
training instances
m_Instances - Variable in class weka.core.Instances
The instances.
m_Instances - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.AveragingResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.CrossValidationResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.DatabaseResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.InstancesResultListener
Stores the instances created so far, before assigning to a header
m_Instances - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.LearningRateResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.PairedTTester
The set of instances we will analyse
m_Instances - Variable in class weka.experiment.RandomSplitResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The dataset of interest
m_Instances - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The dataset of interest
m_Instances - Variable in class weka.gui.AttributeSummaryPanel
The instances we're playing with
m_Instances - Variable in class weka.gui.InstancesSummaryPanel
The instances we're playing with
m_Instances - Variable in class weka.gui.SetInstancesPanel
The current set of instances loaded
m_Instances - Variable in class weka.gui.experiment.ResultsPanel
The instances we're extracting results from
m_Instances - Variable in class weka.gui.explorer.AssociationsPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.AttributeSelectionPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.ClassifierPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.ClustererPanel
The main set of instances we're playing with
m_Instances - Variable in class weka.gui.explorer.PreprocessPanel
The working instances
m_Inverse - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Inverse of test to be used?
m_Invert - Variable in class weka.gui.AttributeSelectionPanel
Press to invert the current selection
m_IsFraction - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
m_IsFraction - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
 
m_IsFraction - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
 
m_IsFraction - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
 
m_IsFraction - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
m_IsFraction - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
m_IsFraction - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
m_IsFraction - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
m_IsRandomNeighborhoods - Variable in class weka.clusterers.MPCKMeans
indicates whether initialization is using random neighborhoods -- in the case of offline metric
m_IsTransductive - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Whether transductive evaluation is to be performed
m_IsTransductive - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
Whether transductive evaluation is to be performed
m_IsTransductive - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Whether transductive evaluation is to be performed
m_IsTransductive - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Whether transductive evaluation is to be performed
m_Iterations - Variable in class weka.clusterers.MPCKMeans
keep track of the number of iterations completed before convergence
m_Iterations - Variable in class weka.clusterers.PCKMeans
keep track of the number of iterations completed before convergence
m_Iterations - Variable in class weka.clusterers.PCSoftKMeans
keep track of the number of iterations completed before convergence
m_Iterations - Variable in class weka.clusterers.SeededKMeans
keep track of the number of iterations completed before convergence
m_Jitter - Variable in class weka.gui.visualize.VisualizePanel
The jitter slider
m_JitterLab - Variable in class weka.gui.visualize.VisualizePanel
Label for the jitter slider
m_KLDivergence - Variable in class weka.clusterers.SemiSupClustererEvaluation
KL Divergence of the clustering
m_KValue - Variable in class weka.classifiers.trees.RandomForest
Final number of features that were considered in last build.
m_KValue - Variable in class weka.classifiers.trees.RandomTree
The number of attributes considered for a split.
m_Kappa - Variable in class weka.clusterers.PCSoftKMeans
kappa value for vmf distribution
m_KeyFieldName - Variable in class weka.experiment.AveragingResultProducer
The name of the key field to average over
m_KeyIndex - Variable in class weka.experiment.AveragingResultProducer
The index of the field to average over in the resultproducers key
m_Keys - Variable in class weka.experiment.AveragingResultProducer
Collects the keys from a single run
m_KnowledgeFlowBut - Variable in class weka.gui.GUIChooser
Click to open the KnowledgeFlow
m_KnowledgeFlowFrame - Variable in class weka.gui.GUIChooser
The frame containing the knowledge flow interface
m_LabeledInstances - Variable in class weka.classifiers.bayes.SemiSupEM
Hard Labeled data
m_LabeledTrain - Variable in class weka.clusterers.SemiSupClustererEvaluation
All labeled training instances
m_Labels - Variable in class weka.filters.unsupervised.attribute.Add
The list of labels for nominal attribute
m_Lambda - Variable in class weka.classifiers.bayes.SemiSupEM
Weight of unlabeled examples during EM training versus labeled examples (see Nigam et al.)
m_Last - Variable in class weka.classifiers.sparse.IBkMetric.NeighborList
The last node in the list
m_LastURL - Variable in class weka.gui.SetInstancesPanel
Stores the last URL that instances were loaded from
m_LastURL - Variable in class weka.gui.explorer.PreprocessPanel
Stores the last URL that instances were loaded from
m_LearningCurve - Variable in class weka.experiment.PairedTTester
Flag to indicate whether learning curves are to be produces
m_Length - Variable in class weka.classifiers.sparse.IBkMetric.NeighborList
The number of nodes to attempt to maintain in the list
m_List - Variable in class weka.gui.ListSelectorDialog
The list component
m_List - Variable in class weka.gui.ResultHistoryPanel
The list component
m_List - Variable in class weka.gui.experiment.AlgorithmListPanel
The component displaying the algorithm list
m_List - Variable in class weka.gui.experiment.DatasetListPanel
The component displaying the dataset list
m_List - Variable in class weka.gui.experiment.HostListPanel
The component displaying the host list
m_Listeners - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Listeners who want to be notified about editing status of this panel
m_Listeners - Variable in class weka.gui.visualize.AttributePanel
The list of things listening to this panel
m_LoadThread - Variable in class weka.gui.experiment.ResultsPanel
A thread to load results instances from a file or database
m_Log - Variable in class weka.gui.experiment.RunPanel
 
m_Log - Variable in class weka.gui.explorer.AssociationsPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.AttributeSelectionPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.ClassifierPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.ClustererPanel
The destination for log/status messages
m_Log - Variable in class weka.gui.explorer.PreprocessPanel
The message logger
m_Log - Variable in class weka.gui.visualize.VisualizePanel
the logger
m_LogPanel - Variable in class weka.gui.explorer.Explorer
The panel for log and status messages
m_LogText - Variable in class weka.gui.LogPanel
Displays the log messages
m_LowThreshold - Variable in class weka.classifiers.meta.ThresholdSelector
The lower threshold used as the basis of correction
m_LowerSize - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The minimum number of instances to use.
m_LowerSize - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The minimum number of instances to use.
m_LowerSize - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The minimum number of instances to use.
m_LowerSize - Variable in class weka.experiment.LearningRateResultProducer
The minimum number of instances to use.
m_LowerSize - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The minimum number of instances to use.
m_LowerSize - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The minimum number of labeled categories to drop.
m_LowerSize - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The minimum number of instances to use.
m_LowerSize - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The minimum number of instances to use.
m_LowerSize - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The minimum number of instances to use.
m_LowerText - Variable in class weka.gui.experiment.RunNumberPanel
Configures the lower run number
m_Ls - Variable in class weka.associations.Apriori
The set of all sets of itemsets L.
m_MAXITS - Variable in class weka.core.Optimization
 
m_MIMetric - Variable in class weka.clusterers.SemiSupClustererEvaluation
MI Metric the clustering
m_MakeBinary - Variable in class weka.filters.supervised.attribute.Discretize
Output binary attributes for discretized attributes.
m_MakeBinary - Variable in class weka.filters.unsupervised.attribute.Discretize
Output binary attributes for discretized attributes.
m_MatchMissingValues - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
True if missing values should count as a match
m_MatrixSource - Variable in class weka.classifiers.meta.CostSensitiveClassifier
Indicates the current cost matrix source
m_MatrixSource - Variable in class weka.classifiers.meta.MetaCost
Indicates the current cost matrix source
m_Max - Variable in class weka.classifiers.lazy.LWR
The maximum values for numeric attributes.
m_Max - Variable in class weka.classifiers.sparse.IBkMetric
The maximum values for numeric attributes.
m_MaxArray - Variable in class weka.classifiers.bayes.SemiSupEM
The maximum values for numeric attributes.
m_MaxCannotLinkDistance - Variable in class weka.clusterers.MPCKMeans
the maximum distance between cannot-link constraints
m_MaxCannotLinkDistances - Variable in class weka.clusterers.MPCKMeans
the maximum distance between cannot-link constraints
m_MaxCannotLinkPoints - Variable in class weka.clusterers.MPCKMeans
 
m_MaxCannotLinkSimilarity - Variable in class weka.clusterers.MPCKMeans
the min similarity between cannot-link constraints
m_MaxConstraintsAllowed - Static variable in class weka.clusterers.PCKMeans
the maximum number of cannot-link constraints allowed
m_MaxConstraintsAllowed - Static variable in class weka.clusterers.PCSoftKMeans
the maximum number of cannot-link constraints allowed
m_MaxDepth - Variable in class weka.classifiers.trees.REPTree
Upper bound on the tree depth
m_MaxIterations - Variable in class weka.classifiers.meta.AdaBoostM1
The maximum number of boost iterations
m_MaxIterations - Variable in class weka.classifiers.meta.LogitBoost
The maximum number of boost iterations
m_MaxIterations - Variable in class weka.classifiers.meta.MultiBoostAB
The maximum number of boost iterations
m_MaxIterations - Variable in class weka.classifiers.meta.QBoost
The maximum number of boost iterations
m_MaxKappaDist - Variable in class weka.clusterers.PCSoftKMeans
 
m_MaxKappaSim - Variable in class weka.clusterers.PCSoftKMeans
max kappa value for vmf distribution
m_MaxTimesPointsMoved - Variable in class weka.clusterers.assigners.RandomAssigner
Number of times points are moved in assignment step till stabilization
m_MaxTimesPointsMoved - Variable in class weka.clusterers.assigners.SimpleAssigner
Number of times points are moved in assignment step till stabilization
m_MaxTimesPointsMoved - Variable in class weka.clusterers.assigners.SortedAssigner
Number of times points are moved in assignment step till stabilization
m_MeanSquared - Variable in class weka.classifiers.lazy.IBk
Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks
m_MeanSquared - Variable in class weka.classifiers.sparse.IBkMetric
Whether to minimise mean squared error rather than mean absolute error when cross-validating on numeric prediction tasks
m_Means - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The means for numeric attributes.
m_Means - Variable in class weka.classifiers.misc.Prototype
The means for numeric attributes.
m_MergeThreshold - Variable in class weka.clusterers.MPCKMeans
holds the default merge threshold for matchMergeStep
m_MergeThreshold - Variable in class weka.clusterers.PCKMeans
holds the default merge threshold for matchMergeStep
m_MergeThreshold - Variable in class weka.clusterers.PCSoftKMeans
holds the default merge threshold for matchMergeStep
m_MetaClassifier - Variable in class weka.classifiers.meta.Stacking
The meta classifier.
m_MetaFormat - Variable in class weka.classifiers.meta.Stacking
Format for meta data
m_Metric - Variable in class weka.classifiers.misc.PrototypeMetric
Metric to be used to compare intances to prototype instance
m_MetricName - Variable in class weka.classifiers.sparse.IBkMetric
 
m_Min - Variable in class weka.classifiers.lazy.LWR
The minimum values for numeric attributes.
m_Min - Variable in class weka.classifiers.sparse.IBkMetric
The minimum values for numeric attributes.
m_MinArray - Variable in class weka.classifiers.bayes.SemiSupEM
The minimum values for numeric attributes.
m_MinNum - Variable in class weka.classifiers.trees.REPTree
The minimum number of instances per leaf.
m_MinNum - Variable in class weka.classifiers.trees.RandomTree
Minimum number of instances for leaf.
m_MinVarianceProp - Variable in class weka.classifiers.trees.REPTree
The minimum proportion of the total variance (over all the data) required for split.
m_MinimizeExpectedCost - Variable in class weka.classifiers.meta.CostSensitiveClassifier
True if the costs should be used by selecting the minimum expected cost (false means weight training data by the costs)
m_MissingLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of missing values
m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStar
missing value treatment
m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
missing value treatment
m_MissingMode - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
missing value treatment
m_MissingProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Probability of test attribute transforming into train attribute with missing value
m_MissingProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Probability of test attribute transforming into train attribute with missing value
m_Mistakes - Variable in class weka.classifiers.functions.Winnow
Accumulated mistake count (for statistics)
m_Model - Variable in class weka.core.DistanceFunction
the data model
m_Model - Variable in class weka.gui.AttributeListPanel
The table model containing attribute names
m_Model - Variable in class weka.gui.AttributeSelectionPanel
The table model containingn attribute names and selection status
m_Model - Variable in class weka.gui.ResultHistoryPanel
The list model
m_ModelFilter - Variable in class weka.gui.explorer.ClassifierPanel
Filter to ensure only model files are selected
m_ModelFilter - Variable in class weka.gui.explorer.ClustererPanel
Filter to ensure only model files are selected
m_ModifyHeader - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Modify header for nominal attributes?
m_MovePointsTillAssignmentStabilizes - Variable in class weka.clusterers.PCKMeans
Move points in assignment step till stabilization?
m_MovePointsTillAssignmentStabilizes - Variable in class weka.clusterers.assigners.RandomAssigner
Move points in assignment step till stabilization?
m_MovePointsTillAssignmentStabilizes - Variable in class weka.clusterers.assigners.SimpleAssigner
Move points in assignment step till stabilization?
m_MovePointsTillAssignmentStabilizes - Variable in class weka.clusterers.assigners.SortedAssigner
Move points in assignment step till stabilization?
m_MustLinkWeight - Variable in class weka.clusterers.MPCKMeans
weight to be given to each constraint
m_MustLinkWeight - Variable in class weka.clusterers.PCKMeans
weight to be given to each constraint
m_MustLinkWeight - Variable in class weka.clusterers.PCSoftKMeans
weight to be given to each constraint
m_NCV - Variable in class weka.classifiers.lazy.LBR
 
m_NRConvergenceDifference - Variable in class weka.clusterers.MPCKMeans
min difference of NR values for convergence
m_Name - Variable in class weka.filters.unsupervised.attribute.Add
The name for the new attribute
m_NeighborSets - Variable in class weka.clusterers.MPCKMeans
neighbor list for active learning: points in each cluster neighborhood
m_NeighborSets - Variable in class weka.clusterers.PCKMeans
neighbor list for active learning: points in each cluster neighborhood
m_NeighborSets - Variable in class weka.clusterers.PCSoftKMeans
neighbor list: points in each neighborhood inferred from constraints
m_NewBatch - Variable in class weka.filters.Filter
Record whether the filter is at the start of a batch
m_NewBut - Variable in class weka.gui.experiment.SetupPanel
Click to create a new experiment with default settings
m_NewBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to create a new experiment with default settings
m_Next - Variable in class weka.classifiers.sparse.IBkMetric.NeighborNode
A link to the next neighbor instance
m_Next - Variable in class weka.core.Queue.QueueNode
The next node in the queue
m_NoPruning - Variable in class weka.classifiers.trees.REPTree
Don't prune
m_NominalIndexes - Variable in class weka.experiment.InstancesResultListener
For lookup of indices given a string value for each nominal attribute
m_NominalMapping - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
If m_ModifyHeader, stores a mapping from old to new indexes
m_NominalStrings - Variable in class weka.experiment.InstancesResultListener
Contains strings seen so far for each nominal attribute
m_NonLocalEndIndex - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
m_NonLocalStartIndex - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Indices for range of non-local features (to be acquired)
m_Normalize - Variable in class weka.core.EuclideanDistance
True if normalization should be done
m_NormalizeAttributes - Variable in class weka.classifiers.misc.Prototype
If set, Normalize all real attribute values between 0 and 1 so that each dimension contributes equally to distance
m_Notes - Variable in class weka.experiment.Experiment
User notes about the experiment
m_NotesText - Variable in class weka.gui.experiment.SetupPanel
Area for user notes Default of 5 rows
m_NotesText - Variable in class weka.gui.experiment.SimpleSetupPanel
Area for user notes Default of 5 rows
m_NumActive - Variable in class weka.clusterers.MPCKMeans
number of pairs to seed with
m_NumActive - Variable in class weka.clusterers.PCKMeans
number of pairs to seed with
m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStar
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of attributes
m_NumAttributes - Variable in class weka.classifiers.meta.CVParameterSelection
The number of attributes in the data
m_NumAttributes - Variable in class weka.core.SparseInstance
The maximum number of values that can be stored.
m_NumAttributes - Variable in class weka.datagenerators.ClusterGenerator
 
m_NumAttributesLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the number of attributes
m_NumAttributesUsed - Variable in class weka.classifiers.lazy.IBk
The number of attributes the contribute to a prediction
m_NumAttributesUsed - Variable in class weka.classifiers.sparse.IBkMetric
The number of attributes the contribute to a prediction
m_NumAttributesUsed - Variable in class weka.core.EuclideanDistance
The number of attributes the contribute to a prediction
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_NumBins - Variable in class weka.classifiers.meta.RegressionByDiscretization
The number of classes in the Discretized training data.
m_NumBins - Variable in class weka.filters.unsupervised.attribute.Discretize
The number of bins to divide the attribute into
m_NumClasses - Variable in class weka.classifiers.bayes.BayesNet
The number of classes
m_NumClasses - Variable in class weka.classifiers.bayes.NaiveBayes
The number of classes (or 1 for numeric class)
m_NumClasses - Variable in class weka.classifiers.lazy.IBk
The number of class values (or 1 if predicting numeric)
m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStar
The number of class values
m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of class values
m_NumClasses - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of class values
m_NumClasses - Variable in class weka.classifiers.meta.AdaBoostM1
The number of classes
m_NumClasses - Variable in class weka.classifiers.meta.LogitBoost
The number of classes
m_NumClasses - Variable in class weka.classifiers.meta.MultiBoostAB
The number of classes
m_NumClasses - Variable in class weka.classifiers.meta.QBoost
The number of classes
m_NumClasses - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The number of classes
m_NumClasses - Variable in class weka.classifiers.misc.VFI
The number of classes
m_NumClasses - Variable in class weka.classifiers.sparse.IBkMetric
The number of class values (or 1 if predicting numeric)
m_NumClasses - Variable in class weka.clusterers.SemiSupClustererEvaluation
The number of underlying classes
m_NumClusters - Variable in class weka.clusterers.FarthestFirst
number of clusters to generate
m_NumClusters - Variable in class weka.clusterers.MPCKMeans
number of clusters to generate, default is -1 to get it from labeled data
m_NumClusters - Variable in class weka.clusterers.PCKMeans
number of clusters to generate, default is -1 to get it from labeled data
m_NumClusters - Variable in class weka.clusterers.PCSoftKMeans
number of clusters to generate, default is -1 to get it from labeled data
m_NumClusters - Variable in class weka.clusterers.SeededKMeans
number of clusters to generate, default is 3
m_NumClusters - Variable in class weka.clusterers.SemiSupClustererEvaluation
The number of produced clusters
m_NumClusters - Variable in class weka.datagenerators.ClusterGenerator
 
m_NumCurrentClusters - Variable in class weka.clusterers.MPCKMeans
Number of clusters in the process
m_NumCurrentClusters - Variable in class weka.clusterers.PCKMeans
Number of clusters in the process
m_NumCurrentClusters - Variable in class weka.clusterers.PCSoftKMeans
Number of clusters in the process
m_NumFolds - Variable in class weka.classifiers.meta.CVParameterSelection
The number of folds used in cross-validation
m_NumFolds - Variable in class weka.classifiers.meta.LogitBoost
The number of folds for the internal cross-validation.
m_NumFolds - Variable in class weka.classifiers.meta.Stacking
Set the number of folds for the cross-validation
m_NumFolds - Variable in class weka.classifiers.trees.REPTree
Number of folds for reduced error pruning.
m_NumFolds - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.CrossValidationResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The number of folds in the cross-validation
m_NumFolds - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The number of folds in the cross-validation
m_NumIndependentComponents - Variable in class weka.attributeSelection.MatlabICA
number of Independent Components
m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStar
The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The number of instances in the dataset
m_NumInstances - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The number of instances in the dataset
m_NumInstancesLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the number of instances
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_NumIterations - Variable in class weka.classifiers.meta.ActiveDecorate
The maximum number of Decorate iterations to run.
m_NumIterations - Variable in class weka.classifiers.meta.AdaBoostM1
The number of successfully generated base classifiers.
m_NumIterations - Variable in class weka.classifiers.meta.Bagging
The number of iterations.
m_NumIterations - Variable in class weka.classifiers.meta.Crate
The maximum number of Crate iterations to run.
m_NumIterations - Variable in class weka.classifiers.meta.DEC
The number of iterations.
m_NumIterations - Variable in class weka.classifiers.meta.Decorate
The maximum number of Decorate iterations to run.
m_NumIterations - Variable in class weka.classifiers.meta.Fable
The maximum number of Decorate iterations to run.
m_NumIterations - Variable in class weka.classifiers.meta.LogitBoost
The number of successfully generated base classifiers.
m_NumIterations - Variable in class weka.classifiers.meta.MetaCost
The number of iterations.
m_NumIterations - Variable in class weka.classifiers.meta.MultiBoostAB
The number of successfully generated base classifiers.
m_NumIterations - Variable in class weka.classifiers.meta.QBag
The number of iterations.
m_NumIterations - Variable in class weka.classifiers.meta.QBoost
The number of successfully generated base classifiers.
m_NumIterations - Variable in class weka.classifiers.meta.SemiSupDecorate
The number of iterations.
m_NumIterations - Variable in class weka.classifiers.meta.TestEnsembleClassifier
 
m_NumMissingLabels - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Number of categories for which labeled data is not provided
m_NumRuns - Variable in class weka.classifiers.meta.LogitBoost
The number of runs for the internal cross-validation.
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_NumSubCmtys - Variable in class weka.classifiers.meta.MultiBoostAB
The number of sub-committees to use
m_NumXValFolds - Variable in class weka.classifiers.meta.MultiScheme
Number of folds to use for cross validation (0 means use training error for selection)
m_NumXValFolds - Variable in class weka.classifiers.meta.ThresholdSelector
The number of folds used in cross-validation
m_Number - Variable in class weka.classifiers.lazy.LBR
 
m_NumberOfInstances - Variable in class weka.classifiers.lazy.LBR
 
m_NumberOfRepetitionsTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for number of repetitions
m_NumericClassData - Variable in class weka.classifiers.meta.LogitBoost
Dummy dataset with a numeric class
m_NumericClassData - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Dummy dataset with a numeric class
m_ObjFunConvergenceDifference - Variable in class weka.clusterers.MPCKMeans
min difference of objective function values for convergence
m_ObjFunConvergenceDifference - Variable in class weka.clusterers.PCKMeans
min difference of objective function values for convergence
m_ObjFunConvergenceDifference - Variable in class weka.clusterers.PCSoftKMeans
min.
m_ObjFunConvergenceDifference - Variable in class weka.clusterers.SeededKMeans
min difference of objective function values for convergence
m_Object - Variable in class weka.gui.GenericObjectEditor
The object being configured
m_ObjectNames - Variable in class weka.gui.GenericObjectEditor
The model containing the list of names to select from
m_ObjectPropertyPanel - Variable in class weka.gui.GenericObjectEditor
The property panel created for the objects
m_Objective - Variable in class weka.clusterers.MPCKMeans
value of current objective function
m_Objective - Variable in class weka.clusterers.PCKMeans
value of objective function
m_Objective - Variable in class weka.clusterers.PCSoftKMeans
value of objective function
m_Objective - Variable in class weka.clusterers.SeededKMeans
value of objective function
m_Objective - Variable in class weka.clusterers.SemiSupClustererEvaluation
Objective function of the clustering
m_Objs - Variable in class weka.gui.ResultHistoryPanel
A hashtable mapping names to arbitrary objects
m_Offset - Variable in class weka.classifiers.meta.LogitBoost
The value by which the actual target value for the true class is offset.
m_OldObjective - Variable in class weka.clusterers.MPCKMeans
value of last objective function
m_OnDemandDirectory - Variable in class weka.classifiers.meta.CostSensitiveClassifier
The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.classifiers.meta.MetaCost
The directory used when loading cost files on demand, null indicates current directory
m_OnDemandDirectory - Variable in class weka.experiment.CostSensitiveClassifierSplitEvaluator
The directory used when loading cost files on demand, null indicates current directory
m_OpenBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
Open object from disk
m_OpenBut - Variable in class weka.gui.experiment.SetupPanel
Click to load an experiment
m_OpenBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to load an experiment
m_OpenDBBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a Database
m_OpenFileBut - Variable in class weka.gui.SetInstancesPanel
Click to open instances from a file
m_OpenFileBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a file
m_OpenURLBut - Variable in class weka.gui.SetInstancesPanel
Click to open instances from a URL
m_OpenURLBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to load base instances from a URL
m_OptimizeBins - Variable in class weka.classifiers.meta.RegressionByDiscretization
Whether the Discretizer will optimise the number of bins
m_OrderAlgorithmsFirstRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing algorithms first in order of execution
m_OrderDatasetsFirstRBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Radio button for choosing datasets first in order of execution
m_Out - Variable in class weka.experiment.CSVResultListener
The destination for results (typically connected to the output file)
m_OutRedirector - Variable in class weka.gui.SimpleCLI
The thread that sends output from m_POO to the output box
m_OutText - Variable in class weka.gui.experiment.ResultsPanel
Displays the output of tests
m_OutText - Variable in class weka.gui.explorer.AssociationsPanel
The output area for associations
m_OutText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The output area for attribute selection results
m_OutText - Variable in class weka.gui.explorer.ClassifierPanel
The output area for classification results
m_OutText - Variable in class weka.gui.explorer.ClustererPanel
The output area for classification results
m_OutputArea - Variable in class weka.gui.SimpleCLI
The output area canvas added to the frame
m_OutputConfusionBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output a confusion matrix
m_OutputEntropyBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output entropy statistics
m_OutputFile - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.CSVResultListener
The destination output file, null sends to System.out
m_OutputFile - Variable in class weka.experiment.CrossValidationResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.RandomSplitResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The destination output file/directory for raw output
m_OutputFile - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The destination output file/directory for raw output
m_OutputModelBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output the model built from the training data
m_OutputPerClassBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output true/false positives, precision/recall for each class
m_OutputPredictionsTextBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to output text predictions
m_PCAMFile - Variable in class weka.attributeSelection.MatlabPCA
Name of the Matlab program file that computes PCA
m_PD - Variable in class weka.gui.experiment.AlgorithmListPanel
The currently displayed property dialog, if any
m_POE - Variable in class weka.gui.SimpleCLI
The new output stream for System.err
m_POO - Variable in class weka.gui.SimpleCLI
The new output stream for System.out
m_ParentSets - Variable in class weka.classifiers.bayes.BayesNet
The parent sets.
m_PercentBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to generate a % split
m_PercentBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to generate a % split
m_PercentLab - Variable in class weka.gui.explorer.ClassifierPanel
Label by where the % split is entered
m_PercentLab - Variable in class weka.gui.explorer.ClustererPanel
Label by where the % split is entered
m_PercentText - Variable in class weka.gui.explorer.ClassifierPanel
The field where the % split is entered
m_PercentText - Variable in class weka.gui.explorer.ClustererPanel
The field where the % split is entered
m_PerformBut - Variable in class weka.gui.experiment.ResultsPanel
Click to start the test
m_PhaseTwoRandom - Variable in class weka.clusterers.MPCKMeans
Round robin or Random in active Phase Two
m_PhaseTwoRandom - Variable in class weka.clusterers.PCKMeans
Round robin or Random in active Phase Two
m_PlotBut - Variable in class weka.gui.experiment.ResultsPanel
Click to launch gnuplot
m_PlotPoints - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_PlotPoints - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_PlotPoints - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_PlotPoints - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set -- ONLY INTEGERS SUPPORTED
m_PlotPoints - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_PlotPoints - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The specific points to plot, integers representing specific numbers of incomplete labels
m_PlotPoints - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_PlotPoints - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_PlotPoints - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_Points - Variable in class weka.experiment.PairedTTester
Points on the learning curve
m_PrecTex - Variable in class weka.gui.experiment.ResultsPanel
Lets the user specify the precision of results desired
m_Precision - Variable in class weka.classifiers.meta.LogitBoost
The threshold on the improvement of the likelihood
m_Precision - Variable in class weka.experiment.PairedTTester
Precision desired - number of decimal places
m_PreprocessPanel - Variable in class weka.gui.explorer.Explorer
The panel for preprocessing instances
m_Priors - Variable in class weka.classifiers.bayes.NaiveBayesSimple
The prior probabilities of the classes.
m_Priors - Variable in class weka.classifiers.lazy.LBR
The prior probabilities of the classes.
m_Prop - Variable in class weka.classifiers.trees.REPTree.Tree
The proportions of training instances going down each branch.
m_Prop - Variable in class weka.classifiers.trees.RandomTree
The proportions of training instances going down each branch.
m_PropertyArray - Variable in class weka.experiment.Experiment
The array of values to set the property to
m_PropertyNumber - Variable in class weka.experiment.Experiment
The current custom property value index when the experiment is running
m_PropertyPath - Variable in class weka.experiment.Experiment
The path to the iterator property
m_Prototypes - Variable in class weka.classifiers.misc.PrototypeMetric
Prototype instance for each class
m_PruningType - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The pruning type used
m_Purity - Variable in class weka.clusterers.SemiSupClustererEvaluation
Purity of the clustering
m_RCAMFile - Variable in class weka.core.metrics.BarHillelMetric
 
m_RCAMFile - Variable in class weka.core.metrics.BarHillelMetricMatlab
Name of the Matlab program file that computes RCA
m_RCAMFileToken - Variable in class weka.core.metrics.BarHillelMetric
Name of the Octave program file that computes RCA
m_RLEditor - Variable in class weka.gui.experiment.SetupPanel
The ResultListener editor
m_RLEditorPanel - Variable in class weka.gui.experiment.SetupPanel
The panel to contain the ResultListener editor
m_RP - Variable in class weka.experiment.CSVResultListener
The ResultProducer sending us results
m_RPEditor - Variable in class weka.gui.experiment.SetupPanel
The ResultProducer editor
m_RPEditorPanel - Variable in class weka.gui.experiment.SetupPanel
The panel to contain the ResultProducer editor
m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStar
Table of random class value colomns
m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Set of colomns: each colomn representing a randomised version of the train dataset class colomn
m_RandClassCols - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set of colomns: each colomn representing a randomised version of the train dataset class colomn
m_Random - Variable in class weka.classifiers.bayes.SemiSupEM
random numbers and seed
m_Random - Variable in class weka.classifiers.meta.ActiveDecorate
The random number generator.
m_Random - Variable in class weka.classifiers.meta.Crate
The random number generator.
m_Random - Variable in class weka.classifiers.meta.Decorate
The random number generator.
m_Random - Variable in class weka.classifiers.meta.Fable
The random number generator.
m_Random - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Random Number, used for randomization in each run
m_Random - Variable in class weka.filters.unsupervised.instance.Randomize
The current random number generator
m_RandomInstance - Variable in class weka.classifiers.meta.LogitBoost
The random number generator used
m_RandomInstance - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The random number generator used
m_RandomLab - Variable in class weka.gui.explorer.ClassifierPanel
 
m_RandomNumberGenerator - Variable in class weka.clusterers.MPCKMeans
holds the random number generator used in various parts of the code
m_RandomNumberGenerator - Variable in class weka.clusterers.PCKMeans
holds the random number generator used in various parts of the code
m_RandomSeed - Variable in class weka.clusterers.MPCKMeans
holds the random Seed, useful for randomPerturbInit
m_RandomSeed - Variable in class weka.clusterers.PCKMeans
holds the random Seed used to seed the random number generator
m_RandomSeed - Variable in class weka.clusterers.PCSoftKMeans
holds the random Seed, useful for randomPerturbInit
m_RandomSeedText - Variable in class weka.gui.explorer.ClassifierPanel
User specified random seed for cross validation or % split
m_RandomSize - Variable in class weka.classifiers.meta.DEC
Number of random instances to add at each iteration.
m_RandomSize - Variable in class weka.classifiers.meta.SemiSupDecorate
Number of random instances to add at each iteration.
m_RangeMode - Variable in class weka.classifiers.meta.ThresholdSelector
The range correction mode
m_Ranges - Variable in class weka.classifiers.lazy.IBk
Ranges of the universe of data, lowest value, highest value and width
m_Ranges - Variable in class weka.classifiers.misc.Prototype
The range (from min to max) taken on by each of the numeric attributes
m_Ranges - Variable in class weka.core.DistanceFunction
the range of the attributes
m_Ranges - Variable in class weka.core.Instances
Ranges of instances
m_Readable - Variable in class weka.core.Tag
The descriptive text
m_ReducedHeader - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The header of the dimensionally reduced data
m_RelationName - Variable in class weka.core.Instances
The dataset's name.
m_RelationNameLab - Variable in class weka.gui.InstancesSummaryPanel
Displays the name of the relation
m_RemainderErrors - Variable in class weka.classifiers.lazy.LBR
 
m_RemoveAll - Variable in class weka.gui.AttributeSelectionPanel
Press to deselect all attributes
m_Repainters - Variable in class weka.gui.visualize.LegendPanel
a list of components that need to be repainted when a colour is changed
m_ReplaceMissingFilter - Variable in class weka.clusterers.FarthestFirst
replace missing values in training instances
m_Result - Variable in class weka.gui.ListSelectorDialog
Whether the selection was made or cancelled
m_Result - Variable in class weka.gui.PropertySelectorDialog
Whether the selection was made or cancelled
m_ResultKeyBut - Variable in class weka.gui.experiment.ResultsPanel
Click to edit the columns used to determine the scheme
m_ResultKeyLabel - Variable in class weka.gui.experiment.ResultsPanel
Displays the currently selected column names for the scheme & options
m_ResultKeyList - Variable in class weka.gui.experiment.ResultsPanel
Displays the list of selected columns determining the scheme
m_ResultKeyModel - Variable in class weka.gui.experiment.ResultsPanel
Stores the list of attributes for selecting the scheme columns
m_ResultListener - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.AveragingResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.CrossValidationResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.DatabaseResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.Experiment
Where results will be sent
m_ResultListener - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.LearningRateResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.RandomSplitResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The ResultListener to send results to
m_ResultListener - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The ResultListener to send results to
m_ResultPath - Variable in class weka.gui.PropertySelectorDialog
Stores the path to the selected property
m_ResultProducer - Variable in class weka.experiment.AveragingResultProducer
The ResultProducer used to generate results
m_ResultProducer - Variable in class weka.experiment.DatabaseResultListener
The ResultProducer to listen to
m_ResultProducer - Variable in class weka.experiment.DatabaseResultProducer
The ResultProducer used to generate results
m_ResultProducer - Variable in class weka.experiment.Experiment
The result producer
m_ResultProducer - Variable in class weka.experiment.LearningRateResultProducer
The ResultProducer used to generate results
m_Results - Variable in class weka.experiment.AveragingResultProducer
Collects the results from a single run
m_Results - Variable in class weka.gui.ResultHistoryPanel
A Hashtable mapping names to result buffers
m_ResultsDestinationCBox - Variable in class weka.gui.experiment.SimpleSetupPanel
Combo box for choosing experiment destination type
m_ResultsDestinationPathLabel - Variable in class weka.gui.experiment.SimpleSetupPanel
Label for destination field
m_ResultsDestinationPathTField - Variable in class weka.gui.experiment.SimpleSetupPanel
Input field for result destination path
m_ResultsPanel - Variable in class weka.gui.experiment.Experimenter
The panel for analysing experimental results
m_ResultsTableName - Variable in class weka.experiment.DatabaseResultListener
The name of the current results table
m_ResultsetKeyColumns - Variable in class weka.experiment.PairedTTester
An array containing the indexes of just the selected columns
m_ResultsetKeyColumnsRange - Variable in class weka.experiment.PairedTTester
The range of columns that specify a unique result set (eg: scheme plus configuration)
m_Resultsets - Variable in class weka.experiment.PairedTTester
Stores a vector for each resultset holding all instances in each set
m_ResultsetsValid - Variable in class weka.experiment.PairedTTester
Indicates whether the instances have been partitioned
m_Root - Variable in class weka.gui.PropertySelectorDialog
The root of the property tree
m_RootObject - Variable in class weka.gui.PropertySelectorDialog
The object at the root of the tree
m_RunColumn - Variable in class weka.experiment.PairedTTester
The index of the column containing the run number
m_RunColumnSet - Variable in class weka.experiment.PairedTTester
The option setting for the run number column (-1 means last)
m_RunCombo - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which column contains the run number
m_RunLower - Variable in class weka.experiment.Experiment
Lower run number
m_RunModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_RunCombo
m_RunNumber - Variable in class weka.experiment.Experiment
The current run number when the experiment is running
m_RunNumberPanel - Variable in class weka.gui.experiment.SetupPanel
The panel for configuring run numbers
m_RunPanel - Variable in class weka.gui.experiment.Experimenter
The panel for running the experiment
m_RunThread - Variable in class weka.gui.SimpleCLI
The thread currently running a class main method
m_RunThread - Variable in class weka.gui.experiment.RunPanel
The thread running the experiment
m_RunThread - Variable in class weka.gui.explorer.AssociationsPanel
A thread that associator runs in
m_RunThread - Variable in class weka.gui.explorer.AttributeSelectionPanel
A thread that attribute selection runs in
m_RunThread - Variable in class weka.gui.explorer.ClassifierPanel
A thread that classification runs in
m_RunThread - Variable in class weka.gui.explorer.ClustererPanel
A thread that clustering runs in
m_RunUpper - Variable in class weka.experiment.Experiment
Upper run number
m_SQLQ - Variable in class weka.gui.explorer.PreprocessPanel
Stores the last sql query executed
m_STPMX - Variable in class weka.core.Optimization
 
m_SaveBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
Save object to disk
m_SaveBut - Variable in class weka.gui.experiment.SetupPanel
Click to save an experiment
m_SaveBut - Variable in class weka.gui.experiment.SimpleSetupPanel
Click to save an experiment
m_SaveBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to apply filters and save the results
m_SaveOut - Variable in class weka.gui.explorer.AssociationsPanel
The buffer saving object for saving output
m_SaveOutBut - Variable in class weka.gui.experiment.ResultsPanel
Click to save test output to a file
m_Scale - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The scale parameter
m_Search - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The search method to use
m_Seed - Variable in class weka.classifiers.BVDecompose
The random number seed
m_Seed - Variable in class weka.classifiers.RegressionBVDecompose
The random number seed
m_Seed - Variable in class weka.classifiers.functions.Winnow
Random seed used for shuffling the dataset, -1 == disable
m_Seed - Variable in class weka.classifiers.meta.ActiveDecorate
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.AdaBoostM1
Seed for boosting with resampling.
m_Seed - Variable in class weka.classifiers.meta.Bagging
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.CVParameterSelection
Random number seed
m_Seed - Variable in class weka.classifiers.meta.CostSensitiveClassifier
Seed for reweighting using resampling.
m_Seed - Variable in class weka.classifiers.meta.Crate
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.DEC
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.Decorate
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.Fable
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.LogitBoost
Seed for boosting with resampling.
m_Seed - Variable in class weka.classifiers.meta.MetaCost
Seed for reweighting using resampling.
m_Seed - Variable in class weka.classifiers.meta.MultiBoostAB
Seed for boosting with resampling.
m_Seed - Variable in class weka.classifiers.meta.MultiClassClassifier
Random number seed
m_Seed - Variable in class weka.classifiers.meta.MultiScheme
Random number seed
m_Seed - Variable in class weka.classifiers.meta.QBag
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.QBoost
Seed for boosting with resampling.
m_Seed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Seed for boosting with resampling.
m_Seed - Variable in class weka.classifiers.meta.SemiSupDecorate
The seed for random number generation.
m_Seed - Variable in class weka.classifiers.meta.Stacking
Random number seed
m_Seed - Variable in class weka.classifiers.meta.ThresholdSelector
Random number seed
m_Seed - Variable in class weka.classifiers.trees.REPTree
Seed for random data shuffling.
m_Seed - Variable in class weka.clusterers.FarthestFirst
random seed
m_Seed - Variable in class weka.filters.unsupervised.instance.Randomize
The random number seed
m_SeedHash - Variable in class weka.clusterers.HAC
holds the ([seed instance] -> [clusterLabel of seed instance]) mapping
m_SeedHash - Variable in class weka.clusterers.MPCKMeans
holds the points involved in the constraints
m_SeedHash - Variable in class weka.clusterers.PCKMeans
holds the points involved in the constraints
m_SeedHash - Variable in class weka.clusterers.PCSoftKMeans
holds the points involved in the constraints
m_SeedHash - Variable in class weka.clusterers.SeededKMeans
holds the ([seed instance] -> [clusterLabel of seed instance]) mapping
m_SeedLab - Variable in class weka.gui.explorer.AttributeSelectionPanel
Label by where cv random seed is entered
m_SeedText - Variable in class weka.gui.explorer.AttributeSelectionPanel
The field where the seed value is entered
m_Seedable - Variable in class weka.clusterers.MPCKMeans
Seedable or not (true by default)
m_Seedable - Variable in class weka.clusterers.PCKMeans
Seedable or not (true by default)
m_Seedable - Variable in class weka.clusterers.PCSoftKMeans
Seedable or not (true by default)
m_Seedable - Variable in class weka.clusterers.SeededKMeans
semisupervision
m_SeedingMethod - Variable in class weka.clusterers.SeededKMeans
seeding method, by default seeded
m_SelectBut - Variable in class weka.gui.ListSelectorDialog
Click to choose the currently selected property
m_SelectBut - Variable in class weka.gui.PropertySelectorDialog
Click to choose the currently selected property
m_SelectCols - Variable in class weka.filters.unsupervised.attribute.Remove
Stores which columns to select as a funky range
m_Selected - Variable in class weka.core.SelectedTag
The index of the selected tag
m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Copy
Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedAttributes - Variable in class weka.filters.unsupervised.attribute.Remove
Stores the indexes of the selected attributes in order, once the dataset is seen
m_SelectedCols - Variable in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Stores which columns to copy
m_SelectedRange - Variable in class weka.filters.unsupervised.attribute.StringToWordVector
Range of columns to convert to word vectors
m_SelectionCommittee - Variable in class weka.classifiers.meta.ActiveDecorate
 
m_SelectionCommittee - Variable in class weka.classifiers.meta.Fable
 
m_SelectionScheme - Variable in class weka.classifiers.meta.ActiveDecorate
The selective sampling scheme to use.
m_SelectionScheme - Variable in class weka.classifiers.meta.Fable
The selective sampling scheme to use.
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_SetCostsBut - Variable in class weka.gui.explorer.ClassifierPanel
 
m_SetCostsFrame - Variable in class weka.gui.explorer.ClassifierPanel
The frame used to show the cost matrix editing panel
m_SetTestBut - Variable in class weka.gui.explorer.ClassifierPanel
The button used to open a separate test dataset
m_SetTestBut - Variable in class weka.gui.explorer.ClustererPanel
The button used to open a separate test dataset
m_SetTestFrame - Variable in class weka.gui.explorer.ClassifierPanel
The frame used to show the test set selection panel
m_SetTestFrame - Variable in class weka.gui.explorer.ClustererPanel
The frame used to show the test set selection panel
m_SetupPanel - Variable in class weka.gui.experiment.Experimenter
The panel for configuring the experiment
m_ShapeCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the shape they want to create for instance selection.
m_ShowStdDevs - Variable in class weka.experiment.PairedTTester
Indicates whether standard deviations should be displayed
m_ShowStdDevs - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select whether standard deviations are to be output or not
m_Shrinkage - Variable in class weka.classifiers.meta.LogitBoost
The value of the shrinkage parameter
m_SigTex - Variable in class weka.gui.experiment.ResultsPanel
Lets the user edit the test significance
m_Sigma - Variable in class weka.classifiers.BVDecompose
The calculated sigma (squared)
m_SignificanceLevel - Variable in class weka.experiment.PairedTTester
The significance level for comparisons
m_SimpleBut - Variable in class weka.gui.GUIChooser
Click to open the simplecli
m_SimpleCLI - Variable in class weka.gui.GUIChooser
The SimpleCLI
m_SimpleSetupRBut - Variable in class weka.gui.experiment.SetupModePanel
The button for choosing simple setup mode
m_SingleName - Variable in class weka.gui.ResultHistoryPanel
The named result being viewed in the single-click display
m_SingleText - Variable in class weka.gui.ResultHistoryPanel
An optional component for single-click display
m_Size - Variable in class weka.core.Queue
Store the current number of elements in the queue
m_SmallestProb - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Smallest probability of test attribute transforming into train attribute
m_SmallestProb - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Smallest probability of test attribute transforming into train attribute
m_SplitEvaluator - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.CrossValidationResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.RandomSplitResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The SplitEvaluator used to generate results
m_SplitEvaluator - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The SplitEvaluator used to generate results
m_SplitPoint - Variable in class weka.classifiers.trees.REPTree.Tree
The split point.
m_SplitPoint - Variable in class weka.classifiers.trees.RandomTree
The split point.
m_StartBut - Variable in class weka.gui.experiment.RunPanel
Click to start running the experiment
m_StartBut - Variable in class weka.gui.explorer.AssociationsPanel
Click to start running the associator
m_StartBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to start running the attribute selector
m_StartBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to start running the classifier
m_StartBut - Variable in class weka.gui.explorer.ClustererPanel
Click to start running the clusterer
m_StartingIndexOfTest - Variable in class weka.clusterers.HAC
starting index of test data in unlabeledData if transductive clustering
m_StartingIndexOfTest - Variable in class weka.clusterers.MPCKMeans
test data -- required to make sure that test points are not selected during active learning
m_StartingIndexOfTest - Variable in class weka.clusterers.PCKMeans
test data -- required to make sure that test points are not selected during active learning
m_StartingIndexOfTest - Variable in class weka.clusterers.PCSoftKMeans
test data -- required to make sure that test points are not selected during active learning
m_StartingIndexOfTest - Variable in class weka.clusterers.SeededKMeans
starting index of test data in unlabeledData if transductive clustering
m_Statement - Variable in class weka.experiment.DatabaseUtils
The statement used for database queries
m_StatsTable - Variable in class weka.gui.AttributeSummaryPanel
Displays other stats in a table
m_StatusBox - Variable in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Controls whether the custom iterator is used or not
m_StatusLab - Variable in class weka.gui.LogPanel
Displays the current status
m_StepSize - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The number of instances to add at each step
m_StepSize - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The number of instances to add at each step
m_StepSize - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The number of instances to add at each step
m_StepSize - Variable in class weka.experiment.LearningRateResultProducer
The number of instances to add at each step
m_StepSize - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The number of instances to add at each step
m_StepSize - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The number of labeled categories to drop at each step
m_StepSize - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The number of instances to add at each step
m_StepSize - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The number of instances to add at each step
m_StepSize - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The number of instances to add at each step
m_Stop - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The stop parameter
m_StopBut - Variable in class weka.gui.experiment.RunPanel
Click to signal the running experiment to halt
m_StopBut - Variable in class weka.gui.explorer.AssociationsPanel
Click to stop a running associator
m_StopBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to stop a running classifier
m_StopBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to stop a running classifier
m_StopBut - Variable in class weka.gui.explorer.ClustererPanel
Click to stop a running clusterer
m_StorePredictionsBut - Variable in class weka.gui.explorer.ClassifierPanel
Check to save the predictions in the results list for visualizing later on
m_StorePredictionsBut - Variable in class weka.gui.explorer.ClustererPanel
Check to save the predictions in the results list for visualizing later on
m_Successors - Variable in class weka.classifiers.trees.REPTree.Tree
The subtrees of this tree.
m_Successors - Variable in class weka.classifiers.trees.RandomTree
The subtrees appended to this tree.
m_SumEnsembleWts - Variable in class weka.classifiers.EnsembleClassifier
Sum of ensemble weights
m_SumOfClusterInstances - Variable in class weka.clusterers.MPCKMeans
temporary variable holding cluster sums while iterating
m_SumOfClusterInstances - Variable in class weka.clusterers.PCKMeans
temporary variable holding cluster sums while iterating
m_SumOfClusterInstances - Variable in class weka.clusterers.PCSoftKMeans
temporary variable holding cluster sums while iterating
m_Summary - Variable in class weka.gui.SetInstancesPanel
The instance summary component
m_Summary - Variable in class weka.gui.explorer.ClassifierPanel
The instances summary panel displayed by m_SetTestFrame
m_Summary - Variable in class weka.gui.explorer.ClustererPanel
The instances summary panel displayed by m_SetTestFrame
m_Support - Variable in class weka.gui.GenericObjectEditor
Handles property change notification
m_Support - Variable in class weka.gui.SetInstancesPanel
Manages sending notifications to people when we change the set of working instances.
m_Support - Variable in class weka.gui.experiment.SetupPanel
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
m_Support - Variable in class weka.gui.experiment.SimpleSetupPanel
Manages sending notifications to people when we change the experiment, at this stage, only the resultlistener so the resultpanel can update.
m_Support - Variable in class weka.gui.explorer.PreprocessPanel
Manages sending notifications to people when we change the set of working instances.
m_TOLX - Variable in class weka.core.Optimization
 
m_TTester - Variable in class weka.gui.experiment.ResultsPanel
The PairedTTester object
m_TabbedPane - Variable in class weka.gui.experiment.Experimenter
The tabbed pane that controls which sub-pane we are working with
m_TabbedPane - Variable in class weka.gui.explorer.Explorer
The tabbed pane that controls which sub-pane we are working with
m_Table - Variable in class weka.gui.AttributeListPanel
The table displaying attribute names
m_Table - Variable in class weka.gui.AttributeSelectionPanel
The table displaying attribute names and selection status
m_Tags - Variable in class weka.core.SelectedTag
The set of tags to choose from
m_Tail - Variable in class weka.core.Queue
Store a reference to the tail of the queue
m_TaskMonitor - Variable in class weka.gui.LogPanel
The panel for monitoring the number of running tasks (if supplied)
m_Test - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The test instance
m_Test - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The test instance
m_Test - Variable in class weka.clusterers.SemiSupClustererEvaluation
All test instances
m_TestInstances - Variable in class weka.gui.explorer.AssociationsPanel
The user-supplied test set (if any)
m_TestInstances - Variable in class weka.gui.explorer.ClassifierPanel
The user-supplied test set (if any)
m_TestInstances - Variable in class weka.gui.explorer.ClustererPanel
The user-supplied test set (if any)
m_TestInstancesCopy - Variable in class weka.gui.explorer.ClassifierPanel
The user supplied test set after preprocess filters have been applied
m_TestInstancesCopy - Variable in class weka.gui.explorer.ClustererPanel
The user supplied test set after preprocess filters have been applied
m_TestSplitBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to a user-specified test set
m_TestSplitBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to a user-specified test set
m_TestsButton - Variable in class weka.gui.experiment.ResultsPanel
Lets the user select which scheme to base comparisons against
m_TestsList - Variable in class weka.gui.experiment.ResultsPanel
Holds the list of schemes to base the test against
m_TestsModel - Variable in class weka.gui.experiment.ResultsPanel
The model embedded in m_TestsList
m_Threshold - Variable in class weka.classifiers.functions.Winnow
Prediction threshold, <0 == numAttributes
m_Threshold - Variable in class weka.classifiers.meta.DEC
Confidence threshold above committee decisions are to be trusted.
m_Threshold - Variable in class weka.classifiers.meta.SemiSupDecorate
Confidence threshold above committee decisions are to be trusted.
m_TotalCost - Variable in class weka.clusterers.SemiSupClustererEvaluation
The total cost of predictions (includes instance weights)
m_TotalCount - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Number of trai instances with no missing attribute values
m_TotalSeedHash - Variable in class weka.clusterers.Seeder
Stores the mapping between all possible seeds and their cluster assignments
m_TotalTrainWithLabels - Variable in class weka.clusterers.MPCKMeans
training instances with labels
m_TotalTrainWithLabels - Variable in class weka.clusterers.PCKMeans
training instances with labels
m_TotalTrainWithLabels - Variable in class weka.clusterers.PCSoftKMeans
training instances with labels
m_TotalTrainWithLabels - Variable in class weka.clusterers.SeededKMeans
training instances with labels
m_Train - Variable in class weka.classifiers.lazy.IBk
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.LWR
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.kstar.KStar
The training instances used for classification.
m_Train - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The train instance
m_Train - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The train instance
m_Train - Variable in class weka.classifiers.sparse.IBkMetric
The training instances used for classification.
m_TrainBut - Variable in class weka.gui.explorer.AttributeSelectionPanel
Click to set test mode to test on training data
m_TrainBut - Variable in class weka.gui.explorer.ClassifierPanel
Click to set test mode to test on training data
m_TrainBut - Variable in class weka.gui.explorer.ClustererPanel
Click to set test mode to test on training data
m_TrainEnsembleDiversity - Variable in class weka.classifiers.EnsembleClassifier
the ensemble diversity computed in the training data
m_TrainEnsembleError - Variable in class weka.classifiers.EnsembleClassifier
the average error of the ensemble on the training data
m_TrainError - Variable in class weka.classifiers.EnsembleClassifier
the error on the training data
m_TrainFoldSize - Variable in class weka.classifiers.meta.CVParameterSelection
The number of instances in a training fold
m_TrainIterations - Variable in class weka.classifiers.BVDecompose
The number of train iterations
m_TrainIterations - Variable in class weka.classifiers.RegressionBVDecompose
The number of train iterations
m_TrainPercent - Variable in class weka.experiment.RandomSplitResultProducer
The percentage of instances to use for training
m_TrainPoolSize - Variable in class weka.classifiers.BVDecompose
The number of instances used in the training pool
m_TrainPoolSize - Variable in class weka.classifiers.RegressionBVDecompose
The number of instances used in the training pool
m_TrainSet - Variable in class weka.classifiers.lazy.kstar.KStarNominalAttribute
The training instances used for classification.
m_TrainSet - Variable in class weka.classifiers.lazy.kstar.KStarNumericAttribute
The training instances used for classification.
m_Trainable - Variable in class weka.clusterers.MPCKMeans
 
m_Tree - Variable in class weka.classifiers.trees.REPTree
The Tree object
m_Tree - Variable in class weka.gui.PropertySelectorDialog
The component displaying the property tree
m_UndoBut - Variable in class weka.gui.explorer.PreprocessPanel
Click to revert back to the last saved point
m_UniqueLab - Variable in class weka.gui.AttributeSummaryPanel
Displays the number of unique values
m_Unlabeled - Variable in class weka.classifiers.meta.SemiSupDecorate
Unlabeled instances
m_UnlabeledData - Variable in class weka.classifiers.bayes.SemiSupEM
Original set of unlabeled Instances
m_UnlabeledInstances - Variable in class weka.classifiers.bayes.SemiSupEM
Soft labeled version of unlabeled data
m_UnlabeledTrain - Variable in class weka.clusterers.SemiSupClustererEvaluation
All unlabaled training instances
m_UpperSize - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The maximum number of instances to use.
m_UpperSize - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The maximum number of instances to use.
m_UpperSize - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The maximum number of instances to use.
m_UpperSize - Variable in class weka.experiment.LearningRateResultProducer
The maximum number of instances to use.
m_UpperSize - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The maximum number of instances to use.
m_UpperSize - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The maximum number of labeled categories to drop.
m_UpperSize - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The maximum number of instances to use.
m_UpperSize - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The maximum number of instances to use.
m_UpperSize - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The maximum number of instances to use.
m_UpperText - Variable in class weka.gui.experiment.RunNumberPanel
Configures the upper run number
m_UseAllK - Variable in class weka.classifiers.lazy.LWR
True if m_kNN should be set to all instances
m_UseBetterEncoding - Variable in class weka.filters.supervised.attribute.Discretize
Use better encoding of split point for MDL.
m_UseCombinedObjectiveFunction - Variable in class weka.clusterers.MPCKMeans
Using Combined Objective function (if true) or Potts Model (if false)
m_UseEqualFrequency - Variable in class weka.filters.unsupervised.attribute.Discretize
Use equal-frequency binning if unsupervised discretization turned on
m_UseKernelEstimator - Variable in class weka.classifiers.bayes.NaiveBayes
Whether to use kernel density estimator rather than normal distribution for numeric attributes
m_UseKononenko - Variable in class weka.filters.supervised.attribute.Discretize
Use Kononenko's MDL criterion instead of Fayyad et al.'s
m_UsePropertyIterator - Variable in class weka.experiment.Experiment
True if the exp should also iterate over a property of the RP
m_UseResampling - Variable in class weka.classifiers.meta.AdaBoostM1
Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.meta.LogitBoost
Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.meta.MultiBoostAB
Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.meta.QBoost
Use boosting with reweighting?
m_UseResampling - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Whether to use resampling
m_UseWeights - Variable in class weka.classifiers.meta.DEC
Use weights for committe votes - default equal wts
m_UseWeights - Variable in class weka.classifiers.meta.SemiSupDecorate
Use weights for committe votes - default equal wts
m_UserDir - Variable in class weka.gui.experiment.DatasetListPanel
The user (start) directory
m_Value - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Stores which value of a numeric attribute is to be used for filtering.
m_ValueBuffer - Variable in class weka.core.Instances
Buffer of values for sparse instance
m_Values - Variable in class weka.filters.unsupervised.instance.RemoveWithValues
Stores which values of nominal attribute are to be used for filtering.
m_Variance - Variable in class weka.classifiers.BVDecompose
The calculated variance
m_Variance - Variable in class weka.classifiers.RegressionBVDecompose
The calculated variance
m_VaryNodes - Variable in class weka.classifiers.bayes.ADNode
list of VaryNode children
m_Verbose - Variable in class weka.clusterers.SeededKMeans
Verbose?
m_Verbose - Variable in class weka.clusterers.Seeder
Verbose?
m_VisualizePanel - Variable in class weka.gui.explorer.Explorer
Label for a panel that still need to be implemented
m_Weight - Variable in class weka.core.Instance
The instance's weight.
m_WeightKernel - Variable in class weka.classifiers.lazy.LWR
The weighting kernel method currently selected
m_WeightTestCorrect - Variable in class weka.clusterers.SemiSupClustererEvaluation
The weight of all correctly categorized test instances.
m_WeightTestIncorrect - Variable in class weka.clusterers.SemiSupClustererEvaluation
The weight of all incorrectly categorized test instances.
m_WeightTestUnclassified - Variable in class weka.clusterers.SemiSupClustererEvaluation
The weight of all uncategorized test instances.
m_WeightTestWithClass - Variable in class weka.clusterers.SemiSupClustererEvaluation
The weight of test instances that had a class assigned to them.
m_WeightThreshold - Variable in class weka.classifiers.meta.AdaBoostM1
Weight Threshold.
m_WeightThreshold - Variable in class weka.classifiers.meta.LogitBoost
Weight thresholding.
m_WeightThreshold - Variable in class weka.classifiers.meta.MultiBoostAB
Weight Threshold.
m_WeightThreshold - Variable in class weka.classifiers.meta.QBoost
Weight Threshold.
m_WindowSize - Variable in class weka.classifiers.lazy.IBk
The maximum number of training instances allowed.
m_WindowSize - Variable in class weka.classifiers.sparse.IBkMetric
The maximum number of training instances allowed.
m_XCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute for the x axis
m_XINGMFile - Variable in class weka.core.metrics.XingMetric
 
m_XINGMFileToken - Variable in class weka.core.metrics.XingMetric
Name of the Octave program file that computes XING
m_YCombo - Variable in class weka.gui.visualize.VisualizePanel
Lets the user select the attribute for the y axis
m_Zero - Static variable in class weka.core.Optimization
 
m_ZipDest - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.CrossValidationResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.RandomSplitResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
The output zipper to use for saving raw splitEvaluator output
m_ZipDest - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
The output zipper to use for saving raw splitEvaluator output
m_aTokens - Variable in class weka.datagenerators.TextSource
An ordered list for looking up tokens.
m_acuity - Variable in class weka.clusterers.Cobweb
Acuity (minimum standard deviation).
m_additionalMeasures - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
The names of any additional measures to look for in SplitEvaluators
m_additionalMeasures - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The names of any additional measures to look for in SplitEvaluators
m_additionalMeasures - Variable in class weka.experiment.ExtractionResultProducer
The names of any additional measures to look for in SplitEvaluators
m_advanceDataSetFirst - Variable in class weka.gui.experiment.SetupPanel
Click to advacne data set before custom generator
m_advanceIteratorFirst - Variable in class weka.gui.experiment.SetupPanel
Click to advance custom generator before data set
m_advancedPanel - Variable in class weka.gui.experiment.SetupModePanel
The advanced setup panel
m_allTheRules - Variable in class weka.associations.Apriori
The list of all generated rules.
m_alpha - Variable in class weka.core.metrics.KL
 
m_alphaDecayRate - Variable in class weka.core.metrics.KL
 
m_animatedIcon - Variable in class weka.gui.beans.BeanVisual
 
m_animatedIconPath - Variable in class weka.gui.beans.BeanVisual
Holds name (including path) of the animated icon
m_arffFileFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
FIlter for choosing ARFF files
m_attTypeToDelete - Variable in class weka.filters.unsupervised.attribute.RemoveType
The type of attribute to delete
m_attrIdxs - Variable in class weka.core.metrics.Metric
indeces of attributes which the metric works on
m_attrIdxs - Variable in class weka.deduping.BasicDeduper
the attribute indeces on which to do deduping
m_attrIdxs - Variable in class weka.deduping.metrics.InstanceMetric
indeces of attributes which the metric works on
m_attrMatrix - Variable in class weka.core.metrics.BarHillelMetric
full matrix returned by Octave code
m_attrMatrix - Variable in class weka.core.metrics.BarHillelMetricMatlab
full matrix returned by Matlab code
m_attrMatrix - Variable in class weka.core.metrics.XingMetric
full matrix returned by Octave code
m_attrWeights - Variable in class weka.core.metrics.LearnableMetric
Weights of individual attributes
m_attrib - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The attribute itself.
m_attrib - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the attributes , using color to represent another attribute.
m_attribIndex - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The index for this attribute.
m_attribList - Variable in class weka.gui.visualize.MatrixPanel
The list for selecting the attributes to display the plot matrix
m_attribs - Variable in class weka.datagenerators.TextSource.Table
 
m_attributeFilter - Variable in class weka.filters.unsupervised.attribute.RemoveType
The attribute filter used to do the filtering
m_autoBounds - Variable in class weka.classifiers.sparse.SVMlight
 
m_axisChanged - Variable in class weka.gui.visualize.Plot2D
if the user changes attribute assigned to an axis
m_bFormatDefined - Variable in class weka.datagenerators.TextSource
True iff defineDataFormat() has been called.
m_bTFIDF - Variable in class weka.datagenerators.TextSource
Collect TFIDF statistics instead of TF.
m_bagger - Variable in class weka.classifiers.trees.RandomForest
The bagger.
m_barColour - Variable in class weka.gui.visualize.AttributePanel
The default colour to use for the background of the bars if a colour is not defined in Visualize.props
m_bestCommittee - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The current best committee
m_bias - Variable in class weka.classifiers.misc.VFI
Bias towards more confident intervals
m_biased - Variable in class weka.classifiers.sparse.SVMlight
Use biased hyperplane (i.e.
m_binPath - Variable in class weka.classifiers.sparse.SVMlight
Path to the directory where SVM-light executables are located
m_boostingIterations - Variable in class weka.classifiers.trees.adtree.ADTree
Option - the number of boosting iterations o perform
m_bufferedMode - Variable in class weka.classifiers.sparse.SVMlight
Is classification done via temporary files or via a buffer?
m_c1 - Variable in class weka.classifiers.sparse.SVMlight
parameter c in sigmoid/poly kernel
m_cIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_cIndex - Variable in class weka.gui.visualize.Plot2D
 
m_cIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_cached - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The x position of each point.
m_canChangeClassInDialog - Variable in class weka.gui.GenericObjectEditor
 
m_cancel - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to remove all splits.
m_cancelBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
cancel button
m_caseInsensitive - Variable in class weka.deduping.metrics.Tokenizer
Converting all tokens to lowercase
m_cellSize - Variable in class weka.gui.visualize.MatrixPanel
The slider to adjust the size of the cells in the matrix
m_centroids - Variable in class weka.clusterers.assigners.LPAssigner
 
m_checksumCoeffs - Variable in class weka.clusterers.MPCKMeans
 
m_checksumCoeffs - Variable in class weka.clusterers.PCKMeans
 
m_checksumCoeffs - Variable in class weka.clusterers.PCSoftKMeans
 
m_checksumHash - Variable in class weka.clusterers.HAC
A 'checksum hash' where indices are hashed to the sum of their attribute values
m_checksumHash - Variable in class weka.clusterers.MPCKMeans
A hash where the instance checksums are hashed
m_checksumHash - Variable in class weka.clusterers.PCKMeans
A hash where the instance checksums are hashed
m_checksumHash - Variable in class weka.clusterers.PCSoftKMeans
A hash where the instance checksums are hashed
m_checksumPerturb - Variable in class weka.clusterers.HAC
 
m_chunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_chunkletAssignmentFilename - Variable in class weka.core.metrics.BarHillelMetric
 
m_chunkletAssignmentFilename - Variable in class weka.core.metrics.BarHillelMetricMatlab
Name of the file where chunklet assignments will be stored
m_chunkletAssignmentFilenameToken - Variable in class weka.core.metrics.BarHillelMetric
Name of the file where chunklet assignments will be stored
m_clampProb - Variable in class weka.deduping.metrics.AffineProbMetric
Minimal value of a probability parameter.
m_classAttrib - Variable in class weka.gui.visualize.MatrixPanel
The combo box to allow user to select the colouring attribute
m_classIndex - Variable in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Attribute index for class attribute
m_classIndex - Variable in class weka.core.metrics.Metric
index of the class attribute
m_classIndex - Variable in class weka.deduping.metrics.InstanceMetric
index of the class attribute
m_classIndex - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The attribute to treat as the class for purposes of cleansing.
m_classInstanceMap - Variable in class weka.core.metrics.PairwiseSelector
A hashmap where class attribute values are mapped to lists of instances of that class
m_classInstanceMap - Variable in class weka.deduping.PairwiseSelector
A hashmap where true object IDs are mapped to lists of strings of that object
m_classNoise - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Add noise to Class Labels in Training Set
m_classNoiseTest - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Add noise to Class Labels in Testing Set
m_classPanel - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the legend for the colouring attribute
m_classSurround - Variable in class weka.gui.visualize.VisualizePanel
Panel that surrounds the class panel with a titled border
m_classValueList - Variable in class weka.core.metrics.PairwiseSelector
A list of classes, each element is the double value of the class attribute
m_classValueList - Variable in class weka.deduping.PairwiseSelector
A list of classes, each element is the double value of the class attribute
m_classValues - Variable in class weka.deduping.BasicDeduper
An array containing class values for instances (for faster statistics)
m_classifier - Variable in class weka.core.metrics.ClassifierMetricLearner
Classifier that is used for learning metric weights
m_classifier - Variable in class weka.core.metrics.LearnableMetric
 
m_classifier - Variable in class weka.deduping.metrics.ClassifierInstanceMetric
Classifier that is used for estimating similarity between records
m_classifier - Variable in class weka.deduping.metrics.KernelVSMetric
The classifier
m_classifier - Variable in class weka.gui.beans.BatchClassifierEvent
The classifier
m_classifier - Variable in class weka.gui.beans.IncrementalClassifierEvent
 
m_classifierClassName - Variable in class weka.core.metrics.LearnableMetric
 
m_classifierRequiresNominalClass - Variable in class weka.core.metrics.LearnableMetric
Certain classifiers may use non-nominal class attributes
m_cleansingClassifier - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The classifier used to do the cleansing
m_clusterAssignments - Variable in class weka.clusterers.HAC
temporary variable holding cluster assignments
m_clusterAssignments - Variable in class weka.deduping.BasicDeduper
temporary variable holding cluster assignments
m_clusterID - Variable in class weka.clusterers.HAC
ID of current cluster
m_clusterer - Variable in class weka.clusterers.assigners.MPCKMeansAssigner
Clusterer that the assigner operates on
m_clusterer - Variable in class weka.extraction.ClusteringExtractor
The clusterer
m_clusters - Variable in class weka.clusterers.HAC
holds the clusters
m_clusters - Variable in class weka.deduping.BasicDeduper
holds the clusters
m_cobwebTree - Variable in class weka.clusterers.Cobweb
Holds the root of the Cobweb tree.
m_col - Variable in class weka.gui.treevisualizer.NamedColor
The actual color object
m_colorList - Variable in class weka.gui.beans.StripChart
default colours for colouring lines
m_colorList - Variable in class weka.gui.visualize.AttributePanel
The colour map to use for colouring points
m_colorList - Variable in class weka.gui.visualize.Plot2D
The list of the colors used
m_colorList - Variable in class weka.gui.visualize.VisualizePanel
The list of the colors used
m_cols - Variable in class weka.gui.treevisualizer.Colors
The array with all the colors input
m_committees - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The committees
m_condProbs - Variable in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Conditional probabilities of each attribute given each class
m_configureHostNames - Variable in class weka.gui.experiment.DistributeExperimentPanel
Popup the HostListPanel
m_connectPoints - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the drawing of lines between consecutive points.
m_constraintHash - Variable in class weka.clusterers.assigners.LPAssigner
 
m_constraintWeight - Variable in class weka.clusterers.assigners.RMNAssigner
scaling factor for constraint weights
m_conversionType - Variable in class weka.core.metrics.BarHillelMetric
The method of converting, by default laplacian
m_conversionType - Variable in class weka.core.metrics.BarHillelMetricMatlab
The method of converting, by default laplacian
m_conversionType - Variable in class weka.core.metrics.KL
The method of converting, by default laplacian
m_conversionType - Variable in class weka.core.metrics.WeightedDotP
The method of converting, by default laplacian
m_conversionType - Variable in class weka.core.metrics.WeightedEuclidean
The method of converting, by default laplacian
m_conversionType - Variable in class weka.core.metrics.WeightedMahalanobis
The method of converting, by default laplacian
m_conversionType - Variable in class weka.core.metrics.XingMetric
The method of converting, by default laplacian
m_conversionType - Variable in class weka.deduping.metrics.AffineMetric
The method of converting, by default laplacian
m_conversionType - Variable in class weka.deduping.metrics.AffineProbMetric
The method of converting, by default laplacian
m_conversionType - Variable in class weka.deduping.metrics.JaccardMetric
The method of converting, by default laplacian
m_conversionType - Variable in class weka.deduping.metrics.KernelVSMetric
The method of converting, by default laplacian
m_conversionType - Variable in class weka.deduping.metrics.VectorSpaceMetric
The method of converting, by default laplacian
m_costFactor - Variable in class weka.classifiers.sparse.SVMlight
Cost: cost-factor, by which training errors on positive examples outweight errors on negative examples
m_count - Variable in class weka.deduping.metrics.TokenOccurrence
The number of times it occurs in the Document
m_counter - Variable in class weka.associations.ItemSet
Counter for how many transactions contain this item set.
m_counts - Variable in class weka.classifiers.misc.VFI
The class counts for each interval of each attribute
m_cp - Variable in class weka.gui.visualize.MatrixPanel
The panel that displays the legend of the colouring attribute
m_csvFileFilter - Variable in class weka.gui.experiment.SimpleSetupPanel
Filter for choosing CSV files
m_currAlpha - Variable in class weka.core.metrics.KL
 
m_currEta - Variable in class weka.clusterers.MPCKMeans
 
m_currIdx - Variable in class weka.clusterers.SeededKMeans
Index of the current element in the E-step
m_currentInstance - Variable in class weka.gui.beans.IncrementalClassifierEvent
 
m_currentSet - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The instances currently in memory for training
m_customColour - Variable in class weka.gui.visualize.PlotData2D
 
m_cutoff - Variable in class weka.clusterers.Cobweb
Cutoff (minimum category utility).
m_cycles - Variable in class weka.associations.Apriori
Number of cycles used before required number of rules was one.
m_d - Variable in class weka.classifiers.sparse.SVMlight
Parameter d in polynomial kernel
m_d - Variable in class weka.datagenerators.TextSource.Real
 
m_dClass - Variable in class weka.datagenerators.TextSource.DataRow
 
m_dNextClass - Variable in class weka.datagenerators.TextSource
The next class ID.
m_data - Variable in class weka.datagenerators.TextSource.DataRow
 
m_data - Variable in class weka.datagenerators.TextSource.Table
 
m_data - Variable in class weka.gui.visualize.MatrixPanel
The dataset for which this panel will display the plot matrix for
m_dataFilename - Variable in class weka.attributeSelection.MatlabICA
Name of the file where dataMatrix will be stored
m_dataFilename - Variable in class weka.clusterers.assigners.LPAssigner
 
m_dataFilename - Variable in class weka.core.metrics.BarHillelMetric
 
m_dataFilename - Variable in class weka.core.metrics.BarHillelMetricMatlab
Name of the file where dataMatrix will be stored
m_dataFilename - Variable in class weka.core.metrics.XingMetric
 
m_dataFilenameBase - Variable in class weka.clusterers.assigners.LPAssigner
 
m_dataFilenameToken - Variable in class weka.core.metrics.BarHillelMetric
Name of the file where dataMatrix will be stored
m_dataFilenameToken - Variable in class weka.core.metrics.XingMetric
Name of the file where dataMatrix will be stored
m_datasetFrequencies - Variable in class weka.core.metrics.KL
Frequencies over the entire dataset used for smoothing
m_debug - Variable in class weka.classifiers.sparse.NaiveBayesSimpleSparse
A debug flag
m_debug - Variable in class weka.classifiers.sparse.SVMlight
Output debugging information
m_debug - Variable in class weka.core.metrics.MatlabMetricLearner
Debugging output
m_debug - Variable in class weka.deduping.BasicDeduper
verbose?
m_debug - Variable in class weka.deduping.PairwiseSelector
Output debugging information
m_debugOutput - Variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.ActiveLearningCurveCVResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.CrossValidationResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.ExtractionResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.LearningCurveCrossValidationResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.RandomSplitResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.SemiSupCrossValidationResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Save raw output of split evaluators --- for debugging purposes
m_debugOutput - Variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Save raw output of split evaluators --- for debugging purposes
m_deduper - Variable in class weka.experiment.DeduperSplitEvaluator
The deduper used for evaluation
m_deduperOptions - Variable in class weka.experiment.DeduperSplitEvaluator
The deduper options (if any)
m_deduperVersion - Variable in class weka.experiment.DeduperSplitEvaluator
The deduper version
m_defaultWeight - Variable in class weka.classifiers.functions.Winnow
Starting weights for the prediction vector(s)
m_delimiters - Variable in class weka.deduping.metrics.WordTokenizer
A default set of delimiters
m_delta - Variable in class weka.associations.Apriori
Delta by which m_minSupport is decreased in each iteration.
m_destinationDatabaseURL - Variable in class weka.gui.experiment.SimpleSetupPanel
The database destination URL to store results into
m_destinationFilename - Variable in class weka.gui.experiment.SimpleSetupPanel
The filename to store results into
m_diffConstraintsFilename - Variable in class weka.core.metrics.XingMetric
 
m_diffConstraintsFilenameToken - Variable in class weka.core.metrics.XingMetric
Name of the file where diffConstaints will be stored
m_diffInstances - Variable in class weka.deduping.metrics.ClassifierInstanceMetric
A temporary dataset that contains diff-instances for training the classifier
m_displayAllPoints - Variable in class weka.gui.visualize.PlotData2D
Display all points (ie.
m_distanceMatrix - Variable in class weka.clusterers.HAC
distance matrix
m_distanceMatrix - Variable in class weka.deduping.BasicDeduper
distance matrix containing the distance between each pair
m_distribution - Variable in class weka.classifiers.trees.j48.ClassifierSplitModel
Distribution of class values.
m_doesProduce - Variable in class weka.experiment.ClassifierSplitEvaluator
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
m_doesProduce - Variable in class weka.experiment.RegressionSplitEvaluator
Array of booleans corresponding to the measures in m_AdditionalMeasures indicating which of the AdditionalMeasures the current classifier can produce
m_dotFileName - Variable in class weka.clusterers.HAC
Dot file name for dumping graph for tree visualization
m_dotWriter - Variable in class weka.clusterers.HAC
Dot file name for dumping graph for tree visualization
m_editopCosts - Variable in class weka.deduping.metrics.AffineProbMetric
parameters for the additive model, obtained from log-probs to speed up computations in the "testing" phase after weights have been learned
m_editopLogProbs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_editopOccs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_editopProbs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_eigenvalueFilename - Variable in class weka.attributeSelection.MatlabPCA
Name of the file where eigenvalues will be stored
m_eigenvectorFilename - Variable in class weka.attributeSelection.MatlabPCA
Name of the file where eigenvectors will be stored
m_eigenvectorFilenameBase - Variable in class weka.attributeSelection.MatlabPCA
 
m_enableDistributedExperiment - Variable in class weka.gui.experiment.DistributeExperimentPanel
Distribute the current experiment to remote hosts
m_endAtGapCost - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_endAtGapLogProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_endAtGapOccs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_endAtGapProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_endAtSubCost - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_endAtSubLogProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_endAtSubOccs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_endAtSubProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_engine - Variable in class weka.clusterers.assigners.LPAssigner
The matlab engine
m_engineType - Variable in class weka.clusterers.assigners.LPAssigner
The engine
m_epsilon - Variable in class weka.clusterers.assigners.RMNAssigner
small value to replace 0 in some places, to avoid numerical underflow
m_epsilon - Variable in class weka.core.metrics.GDMetricLearner
The convergence criterion for total weight updates
m_eta - Variable in class weka.clusterers.MPCKMeans
gradient descent parameters
m_etaDecayRate - Variable in class weka.clusterers.MPCKMeans
 
m_evaluator - Variable in class weka.core.metrics.AttrEvalMetricLearner
The attribute evaluator used
m_examplesCounted - Variable in class weka.classifiers.trees.adtree.ADTree
Statistics - the number of instances processed during search
m_expScalingFactor - Variable in class weka.clusterers.assigners.RMNAssigner
scaling factor for exponent
m_experimentFinished - Variable in class weka.experiment.RemoteExperimentEvent
True if a remote experiment has finished
m_extractor - Variable in class weka.experiment.ExtractionSplitEvaluator
The extractor used for evaluation
m_extractor - Variable in class weka.extraction.ClusteringExtractor
The baseline extractor that is used
m_extractorOptions - Variable in class weka.experiment.ExtractionSplitEvaluator
The extractor options (if any)
m_extractorVersion - Variable in class weka.experiment.ExtractionSplitEvaluator
The extractor version
m_f - Variable in class weka.core.Optimization
 
m_featureMiss - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set features missing, do not include Class as a Feature in Training Set
m_featureMissTest - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set features missing, do not include Class as a Feature in Testing Set
m_featureNoise - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Add noise to Features, do not include Class as a Feature in Training Set
m_featureNoiseTest - Variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
Add noise to Features, do not include Class as a Feature in Testing Set
m_fieldMetrics - Variable in class weka.deduping.metrics.ClassifierInstanceMetric
The actual array of metrics
m_filterThread - Variable in class weka.gui.beans.Filter
 
m_foldCreationMode - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
 
m_foldCreationMode - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
m_format - Variable in class weka.datagenerators.TextSource.Table
 
m_gamma - Variable in class weka.classifiers.sparse.SVMlight
Parameter gamma in rbf kernel
m_gapEndCost - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapEndLogProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapEndOccs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapEndProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapExtendCost - Variable in class weka.deduping.metrics.AffineMetric
The cost of continuing a gap
m_gapExtendCost - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapExtendLogProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapExtendOccs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapExtendProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapStartCost - Variable in class weka.deduping.metrics.AffineMetric
The cost of opening a gap
m_gapStartCost - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapStartLogProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapStartOccs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_gapStartProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_globalCounts - Variable in class weka.classifiers.misc.VFI
The global class counts
m_goodPairs - Variable in class weka.clusterers.SemiSupClustererEvaluation
 
m_graphString - Variable in class weka.gui.beans.GraphEvent
 
m_graphTitle - Variable in class weka.gui.beans.GraphEvent
 
m_hashClasses - Variable in class weka.datagenerators.TextSource
A map for looking up classes.
m_hashTokens - Variable in class weka.datagenerators.TextSource
A map for looking up tokens.
m_hashtables - Variable in class weka.associations.Apriori
The same information stored in hash tables.
m_heights - Variable in class weka.gui.visualize.AttributePanel
Holds the random height for each instance.
m_history - Variable in class weka.gui.beans.GraphViewer
 
m_history - Variable in class weka.gui.beans.TextViewer
List of text revieved so far
m_hostList - Variable in class weka.gui.experiment.DistributeExperimentPanel
The host list panel
m_i - Variable in class weka.datagenerators.TextSource.Int
 
m_iNode - Variable in class weka.classifiers.bayes.VaryNode
index of the node varied
m_icon - Variable in class weka.gui.beans.BeanVisual
ImageIcons for the icons.
m_iconPath - Variable in class weka.gui.beans.BeanVisual
Holds name (including path) of the static icon
m_id - Variable in class weka.classifiers.functions.neural.NeuralConnection
The string that uniquely (provided naming is done properly) identifies this unit.
m_id - Variable in class weka.classifiers.trees.j48.ClassifierTree
The id for the node.
m_ignoreBut - Variable in class weka.gui.explorer.ClustererPanel
The button used to popup a list for choosing attributes to ignore while clustering
m_ignoreKeyList - Variable in class weka.gui.explorer.ClustererPanel
 
m_ignoreKeyModel - Variable in class weka.gui.explorer.ClustererPanel
 
m_independentComponents - Variable in class weka.attributeSelection.MatlabICA
Will hold the independent components
m_independentComponentsFilename - Variable in class weka.attributeSelection.MatlabICA
Name of the file where independentComponents will be stored
m_independentComponentsFilenameBase - Variable in class weka.attributeSelection.MatlabICA
 
m_indexVal - Variable in class weka.gui.visualize.AttributePanelEvent
The index for the new attribute
m_inputList - Variable in class weka.classifiers.functions.neural.NeuralConnection
The list of inputs to this unit.
m_inputNums - Variable in class weka.classifiers.functions.neural.NeuralConnection
The numbering for the connections at the other end of the input lines.
m_instanceConstraintHash - Variable in class weka.clusterers.MPCKMeans
 
m_instanceConstraintHash - Variable in class weka.clusterers.PCKMeans
holds the ([instance i] -> [Arraylist of constraints involving i]) mapping.
m_instanceConstraintMap - Variable in class weka.core.metrics.KL
A hashmap that maps every instance to a set of instances with which JS has been computed
m_instanceNormHash - Variable in class weka.core.metrics.KL
We hash sum(p log(p)) terms for the input instances to speed up computation
m_instanceRefHash - Variable in class weka.deduping.blocking.Blocking
Strings are mapped to StringReferences in this hash
m_instanceRefs - Variable in class weka.deduping.blocking.Blocking
A list of all indexed instance.
m_instances - Variable in class weka.associations.Apriori
The instances (transactions) to be used for generating the association rules.
m_instances - Variable in class weka.classifiers.sparse.NaiveBayesSimpleSparse
The instances used for training.
m_instances - Variable in class weka.clusterers.FarthestFirst
training instances, not necessary to keep, could be replaced by m_ClusterCentroids where needed for header info
m_instances - Variable in class weka.clusterers.assigners.LPAssigner
fields to be initialized from m_clusterer
m_instances - Variable in class weka.core.metrics.GDMetricLearner
The training data
m_instances - Variable in class weka.deduping.PairwiseSelector
The set of instances used for training
m_instances - Variable in class weka.deduping.blocking.Blocking
The dataset that contains the instances
m_instances - Variable in class weka.deduping.metrics.KernelVSMetric
The dataset for the vector space attributes
m_instances - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
The dataset of interest
m_instances - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The dataset of interest
m_instances - Variable in class weka.experiment.ExtractionResultProducer
The dataset of interest
m_instancesConsumed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_instancesHash - Variable in class weka.clusterers.HAC
instance hash
m_instancesHash - Variable in class weka.deduping.BasicDeduper
instance hash, where each Integer index is hashed to an instance
m_intervalBounds - Variable in class weka.classifiers.misc.VFI
The lower bounds for each attribute
m_inverseMixingMatrix - Variable in class weka.attributeSelection.MatlabICA
Will hold the inverse of the mixing matrix
m_inverseMixingMatrixFilename - Variable in class weka.attributeSelection.MatlabICA
Name of the file where inverseMixingMatrix will be stored
m_invert - Variable in class weka.filters.unsupervised.attribute.RemoveType
Whether to invert selection
m_invertMatching - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
Whether to invert the match so the correctly classified instances are discarded
m_isClassAttributeString - Static variable in class weka.clusterers.InstancePair
 
m_isClassAttributeString - Variable in class weka.clusterers.MPCKMeans
checks to see if class is a string
m_isDiffClassNominal - Variable in class weka.core.metrics.ClassifierMetricLearner
Class attribute for diff-instances can be either nominal or numeric
m_isDistanceBased - Variable in class weka.clusterers.HAC
Is the metric (and hence the algorithm) relying on similarities or distances?
m_isEmpty - Variable in class weka.classifiers.rules.part.ClassifierDecList
True if node is empty.
m_isEmpty - Variable in class weka.classifiers.trees.j48.ClassifierTree
True if node is empty.
m_isLeaf - Variable in class weka.classifiers.rules.part.ClassifierDecList
True if node is leaf.
m_isLeaf - Variable in class weka.classifiers.trees.j48.ClassifierTree
True if node is leaf.
m_isOfflineMetric - Variable in class weka.clusterers.MPCKMeans
is it an offline metric (BarHillelMetric or XingMetric)?
m_isSparseInstance - Variable in class weka.clusterers.MPCKMeans
indicates whether instances are sparse
m_isSparseInstance - Variable in class weka.clusterers.PCKMeans
indicates whether instances are sparse
m_isSparseInstance - Variable in class weka.clusterers.PCSoftKMeans
indicates whether instances are sparse
m_it - Variable in class weka.datagenerators.TextSource.Table
 
m_items - Variable in class weka.associations.ItemSet
The items stored as an array of of ints.
m_jitter - Variable in class weka.gui.visualize.MatrixPanel
The slider to add jitter to the plots
m_js - Variable in class weka.gui.visualize.MatrixPanel
The scroll pane to scrolling the matrix
m_kNN - Variable in class weka.classifiers.lazy.IBk
The number of neighbours to use for classification (currently)
m_kNN - Variable in class weka.classifiers.lazy.LWR
The number of neighbours used to select the kernel bandwidth
m_kNN - Variable in class weka.classifiers.sparse.IBkMetric
The number of neighbours to use for classification (currently)
m_kNNUpper - Variable in class weka.classifiers.lazy.IBk
The value of kNN provided by the user.
m_kNNUpper - Variable in class weka.classifiers.sparse.IBkMetric
The value of kNN provided by the user.
m_kNNValid - Variable in class weka.classifiers.lazy.IBk
Whether the value of k selected by cross validation has been invalidated by a change in the training instances
m_kNNValid - Variable in class weka.classifiers.sparse.IBkMetric
Whether the value of k selected by cross validation has been invalidated by a change in the training instances
m_kernelType - Variable in class weka.classifiers.sparse.SVMlight
 
m_labelMetrics - Variable in class weka.gui.visualize.Plot2D
 
m_labeledTrainPairs - Variable in class weka.clusterers.SemiSupClustererEvaluation
Training pairs
m_lambdaJM - Variable in class weka.core.metrics.KL
The lambda value for the Jelinek-Mercer smoothing
m_lastAddedSplitNum - Variable in class weka.classifiers.trees.adtree.ADTree
The number of the last splitter added to the tree
m_lastLogLikelihood - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_lastValidationError - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_latexOutput - Variable in class weka.experiment.PairedTTester
Produce tables in latex format
m_learningRate - Variable in class weka.core.metrics.GDMetricLearner
The learning rate
m_left - Variable in class weka.classifiers.trees.m5.RuleNode
child nodes
m_legendPanel - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays legend info if there is more than one plot
m_length - Variable in class weka.deduping.metrics.HashMapVector
Store the length of a vector for efficiency
m_length - Variable in class weka.deduping.metrics.StringReference
The length of the corresponding Document vector.
m_lengthNormalized - Variable in class weka.core.metrics.WeightedDotP
Should cosine similarity be normalized by the length of two instance vectors?
m_lexer - Variable in class weka.datagenerators.TextSource
The lexer.
m_linkingType - Variable in class weka.clusterers.HAC
Default linking method
m_listenee - Variable in class weka.gui.beans.AbstractEvaluator
 
m_listenee - Variable in class weka.gui.beans.AbstractTestSetProducer
non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
non null if this object is a target for any events.
m_listenee - Variable in class weka.gui.beans.AbstractTrainingSetProducer
non null if this object is a target for any events.
m_listeners - Variable in class weka.gui.beans.AbstractDataSource
Objects listening for events from data sources
m_listeners - Variable in class weka.gui.beans.AbstractTestSetProducer
Objects listening to us
m_listeners - Variable in class weka.gui.beans.AbstractTrainingSetProducer
Objects listening for training set events
m_loadEigenValuesFromFile - Variable in class weka.attributeSelection.MatlabICA
load eigenvalues of covariance matrix from file?
m_loadEigenVectorsFromFile - Variable in class weka.attributeSelection.MatlabICA
load eigenvectors of covariance matrix from file?
m_localModel - Variable in class weka.classifiers.rules.part.ClassifierDecList
Local model at node.
m_localModel - Variable in class weka.classifiers.trees.j48.ClassifierTree
Local model at node.
m_logMessage - Variable in class weka.experiment.RemoteExperimentEvent
A log type message
m_logPanel - Variable in class weka.gui.beans.KnowledgeFlow
 
m_logTermWeight - Variable in class weka.clusterers.MPCKMeans
Relative importance of the log-term for the weights in the objective function
m_logTerms - Variable in class weka.clusterers.MPCKMeans
We will hash log terms to avoid recomputing every time TODO: implement for Euclidean
m_logger - Variable in class weka.gui.beans.AbstractEvaluator
 
m_logger - Variable in class weka.gui.beans.AbstractTestSetProducer
Logger
m_logger - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
 
m_logger - Variable in class weka.gui.beans.ClassAssigner
 
m_lowerBoundMinSupport - Variable in class weka.associations.Apriori
The lower bound for the minimum support.
m_lstFilters - Variable in class weka.datagenerators.TextSource
The list of token filters which are applied in order.
m_m - Variable in class weka.classifiers.bayes.NaiveBayesSimple
m parameter for Laplace m estimate, corresponding to size of pseudosample
m_m - Variable in class weka.classifiers.sparse.NaiveBayesSimpleSparse
m parameter for Laplace m estimate, corresponding to size of pseudosample
m_mFile - Variable in class weka.attributeSelection.MatlabNMF
Name of the Matlab program file that computes NMF
m_masterName - Variable in class weka.gui.visualize.Plot2D
The name of the master plot
m_masterPlot - Variable in class weka.gui.visualize.Plot2D
The master plot
m_matchCost - Variable in class weka.deduping.metrics.AffineMetric
The cost of matching two characters
m_maxBatchSizeRequired - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The maximum number of instances required for processing
m_maxBlankIterations - Variable in class weka.clusterers.MPCKMeans
the maximum number of iterations with no points moved
m_maxC - Variable in class weka.gui.visualize.AttributePanel
Holds the min and max values of the colouring attributes
m_maxC - Variable in class weka.gui.visualize.Plot2D
 
m_maxC - Variable in class weka.gui.visualize.PlotData2D
 
m_maxCLDistances - Variable in class weka.clusterers.assigners.LPAssigner
 
m_maxChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The maimum chunk size used for training
m_maxEntrop - Variable in class weka.classifiers.misc.VFI
The maximum entropy for the class
m_maxImplicitCommonTokenFraction - Variable in class weka.deduping.PairwiseSelector
The maximum fraction of common tokens that instances can have to be included as implicit negatives
m_maxIter - Variable in class weka.core.metrics.XingMetric
max number of iterations
m_maxIterations - Variable in class weka.clusterers.MPCKMeans
the maximum number of iterations
m_maxIterations - Variable in class weka.core.metrics.GDMetricLearner
Maximum number of iterations
m_maxMargin - Variable in class weka.classifiers.sparse.SVMlight
SVM-light predictions are positive or negative margins; to convert to a distribution we need min/max margin values...
m_maxModels - Variable in class weka.classifiers.meta.AdditiveRegression
Maximum number of models to produce.
m_maxPoints - Variable in class weka.core.metrics.WeightedMahalanobis
Max instance storage (max is in the space of projected instances, values are in **ORIGINAL** space) Currently somewhat convoluted, TODO: re-write the max value code in MPCKMeans
m_maxProjPoints - Variable in class weka.core.metrics.WeightedMahalanobis
 
m_maxSetNumber - Variable in class weka.gui.beans.BatchClassifierEvent
The last set number for this series
m_maxSetNumber - Variable in class weka.gui.beans.TestSetEvent
Maximum number of sets (ie 10 in a 10 fold)
m_maxSetNumber - Variable in class weka.gui.beans.TrainingSetEvent
Maximum number of sets (ie 10 in a 10 fold)
m_maxVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
The min and max values for this attribute.
m_maxVariancePercentage - Variable in class weka.filters.unsupervised.attribute.RemoveUseless
The type of attribute to delete
m_maxX - Variable in class weka.gui.visualize.Plot2D
Holds the min and max values of the x, y and colouring attributes over all plots
m_maxX - Variable in class weka.gui.visualize.PlotData2D
Holds the min and max values of the x, y and colouring attributes for this plot
m_maxY - Variable in class weka.gui.visualize.Plot2D
 
m_maxY - Variable in class weka.gui.visualize.PlotData2D
 
m_max_iterations - Variable in class weka.classifiers.bayes.SemiSupEM
maximum iterations to perform
m_mergeThreshold - Variable in class weka.clusterers.HAC
The threshold distance beyond which no clusters are merged (except for one - TODO)
m_messageString - Variable in class weka.experiment.RemoteExperimentEvent
The message
m_metric - Variable in class weka.classifiers.sparse.IBkMetric
distance Metric
m_metric - Variable in class weka.clusterers.HAC
metric used to calculate similarity/distance
m_metric - Variable in class weka.clusterers.MPCKMeans
distance Metric
m_metric - Variable in class weka.clusterers.PCKMeans
distance Metric
m_metric - Variable in class weka.clusterers.PCSoftKMeans
distance Metric
m_metric - Variable in class weka.clusterers.SeededKMeans
distance Metric
m_metric - Variable in class weka.clusterers.assigners.LPAssigner
 
m_metric - Variable in class weka.core.metrics.AttrEvalMetricLearner
The metric that the classifier was used to learn, useful for external-calculation based metrics
m_metric - Variable in class weka.core.metrics.ClassifierMetricLearner
The metric that the classifier was used to learn, useful for external-calculation based metrics
m_metric - Variable in class weka.core.metrics.GDMetricLearner
The metric that the classifier was used to learn, useful for external-calculation based metrics
m_metric - Variable in class weka.deduping.metrics.SumInstanceMetric
 
m_metricBuilt - Variable in class weka.clusterers.HAC
has the metric has been constructed? a fix for multiple buildClusterer's
m_metricBuilt - Variable in class weka.clusterers.MPCKMeans
has the metric has been constructed? a fix for multiple buildClusterer's
m_metricBuilt - Variable in class weka.clusterers.PCKMeans
has the metric has been constructed? a fix for multiple buildClusterer's
m_metricBuilt - Variable in class weka.clusterers.PCSoftKMeans
has the metric has been constructed? a fix for multiple buildClusterer's
m_metricBuilt - Variable in class weka.clusterers.SeededKMeans
has the metric has been constructed? a fix for multiple buildClusterer's
m_metricLearner - Variable in class weka.core.metrics.KL
A metric learner responsible for training the parameters of the metric
m_metricLearner - Variable in class weka.core.metrics.WeightedDotP
A metric learner responsible for training the parameters of the metric
m_metricLearner - Variable in class weka.core.metrics.WeightedEuclidean
A metric learner responsible for training the parameters of the metric
m_metricName - Variable in class weka.clusterers.HAC
 
m_metricName - Variable in class weka.clusterers.SeededKMeans
Name of metric
m_metricType - Variable in class weka.associations.Apriori
The selected metric type.
m_metrics - Variable in class weka.clusterers.MPCKMeans
 
m_metrics - Variable in class weka.clusterers.assigners.LPAssigner
 
m_metrics - Variable in class weka.deduping.metrics.InstanceMetric
 
m_minC - Variable in class weka.gui.visualize.AttributePanel
 
m_minC - Variable in class weka.gui.visualize.Plot2D
 
m_minC - Variable in class weka.gui.visualize.PlotData2D
 
m_minChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The minimum chunk size used for training
m_minCommonTokens - Variable in class weka.deduping.metrics.SumInstanceMetric
We may require objects to have a minimum number of common tokens for them to be considered for distance computation
m_minLogLikelihoodIncr - Static variable in class weka.classifiers.bayes.SemiSupEM
 
m_minMargin - Variable in class weka.classifiers.sparse.SVMlight
 
m_minMetric - Variable in class weka.associations.Apriori
The minimum metric score.
m_minNumInstances - Variable in class weka.classifiers.trees.m5.M5Base
The minimum number of instances to allow at a leaf node
m_minNumObj - Variable in class weka.classifiers.rules.part.ClassifierDecList
Minimum number of objects
m_minStdDev - Variable in class weka.classifiers.bayes.NaiveBayesSimple
default minimum standard deviation
m_minSupport - Variable in class weka.associations.Apriori
The minimum support.
m_minTokenLength - Variable in class weka.deduping.metrics.WordTokenizer
The default minimum length of a token
m_minVal - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
 
m_minX - Variable in class weka.gui.visualize.Plot2D
 
m_minX - Variable in class weka.gui.visualize.PlotData2D
 
m_minY - Variable in class weka.gui.visualize.Plot2D
 
m_minY - Variable in class weka.gui.visualize.PlotData2D
 
m_mixingMatrix - Variable in class weka.attributeSelection.MatlabICA
Will hold the mixing matrix
m_mixingMatrixFilename - Variable in class weka.attributeSelection.MatlabICA
Name of the file where mixingMatrix will be stored
m_mixingMatrixFilenameBase - Variable in class weka.attributeSelection.MatlabICA
 
m_mode - Variable in class weka.classifiers.sparse.SVMlight
 
m_mode - Variable in class weka.extraction.ClusteringExtractor
 
m_modePanel - Variable in class weka.gui.experiment.SimpleSetupPanel
The panel which switched between simple and advanced setup modes
m_modelFilename - Variable in class weka.classifiers.sparse.SVMlight
 
m_modelFilenameBase - Variable in class weka.classifiers.sparse.SVMlight
Name of the file where a model will be temporarily created
m_modelHasChanged - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_modelHasChangedLL - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_models - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_n - Variable in class weka.deduping.metrics.NGramTokenizer
Length of an n-gram
m_nCount - Variable in class weka.classifiers.bayes.ADNode
count
m_nDF - Variable in class weka.datagenerators.TextSource.Token
The document frequency.
m_nID - Variable in class weka.datagenerators.TextSource.Token
The token ID, which is the same as the attribute index.
m_nIndex - Variable in class weka.datagenerators.TextSource.Table
 
m_nMCV - Variable in class weka.classifiers.bayes.VaryNode
most common value
m_nNextToken - Variable in class weka.datagenerators.TextSource
The next token ID.
m_nOrder - Variable in class weka.classifiers.bayes.BayesNet
topological ordering of the network
m_nStartNode - Variable in class weka.classifiers.bayes.ADNode
first node in VaryNode array
m_name - Variable in class weka.gui.treevisualizer.NamedColor
The name of the color
m_negMatrixFilename - Variable in class weka.core.metrics.MatlabMetricLearner
Name of the temporary file where the matrix representing the diff-class diff.
m_negPairList - Variable in class weka.deduping.PairwiseSelector
A list with a sufficient pool of negative examples as TrainingPair's
m_negStringMode - Variable in class weka.deduping.PairwiseSelector
 
m_negTrainInstances - Variable in class weka.classifiers.trees.adtree.ADTree
The training instances with negative class - referencing the training dataset
m_negativesMode - Variable in class weka.core.metrics.HardPairwiseSelector
 
m_negativesMode - Variable in class weka.deduping.PairwiseSelector
 
m_newValidationFs - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_nodesExpanded - Variable in class weka.classifiers.trees.adtree.ADTree
Statistics - the number of prediction nodes investigated during search
m_nominalAttIndices - Variable in class weka.classifiers.trees.adtree.ADTree
An array containing the inidices to the nominal attributes in the data
m_noopCost - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_noopLogProb - Variable in class weka.deduping.metrics.AffineProbMetric
Parameters for the generative model
m_noopOccs - Variable in class weka.deduping.metrics.AffineProbMetric
Parameters for the generative model
m_noopProb - Variable in class weka.deduping.metrics.AffineProbMetric
Parameters for the generative model
m_normal - Static variable in class weka.clusterers.Cobweb
Normal constant.
m_normalized - Variable in class weka.deduping.metrics.AffineMetric
Should the distance be normalized by the lengths of the strings?
m_normalized - Variable in class weka.deduping.metrics.AffineProbMetric
Normalization of edit distance by string length; equivalent to using the posterior probability in the generative model
m_numActualDupePairsTrain - Variable in class weka.deduping.BasicDeduper
 
m_numActualNegPairs - Variable in class weka.deduping.metrics.InstanceMetric
 
m_numActualNonDupePairsTrain - Variable in class weka.deduping.BasicDeduper
 
m_numActualPosPairs - Variable in class weka.deduping.metrics.InstanceMetric
The actual number of training pairs used in the last training round
m_numAttributes - Variable in class weka.classifiers.sparse.NaiveBayesSimpleSparse
The total number of features
m_numAttributes - Variable in class weka.core.metrics.Metric
number of attributes
m_numAttributesSelected - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The number of attributes selected by the attribute selection phase
m_numAtts - Variable in class weka.classifiers.lazy.LBR
number of attributes for the dataset
m_numBlankIterations - Variable in class weka.clusterers.MPCKMeans
keep track of the number of iterations when no points were moved
m_numCLConstraints - Variable in class weka.clusterers.assigners.LPAssigner
 
m_numClasses - Variable in class weka.classifiers.lazy.LBR
number of classes for dataset
m_numClasses - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The number of class vals in the training data (1 if class is numeric)
m_numClasses - Variable in class weka.classifiers.sparse.NaiveBayesSimpleSparse
The number of classes
m_numClusters - Variable in class weka.clusterers.HAC
Number of clusters
m_numClusters - Variable in class weka.clusterers.assigners.LPAssigner
 
m_numClusters - Variable in class weka.deduping.DedupingEvaluation
The number of produced clusters
m_numConstraintVars - Variable in class weka.clusterers.assigners.LPAssigner
 
m_numConstraints - Variable in class weka.clusterers.assigners.LPAssigner
 
m_numCurrentClusters - Variable in class weka.clusterers.HAC
Number of clusters in the process
m_numCurrentObjects - Variable in class weka.deduping.BasicDeduper
Number of clusters in the process
m_numFeatures - Variable in class weka.classifiers.trees.RandomForest
Number of features to consider in random feature selection.
m_numFolds - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
The number of folds in the cross-validation
m_numFolds - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The number of folds in the cross-validation
m_numFolds - Variable in class weka.experiment.ExtractionResultProducer
The number of folds in the cross-validation
m_numFolds - Variable in class weka.gui.experiment.SimpleSetupPanel
The number of folds for a cross-validation experiment
m_numGoodPairs - Variable in class weka.deduping.BasicDeduper
 
m_numInputs - Variable in class weka.classifiers.functions.neural.NeuralConnection
The number of inputs.
m_numInstances - Variable in class weka.classifiers.trees.m5.RuleNode
the number of instances reaching this node
m_numInstances - Variable in class weka.clusterers.assigners.LPAssigner
 
m_numInstancesConsumed - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The number of instances consumed
m_numInsts - Variable in class weka.classifiers.lazy.LBR
number of instances in dataset
m_numIterations - Variable in class weka.classifiers.functions.Winnow
The number of iterations
m_numIterations - Variable in class weka.deduping.metrics.AffineProbMetric
Maximum number of iterations for training the model; usually converge in <10 iterations
m_numLabelVars - Variable in class weka.clusterers.assigners.LPAssigner
 
m_numMLConstraints - Variable in class weka.clusterers.assigners.LPAssigner
 
m_numNegPairs - Variable in class weka.core.metrics.ClassifierMetricLearner
 
m_numNegPairs - Variable in class weka.core.metrics.GDMetricLearner
 
m_numNegPairs - Variable in class weka.deduping.metrics.ClassifierInstanceMetric
 
m_numNegPairs - Variable in class weka.deduping.metrics.SumInstanceMetric
 
m_numNeighborhoods - Variable in class weka.clusterers.MPCKMeans
number of neighborhood sets
m_numObjects - Variable in class weka.deduping.BasicDeduper
The total number of true objects
m_numOfCleansingIterations - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The maximum number of cleansing iterations to perform (<1 = until fully cleansed)
m_numOfCrossValidationFolds - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The number of cross validation folds to perform (<2 = no cross validation)
m_numOutputs - Variable in class weka.classifiers.functions.neural.NeuralConnection
The number of outputs.
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_numPosDiffInstances - Variable in class weka.core.metrics.LearnableMetric
The maximum number of same-class examples to construct diff-instances from
m_numPosPairs - Variable in class weka.core.metrics.ClassifierMetricLearner
 
m_numPosPairs - Variable in class weka.core.metrics.GDMetricLearner
 
m_numPosPairs - Variable in class weka.deduping.metrics.ClassifierInstanceMetric
The desired number of training pairs
m_numPosPairs - Variable in class weka.deduping.metrics.SumInstanceMetric
The number of positive pairs desired for training
m_numPotentialDupePairsTrain - Variable in class weka.deduping.BasicDeduper
 
m_numPotentialNegatives - Variable in class weka.core.metrics.PairwiseSelector
The number of possible different-class pairs
m_numPotentialNegatives - Variable in class weka.deduping.PairwiseSelector
The number of possible different-class pairs
m_numPotentialNonDupePairsTrain - Variable in class weka.deduping.BasicDeduper
 
m_numPotentialPositives - Variable in class weka.core.metrics.PairwiseSelector
The number of possible same-class pairs
m_numPotentialPositives - Variable in class weka.deduping.PairwiseSelector
The number of possible same-class pairs
m_numRepetitions - Variable in class weka.gui.experiment.SimpleSetupPanel
The number of times to repeat the sub-experiment
m_numRules - Variable in class weka.associations.Apriori
The maximum number of rules that are output.
m_numSeededClusters - Variable in class weka.clusterers.HAC
Number of seeded clusters
m_numStringParts - Variable in class weka.deduping.metrics.KernelVSMetric
The number of vector spaces
m_numSubsets - Variable in class weka.classifiers.trees.j48.ClassifierSplitModel
Number of created subsets.
m_numTotalPairs - Variable in class weka.deduping.BasicDeduper
Statistics
m_numTotalPairsTest - Variable in class weka.deduping.BasicDeduper
 
m_numTotalPairsTrain - Variable in class weka.deduping.BasicDeduper
 
m_numTotalTokens - Variable in class weka.core.metrics.KL
Total number of tokens in the dataset
m_numTrees - Variable in class weka.classifiers.trees.RandomForest
Number of trees in forest.
m_numTruePairs - Variable in class weka.deduping.BasicDeduper
 
m_numVars - Variable in class weka.clusterers.assigners.LPAssigner
 
m_numberAdditionalMeasures - Variable in class weka.experiment.ClassifierSplitEvaluator
The number of additional measures that need to be filled in after taking into account column constraints imposed by the final destination for results
m_numberMerges - Variable in class weka.clusterers.Cobweb
 
m_numberOfClusters - Variable in class weka.clusterers.Cobweb
Number of clusters (nodes in the tree).
m_numberSplits - Variable in class weka.clusterers.Cobweb
 
m_numericAttIndices - Variable in class weka.classifiers.trees.adtree.ADTree
An array containing the inidices to the numeric attributes in the data
m_numericClassifyThreshold - Variable in class weka.filters.unsupervised.instance.RemoveMisclassified
The threshold for deciding when a numeric value is correctly classified
m_objCannotLinks - Variable in class weka.clusterers.MPCKMeans
 
m_objCannotLinksCurrPoint - Variable in class weka.clusterers.MPCKMeans
 
m_objCannotLinksCurrPointBest - Variable in class weka.clusterers.MPCKMeans
 
m_objFunDecreasing - Variable in class weka.clusterers.MPCKMeans
Is the objective function increasing or decreasing? Depends on type of metric used: for similarity-based metric, increasing, for distance-based - decreasing
m_objFunDecreasing - Variable in class weka.clusterers.PCKMeans
Is the objective function increasing or decreasing? Depends on type of metric used: for similarity-based metric - increasing, for distance-based - decreasing
m_objFunDecreasing - Variable in class weka.clusterers.PCSoftKMeans
Is the objective function increasing or decreasing? Depends on type of metric used: for similarity-based metric - increasing, for distance-based - decreasing
m_objFunDecreasing - Variable in class weka.clusterers.SeededKMeans
Is the objective function increasing or decreasing? Depends on type of metric used: for similarity-based metric, increasing, for distance-based - decreasing
m_objMustLinks - Variable in class weka.clusterers.MPCKMeans
 
m_objMustLinksCurrPoint - Variable in class weka.clusterers.MPCKMeans
 
m_objMustLinksCurrPointBest - Variable in class weka.clusterers.MPCKMeans
 
m_objNormalizer - Variable in class weka.clusterers.MPCKMeans
 
m_objNormalizerCurrPoint - Variable in class weka.clusterers.MPCKMeans
 
m_objNormalizerCurrPointBest - Variable in class weka.clusterers.MPCKMeans
 
m_objVariance - Variable in class weka.clusterers.MPCKMeans
Variables to track components of the objective function
m_objVarianceCurrPoint - Variable in class weka.clusterers.MPCKMeans
Variable to track the contribution of the currently considered point
m_objVarianceCurrPointBest - Variable in class weka.clusterers.MPCKMeans
 
m_okBut - Variable in class weka.gui.GenericObjectEditor.GOEPanel
ok button
m_oldWidth - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
Used to determine if the positions need to be recalculated.
m_originalPlot - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The master plot
m_outFilename - Variable in class weka.clusterers.assigners.LPAssigner
 
m_outFilenameBase - Variable in class weka.clusterers.assigners.LPAssigner
 
m_outputFile - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
The destination output file/directory for raw output
m_outputFile - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The destination output file/directory for raw output
m_outputFile - Variable in class weka.experiment.ExtractionResultProducer
The destination output file/directory for raw output
m_outputItemSets - Variable in class weka.associations.Apriori
Output itemsets found?
m_outputList - Variable in class weka.classifiers.functions.neural.NeuralConnection
The list of outputs from this unit.
m_outputNums - Variable in class weka.classifiers.functions.neural.NeuralConnection
The numbering for the connections at the other end of the out lines.
m_pairList - Variable in class weka.core.metrics.GDMetricLearner
 
m_pairSet - Variable in class weka.deduping.blocking.Blocking
A TreeSet where the InstancePairs are stored for subsequent retrieval
m_plot - Variable in class weka.gui.visualize.VisualizePanel
The panel that displays the plot
m_plot2D - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The actual generic plotting panel
m_plotCompanion - Variable in class weka.gui.visualize.Plot2D
An optional "compainion" of the panel.
m_plotInstances - Variable in class weka.gui.visualize.AttributePanel
The instances to be plotted
m_plotInstances - Variable in class weka.gui.visualize.Plot2D
The instances to be plotted
m_plotInstances - Variable in class weka.gui.visualize.PlotData2D
The instances
m_plotInstances - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
The instances from the master plot
m_plotName - Variable in class weka.gui.visualize.PlotData2D
The name of this plot
m_plotName - Variable in class weka.gui.visualize.VisualizePanel
The name of the plot (not currently displayed, but can be used in the containing Frame or Panel)
m_plotPoints - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_plotPoints - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_plotPoints - Variable in class weka.experiment.ExtractionResultProducer
The specific points to plot, either integers representing specific numbers of training examples, or decimal fractions representing percentages of the full training set
m_plotSurround - Variable in class weka.gui.visualize.VisualizePanel
Panel that surrounds the plot panel with a titled border
m_plots - Variable in class weka.gui.visualize.LegendPanel
the list of plot elements
m_plots - Variable in class weka.gui.visualize.Plot2D
The plots to display
m_pointDrawn - Variable in class weka.gui.visualize.AttributePanel.AttributeSpacing
A temporary array used to strike any instances that would be drawn redundantly.
m_pointLookup - Variable in class weka.gui.visualize.PlotData2D
Panel coordinate cache for data points
m_posMatrixFilename - Variable in class weka.core.metrics.MatlabMetricLearner
Name of the temporary file where the matrix representing the same-class diff.
m_posNegDiffInstanceRatio - Variable in class weka.core.metrics.LearnableMetric
Proportion of different-class versus same-class diff-instances
m_posPairList - Variable in class weka.deduping.PairwiseSelector
A list with all the positive examples as TrainingPair's
m_posStringMode - Variable in class weka.deduping.PairwiseSelector
 
m_posTrainInstances - Variable in class weka.classifiers.trees.adtree.ADTree
The training instances with positive class - referencing the training dataset
m_positivesMode - Variable in class weka.core.metrics.HardPairwiseSelector
 
m_positivesMode - Variable in class weka.deduping.PairwiseSelector
 
m_predictionFilename - Variable in class weka.classifiers.sparse.SVMlight
 
m_predictionFilenameBase - Variable in class weka.classifiers.sparse.SVMlight
Name of the file where predictions will be temporarily stored unless buffered IO is used
m_preferredColourDimension - Variable in class weka.gui.visualize.VisualizePanel
 
m_preferredXDimension - Variable in class weka.gui.visualize.VisualizePanel
These hold the names of preferred columns to visualize on---if the user has defined them in the Visualize.props file
m_preferredYDimension - Variable in class weka.gui.visualize.VisualizePanel
 
m_priors - Variable in class weka.classifiers.sparse.NaiveBayesSimpleSparse
The prior probabilities of the classes.
m_procReader - Variable in class weka.classifiers.sparse.SVMlight
 
m_procWriter - Variable in class weka.classifiers.sparse.SVMlight
 
m_progFilename - Variable in class weka.clusterers.assigners.LPAssigner
 
m_projectedInstanceHash - Variable in class weka.core.metrics.WeightedMahalanobis
A hash where instances are projected using the weights
m_pseudoCountDirichlet - Variable in class weka.core.metrics.KL
The pseudocount value for the Dirichlet smoothing
m_random - Variable in class weka.classifiers.trees.RandomTree
Random number generator.
m_random - Variable in class weka.classifiers.trees.adtree.ADTree
The random number generator - used for the random search heuristic
m_randomGen - Variable in class weka.clusterers.HAC
 
m_randomSeed - Variable in class weka.classifiers.trees.RandomForest
The random seed.
m_randomSeed - Variable in class weka.classifiers.trees.RandomTree
The random seed to use.
m_randomSeed - Variable in class weka.classifiers.trees.adtree.ADTree
Option - the seed to use for a random search
m_randomSeed - Variable in class weka.clusterers.HAC
holds the random Seed, useful for random selection initialization
m_randomSeed - Variable in class weka.clusterers.SeededKMeans
holds the random Seed, useful for randomPerturbInit
m_randomize - Variable in class weka.experiment.RandomSplitResultProducer
Whether dataset is to be randomized
m_reader - Variable in class weka.datagenerators.TextSource
The document reader.
m_regressionTree - Variable in class weka.classifiers.trees.m5.M5Base
Make a regression tree/rule instead of a model tree/rule
m_relativeCheck - Variable in class weka.gui.experiment.DatasetListPanel
Make file paths relative to the user (start) directory
m_remoteHosts - Variable in class weka.experiment.RemoteExperiment
Holds the names of machines with remoteEngine servers running
m_removeInconsistentExamples - Variable in class weka.classifiers.sparse.SVMlight
remove inconsistent training examples and retrain
m_removeMissingCols - Variable in class weka.associations.Apriori
 
m_resampleBt - Variable in class weka.gui.visualize.MatrixPanel
The label for resample percentage
m_resamplePercent - Variable in class weka.gui.visualize.MatrixPanel
The text area for percentage to resample data
m_result - Variable in class weka.experiment.ClassifierSplitEvaluator
Holds the statistics for the most recent application of the classifier
m_result - Variable in class weka.experiment.DeduperSplitEvaluator
Holds the statistics for the most recent application of the deduper
m_result - Variable in class weka.experiment.ExtractionSplitEvaluator
Holds the statistics for the most recent application of the extractor
m_result - Variable in class weka.experiment.RegressionSplitEvaluator
Holds the statistics for the most recent application of the classifier
m_result - Variable in class weka.experiment.SemiSupClustererSplitEvaluator
Holds the statistics for the most recent application of the clusterer
m_resultListener - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
The ResultListener to send results to
m_resultListener - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The ResultListener to send results to
m_resultListener - Variable in class weka.experiment.ExtractionResultProducer
The ResultListener to send results to
m_retrieval - Variable in class weka.core.converters.AbstractLoader
The current retrieval mode
m_reverseInstancesHash - Variable in class weka.clusterers.HAC
reverse instance hash
m_reverseInstancesHash - Variable in class weka.deduping.BasicDeduper
reverse instance hash, where each instance is hashed to its Integer index
m_right - Variable in class weka.classifiers.trees.m5.RuleNode
 
m_root - Variable in class weka.classifiers.trees.adtree.ADTree
The root of the tree
m_rseed - Variable in class weka.classifiers.bayes.SemiSupEM
 
m_rseed - Variable in class weka.gui.visualize.MatrixPanel
Random seed for random subsample
m_ruleSet - Variable in class weka.classifiers.trees.m5.M5Base
the rule set
m_s - Variable in class weka.classifiers.sparse.SVMlight
Parameter s in sigmoid/polynomial kernel
m_sIndex - Variable in class weka.gui.visualize.Plot2D
 
m_sIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_saveBut - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to save the visualized set of instances
m_saveInstanceData - Variable in class weka.classifiers.trees.adtree.ADTree
Option - whether the tree should remember the instance data
m_saveInstances - Variable in class weka.classifiers.trees.m5.M5Base
Save instances at each node in an M5 tree for visualization purposes.
m_saveInstances - Variable in class weka.clusterers.Cobweb
Output instances in graph representation of Cobweb tree (Allows instances at nodes in the tree to be visualized in the Explorer).
m_scriptFilename - Variable in class weka.core.metrics.MatlabMetricLearner
Matlab program that is used for learning metric weights
m_searchPath - Variable in class weka.classifiers.trees.adtree.ADTree
Option - the search mode
m_search_bestInsertionNode - Variable in class weka.classifiers.trees.adtree.ADTree
The best node to insert under, as found so far by the latest search
m_search_bestPathNegInstances - Variable in class weka.classifiers.trees.adtree.ADTree
The negative instances that apply to the best path found so far
m_search_bestPathPosInstances - Variable in class weka.classifiers.trees.adtree.ADTree
The positive instances that apply to the best path found so far
m_search_bestSplitter - Variable in class weka.classifiers.trees.adtree.ADTree
The best splitter to insert, as found so far by the latest search
m_search_smallestZ - Variable in class weka.classifiers.trees.adtree.ADTree
The smallest Z value found so far by the latest search
m_seedUnseenClasses - Variable in class weka.classifiers.bayes.SemiSupEM
Create soft labeled Seed for unseen classes
m_seedable - Variable in class weka.clusterers.HAC
seeding
m_selAttrib - Variable in class weka.gui.visualize.MatrixPanel
The button to display a window to select attributes
m_selectionTime - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The time taken to select attributes in milliseconds
m_selector - Variable in class weka.core.metrics.ClassifierMetricLearner
The pairwise selector used by the metric
m_selector - Variable in class weka.core.metrics.GDMetricLearner
The pairwise selector used by the metric
m_separateTrainingFile - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The separate training file if desired
m_setNumber - Variable in class weka.gui.beans.BatchClassifierEvent
The set number for the test set
m_setNumber - Variable in class weka.gui.beans.TestSetEvent
what number is this test set (ie fold 2 of 10 folds)
m_setNumber - Variable in class weka.gui.beans.TrainingSetEvent
what number is this training set (ie fold 2 of 10 folds)
m_shapeSize - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the size of points.
m_shapeType - Variable in class weka.gui.visualize.PlotData2D
Additional optional information to control the point shape for this data.
m_showAttBars - Variable in class weka.gui.visualize.VisualizePanel
Show the attribute bar panel
m_shrinkage - Variable in class weka.classifiers.meta.AdditiveRegression
Shrinkage (Learning rate).
m_significanceLevel - Variable in class weka.associations.Apriori
Significance level for optional significance test.
m_simConstraintsFilename - Variable in class weka.core.metrics.XingMetric
 
m_simConstraintsFilenameToken - Variable in class weka.core.metrics.XingMetric
Name of the file where simConstaints will be stored
m_simplePanel - Variable in class weka.gui.experiment.SetupModePanel
The simple setup panel
m_singlePass - Variable in class weka.clusterers.assigners.RMNAssigner
Inference can be single-pass approximate or multi-pass approximate
m_skipHash - Variable in class weka.clusterers.SeededKMeans
Points that are to be skipped in the clustering process because they are collapsed to zero
m_smoothingType - Variable in class weka.core.metrics.KL
The smoothing method
m_sons - Variable in class weka.classifiers.rules.part.ClassifierDecList
References to sons.
m_sons - Variable in class weka.classifiers.trees.j48.ClassifierTree
References to sons.
m_sourceFile - Variable in class weka.core.converters.C45Loader
Holds the source of the data set.
m_sourceFile - Variable in class weka.core.converters.CSVLoader
Holds the source of the data set.
m_spaceChars - Variable in class weka.deduping.metrics.NGramTokenizer
 
m_spaceEquivalents - Variable in class weka.deduping.metrics.NGramTokenizer
A default set of space-equivalent characters
m_span - Variable in class weka.gui.visualize.AttributePanel
The container window for the attribute bars, and also where the X,Y or B get printed.
m_span - Variable in class weka.gui.visualize.LegendPanel
the panel that contains the legend entries
m_splitByDataSet - Variable in class weka.experiment.RemoteExperiment
If true, then sub experiments are created on the basis of data sets rather than run number.
m_splitByDataSet - Variable in class weka.gui.experiment.DistributeExperimentPanel
Split experiment up by data set.
m_splitByRun - Variable in class weka.gui.experiment.DistributeExperimentPanel
Split experiment up by run number.
m_splitCrit - Static variable in class weka.classifiers.rules.part.ClassifierDecList
To compute the entropy.
m_splitEvaluator - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
The SplitEvaluator used to generate results
m_splitEvaluator - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The SplitEvaluator used to generate results
m_splitEvaluator - Variable in class weka.experiment.ExtractionResultProducer
The SplitEvaluator used to generate results
m_splitListener - Variable in class weka.gui.visualize.VisualizePanel
An optional listener that we will inform when the user creates a split to seperate instances.
m_statistics - Variable in class weka.deduping.Deduper
An arraylist of Object arrays containing statistics
m_statistics - Variable in class weka.extraction.Extractor
An arraylist of Object arrays containing statistics
m_statusMessage - Variable in class weka.experiment.RemoteExperimentEvent
A status type message
m_stemmer - Variable in class weka.deduping.metrics.Tokenizer
 
m_stemming - Variable in class weka.deduping.metrics.Tokenizer
Stemming
m_stopwordFilename - Static variable in class weka.deduping.metrics.Tokenizer
The with the stopword list
m_stopwordRemoval - Variable in class weka.deduping.metrics.Tokenizer
Stopword removal
m_stopwordSet - Static variable in class weka.deduping.metrics.Tokenizer
Stopword hash
m_strDocReader - Variable in class weka.datagenerators.TextSource
The option string for document reader.
m_strFilters - Variable in class weka.datagenerators.TextSource
The option string for token filters.
m_strLexer - Variable in class weka.datagenerators.TextSource
The option string for lexer.
m_strToken - Variable in class weka.datagenerators.TextSource.Token
The token string.
m_string - Variable in class weka.deduping.metrics.StringReference
The referenced string.
m_string - Variable in class weka.deduping.metrics.TokenString
 
m_stringMetrics - Variable in class weka.deduping.metrics.ClassifierInstanceMetric
StringMetric prototype that are to be used on each field
m_stringMetrics - Variable in class weka.deduping.metrics.SumInstanceMetric
An array of StringMetrics that are to be used on each attribute
m_stringRef - Variable in class weka.deduping.metrics.TokenOccurrence
A reference to the Document where it occurs
m_stringRefHash - Variable in class weka.deduping.metrics.JaccardMetric
Strings are mapped to StringReferences in this hash
m_stringRefHash - Variable in class weka.deduping.metrics.KernelVSMetric
Strings are mapped to StringReferences in this hash
m_stringRefHash - Variable in class weka.deduping.metrics.VectorSpaceMetric
Strings are mapped to StringReferences in this hash
m_stringRefs - Variable in class weka.deduping.metrics.JaccardMetric
A list of all indexed strings.
m_stringRefs - Variable in class weka.deduping.metrics.KernelVSMetric
A list of all indexed strings.
m_stringRefs - Variable in class weka.deduping.metrics.VectorSpaceMetric
A list of all indexed strings.
m_structure - Variable in class weka.core.converters.ArffLoader
Holds the determined structure (header) of the data set.
m_structure - Variable in class weka.core.converters.C45Loader
Holds the determined structure (header) of the data set.
m_structure - Variable in class weka.core.converters.CSVLoader
Holds the determined structure (header) of the data set.
m_subCost - Variable in class weka.deduping.metrics.AffineMetric
The cost of a substituting two characters
m_subCost - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_subExpComplete - Variable in class weka.experiment.RemoteExperiment
The status of each of the sub-experiments
m_subExperiments - Variable in class weka.experiment.RemoteExperiment
The sub experiments
m_subInstances - Variable in class weka.classifiers.lazy.LBR
 
m_subLogProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_subOccs - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_subOldErrorFlags - Variable in class weka.classifiers.lazy.LBR
 
m_subProb - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_submit - Variable in class weka.gui.visualize.VisualizePanel
Button for the user to enter the splits.
m_svmTrained - Variable in class weka.classifiers.sparse.SVMlight
Has the SVM been trained
m_tCounts - Variable in class weka.classifiers.lazy.LBR
All the counts for nominal attributes.
m_tPriors - Variable in class weka.classifiers.lazy.LBR
The prior probabilities of the classes.
m_table - Variable in class weka.datagenerators.TextSource
The example table.
m_tempDirFile - Variable in class weka.classifiers.sparse.SVMlight
 
m_tempDirFile - Variable in class weka.clusterers.assigners.LPAssigner
 
m_tempDirPath - Variable in class weka.classifiers.sparse.SVMlight
Path to the directory where temporary files will be stored
m_tempDirPath - Variable in class weka.clusterers.assigners.LPAssigner
Path to the directory where temporary files will be stored
m_tempUndoFiles - Variable in class weka.gui.explorer.PreprocessPanel
Keeps track of undo points
m_tempUndoIndex - Variable in class weka.gui.explorer.PreprocessPanel
The next available slot for an undo point
m_test - Variable in class weka.classifiers.rules.part.ClassifierDecList
The pruning instances.
m_test - Variable in class weka.classifiers.trees.j48.ClassifierTree
The pruning instances.
m_testFilename - Variable in class weka.classifiers.sparse.SVMlight
 
m_testFilenameBase - Variable in class weka.classifiers.sparse.SVMlight
Name of the temporary file where a test instance is dumped if buffered IO is not used
m_testInstances - Variable in class weka.deduping.BasicDeduper
A set of instances to dedupe
m_testInstances - Variable in class weka.deduping.DedupingEvaluation
Test instances
m_testInstances - Variable in class weka.extraction.ExtractionEvaluation
Test instances
m_testListeners - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
Objects listening for test set events
m_testSet - Variable in class weka.gui.beans.BatchClassifierEvent
Instances that can be used for testing the classifier
m_testSet - Variable in class weka.gui.beans.TestSetEvent
The test set instances
m_testTimeStart - Variable in class weka.deduping.BasicDeduper
 
m_text - Variable in class weka.gui.beans.TextEvent
The text
m_textTitle - Variable in class weka.gui.beans.TextEvent
The title for the text.
m_toSelectModel - Variable in class weka.classifiers.rules.part.ClassifierDecList
The model selection method.
m_toSelectModel - Variable in class weka.classifiers.trees.j48.ClassifierTree
The model selection method.
m_tokenAttrMap - Variable in class weka.deduping.metrics.KernelVSMetric
A HashMap where each token is mapped to the corresponding Attribute
m_tokenHash - Variable in class weka.deduping.blocking.Blocking
A HashMap where tokens are indexed.
m_tokenHash - Variable in class weka.deduping.metrics.JaccardMetric
A HashMap where tokens are indexed.
m_tokenHash - Variable in class weka.deduping.metrics.KernelVSMetric
A HashMap where tokens are indexed.
m_tokenHash - Variable in class weka.deduping.metrics.VectorSpaceMetric
A HashMap where tokens are indexed.
m_tokenizer - Variable in class weka.deduping.blocking.Blocking
An underlying tokenizer that is used for converting strings into HashMapVectors
m_tokenizer - Variable in class weka.deduping.metrics.JaccardMetric
An underlying tokenizer that is used for converting strings into HashMapVectors
m_tokenizer - Variable in class weka.deduping.metrics.KernelVSMetric
An underlying tokenizer that is used for converting strings into HashMapVectors
m_tokenizer - Variable in class weka.deduping.metrics.VectorSpaceMetric
An underlying tokenizer that is used for converting strings into HashMapVectors
m_topOfTree - Variable in class weka.classifiers.trees.m5.Rule
the top of the m5 tree for this rule
m_totalPairs - Variable in class weka.clusterers.SemiSupClustererEvaluation
If the class is not nominal, we do not need the confusion matrix but do pairs counts directly
m_totalTime - Variable in class weka.classifiers.meta.AttributeSelectedClassifier
The time taken to select attributes AND build the classifier
m_totalTransactions - Variable in class weka.associations.ItemSet
The total number of transactions
m_train - Variable in class weka.classifiers.rules.part.ClassifierDecList
The training instances.
m_train - Variable in class weka.classifiers.sparse.SVMlight
The training instances used for classification.
m_train - Variable in class weka.classifiers.trees.j48.ClassifierTree
The training instances.
m_trainFilename - Variable in class weka.classifiers.sparse.SVMlight
 
m_trainFilenameBase - Variable in class weka.classifiers.sparse.SVMlight
Name of the temporary file where training data will be dumped temporarily
m_trainInstances - Variable in class weka.classifiers.trees.adtree.ADTree
The instances used to train the tree
m_trainInstances - Variable in class weka.deduping.DedupingEvaluation
Training instances
m_trainInstances - Variable in class weka.extraction.ExtractionEvaluation
Training instances
m_trainPercent - Variable in class weka.gui.experiment.SimpleSetupPanel
The training percentage for a train/test split experiment
m_trainProportion - Variable in class weka.deduping.BasicDeduper
The proportion of the training fold that should be used for training
m_trainTime - Variable in class weka.deduping.BasicDeduper
 
m_trainTotalWeight - Variable in class weka.classifiers.trees.adtree.ADTree
The total weight of the instances - used to speed Z calculations
m_trainable - Variable in class weka.core.metrics.LearnableMetric
True if metric learning is used.
m_trained - Variable in class weka.deduping.metrics.KernelVSMetric
has the classifier been trained?
m_trainingListeners - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
Objects listening for trainin set events
m_trainingSet - Variable in class weka.gui.beans.TrainingSetEvent
The training instances
m_treeNodeOfCurrentObject - Variable in class weka.gui.GenericObjectEditor
The tree node of the current object so we can re-select it for the user
m_trueGoodPairs - Variable in class weka.clusterers.SemiSupClustererEvaluation
 
m_ts - Variable in class weka.datagenerators.TextSource.Table
 
m_type - Variable in class weka.classifiers.functions.neural.NeuralConnection
The type of unit this is.
m_unitError - Variable in class weka.classifiers.functions.neural.NeuralConnection
The error value for this unit, NaN if not calculated.
m_unitValue - Variable in class weka.classifiers.functions.neural.NeuralConnection
The output value for this unit, NaN if not calculated.
m_updateBt - Variable in class weka.gui.visualize.MatrixPanel
The button that updates the display to reflect the changes made by the user.
m_upperBoundMinSupport - Variable in class weka.associations.Apriori
The upper bound on the support
m_useBlocking - Variable in class weka.deduping.BasicDeduper
Use blocking ?
m_useCustomColour - Variable in class weka.gui.visualize.PlotData2D
Custom colour for this plot
m_useDITCSmoothing - Variable in class weka.core.metrics.KL
Even with unsmoothed data, DITC-type smoothing can be used
m_useFalseImplicitNegatives - Variable in class weka.deduping.PairwiseSelector
 
m_useGenerativeModel - Variable in class weka.deduping.metrics.AffineProbMetric
true if we are using a generative model for distance in the "testing" phase after learning the parameters By default we want to use the additive model that uses probabilities converted to costs
m_useIDF - Variable in class weka.deduping.blocking.Blocking
Should IDF weighting be used?
m_useIDF - Variable in class weka.deduping.metrics.KernelVSMetric
Should IDF weighting be used?
m_useIDF - Variable in class weka.deduping.metrics.VectorSpaceMetric
Should IDF weighting be used?
m_useIDivergence - Variable in class weka.core.metrics.KL
We can switch between regular KL divergence and I-divergence
m_useMultipleMetrics - Variable in class weka.clusterers.MPCKMeans
Individual metrics for each cluster can be used
m_useMultipleMetrics - Variable in class weka.clusterers.assigners.LPAssigner
 
m_useMustLinkPairsOnly - Variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
We can choose to use only must-link training pairs
m_useRejectedPositives - Variable in class weka.deduping.PairwiseSelector
 
m_useUnpruned - Variable in class weka.classifiers.trees.m5.M5Base
Do not prune tree/rules
m_usedChars - Variable in class weka.deduping.metrics.AffineProbMetric
TODO: given a corpus, populate this array with the characters that are actually encountered
m_userHasBeenAskedAboutConversion - Variable in class weka.gui.experiment.SimpleSetupPanel
Whether or not the user has consented for the experiment to be simplified
m_validationChunkSize - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The size of the validation set
m_validationFs - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
m_validationSet - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The instances used for validation
m_validationSetChanged - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
Whether the validation set has recently been changed
m_vector - Variable in class weka.deduping.metrics.StringReference
The corresponding HashMapVector
m_verbose - Variable in class weka.associations.Apriori
Report progress iteratively
m_verbose - Variable in class weka.classifiers.bayes.SemiSupEM
Verbose?
m_verbose - Variable in class weka.clusterers.HAC
verbose?
m_verbose - Variable in class weka.clusterers.MPCKMeans
verbose?
m_verbose - Variable in class weka.clusterers.PCKMeans
verbose?
m_verbose - Variable in class weka.clusterers.PCSoftKMeans
verbose?
m_verbose - Variable in class weka.deduping.metrics.AffineProbMetric
 
m_verbose - Variable in class weka.extraction.ClusteringExtractor
Verbose?
m_verbosityLevel - Variable in class weka.classifiers.sparse.SVMlight
verbosity level
m_visXIndex - Variable in class weka.gui.explorer.ClassifierPanel
default x index for visualizing
m_visXIndex - Variable in class weka.gui.explorer.ClustererPanel
default x index for visualizing
m_visYIndex - Variable in class weka.gui.explorer.ClassifierPanel
default y index for visualizing
m_visYIndex - Variable in class weka.gui.explorer.ClustererPanel
default y index for visualizing
m_visual - Variable in class weka.gui.beans.AbstractDataSource
Default visual for data sources
m_visual - Variable in class weka.gui.beans.AbstractEvaluator
Default visual for evaluators
m_visual - Variable in class weka.gui.beans.AbstractTestSetProducer
 
m_visual - Variable in class weka.gui.beans.AbstractTrainAndTestSetProducer
 
m_visual - Variable in class weka.gui.beans.AbstractTrainingSetProducer
 
m_visual - Variable in class weka.gui.beans.ClassAssigner
 
m_visual - Variable in class weka.gui.beans.Classifier
 
m_visual - Variable in class weka.gui.beans.DataVisualizer
 
m_visual - Variable in class weka.gui.beans.Filter
 
m_visual - Variable in class weka.gui.beans.GraphViewer
 
m_visual - Variable in class weka.gui.beans.StripChart
 
m_visual - Variable in class weka.gui.beans.TextViewer
 
m_visualLabel - Variable in class weka.gui.beans.BeanVisual
 
m_visualName - Variable in class weka.gui.beans.BeanVisual
Name for the bean
m_weightByConfidence - Variable in class weka.classifiers.misc.VFI
Exponentially bias more confident intervals
m_weights - Variable in class weka.core.metrics.WeightedMahalanobis
The full matrix of attribute weights
m_weightsFilename - Variable in class weka.core.metrics.MatlabMetricLearner
Name of the temporary file where the weights will be stored by Matlab after calculation
m_weightsSquare - Variable in class weka.core.metrics.WeightedMahalanobis
weights^0.5, used to project instances to the new space to speed up calculations
m_weightsUpdated - Variable in class weka.classifiers.functions.neural.NeuralConnection
True if the weights have already been updated.
m_width - Variable in class weka.classifiers.sparse.SVMlight
Epsilon width of tube for regression
m_x - Variable in class weka.classifiers.functions.neural.NeuralConnection
The x coord of this unit purely for displaying purposes.
m_xChange - Variable in class weka.gui.visualize.AttributePanelEvent
True if the x selection changed
m_xIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_xIndex - Variable in class weka.gui.visualize.Plot2D
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
m_xIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
Indexes of the attributes to go on the x and y axis and the attribute to use for colouring and the current shape for drawing
m_y - Variable in class weka.classifiers.functions.neural.NeuralConnection
The y coord of this unit purely for displaying purposes.
m_yChange - Variable in class weka.gui.visualize.AttributePanelEvent
True if the y selection changed
m_yIndex - Variable in class weka.gui.visualize.AttributePanel
 
m_yIndex - Variable in class weka.gui.visualize.Plot2D
 
m_yIndex - Variable in class weka.gui.visualize.VisualizePanel.PlotPanel
 
m_zeroR - Variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The default scheme used when committees aren't ready
m_zipDest - Variable in class weka.experiment.DedupingPRCurveCVResultProducer
The output zipper to use for saving raw splitEvaluator output
m_zipDest - Variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
The output zipper to use for saving raw splitEvaluator output
m_zipDest - Variable in class weka.experiment.ExtractionResultProducer
The output zipper to use for saving raw splitEvaluator output
main(String[]) - Static method in class GetAllSubPackages
 
main(String[]) - Static method in class weka.associations.Apriori
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.CfsSubsetEval
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.ClassifierSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.ConsistencySubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.GainRatioAttributeEval
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.MatlabICA
Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.MatlabNMF
Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.MatlabPCA
Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.OneRAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.PrincipalComponents
Main method for testing this class
main(String[]) - Static method in class weka.attributeSelection.ReliefFAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SVMAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Main method for testing this class.
main(String[]) - Static method in class weka.attributeSelection.WrapperSubsetEval
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.BVDecompose
Test method for this class
main(String[]) - Static method in class weka.classifiers.CheckClassifier
Test method for this class
main(String[]) - Static method in class weka.classifiers.EnsembleEvaluation
A 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.RegressionBVDecompose
Test method for 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.BayesNetB2
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.DiscreteEstimatorBayes
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.NaiveBayesSimple
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.NaiveBayesUpdateable
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.bayes.SemiSupEM
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.evaluation.CostCurve
Tests the CostCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.MarginCurve
Tests the MarginCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.evaluation.ThresholdCurve
Tests the ThresholdCurve generation from the command line.
main(String[]) - Static method in class weka.classifiers.functions.LeastMedSq
generate a Linear regression predictor for testing
main(String[]) - Static method in class weka.classifiers.functions.LinearRegression
Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.functions.Logistic
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.SMO
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.UnivariateLinearRegression
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.Winnow
Main method.
main(String[]) - Static method in class weka.classifiers.functions.neural.NeuralNetwork
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.functions.pace.ChisqMixture
Method to test this class
main(String[]) - Static method in class weka.classifiers.functions.pace.DiscreteFunction
 
main(String[]) - Static method in class weka.classifiers.functions.pace.DoubleVector
 
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 weka.classifiers.functions.pace.PaceMatrix
 
main(String[]) - Static method in class weka.classifiers.functions.pace.PaceRegression
Generates a linear regression function predictor.
main(String[]) - Static method in class weka.classifiers.lazy.IB1
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.IBk
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LBR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.LWR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.lazy.kstar.KStar
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.ActiveDecorate
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdaBoostM1
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AdditiveRegression
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.AttributeSelectedClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Bagging
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.CVParameterSelection
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.CostSensitiveClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Crate
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.DEC
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.DistributionMetaClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.Fable
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.LogitBoost
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MetaCost
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiBoostAB
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiClassClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.MultiScheme
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.OrdinalClassClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.QBag
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.QBoost
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.RegressionByDiscretization
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.meta.SemiSupDecorate
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.TestEnsembleClassifier
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.misc.HyperPipes
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.Prototype
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.misc.PrototypeMetric
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.rules.ConjunctiveRule
Main method.
main(String[]) - Static method in class weka.classifiers.rules.DecisionTable
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.JRip
Main method.
main(String[]) - Static method in class weka.classifiers.rules.M5Rules
Main method by which this class can be tested
main(String[]) - Static method in class weka.classifiers.rules.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.Ridor
Main method.
main(String[]) - Static method in class weka.classifiers.rules.ZeroR
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.rules.part.PART
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.sparse.IBkMetric
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.sparse.SVMlight
Main method for testing 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.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.RandomForest
Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.RandomTree
Main method for this class.
main(String[]) - Static method in class weka.classifiers.trees.UserClassifier
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.adtree.ADTree
Main method for testing this class.
main(String[]) - Static method in class weka.classifiers.trees.j48.J48
Main method for testing this class
main(String[]) - Static method in class weka.classifiers.trees.m5.M5P
Main method by which this class can be tested
main(String[]) - Static method in class weka.clusterers.AlgVector
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.ClusterEvaluation
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.Cobweb
 
main(String[]) - Static method in class weka.clusterers.DistributionMetaClusterer
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.FarthestFirst
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.HAC
 
main(String[]) - Static method in class weka.clusterers.MPCKMeans
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.PCKMeans
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.PCSoftKMeans
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.SeededKMeans
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.SimpleKMeans
Main method for testing this class.
main(String[]) - Static method in class weka.clusterers.XMeans
Main method for testing this class.
main(String[]) - Static method in class weka.core.AlgVector
Main method for testing this class.
main(String[]) - Static method in class weka.core.Attribute
Simple main method for testing this class.
main(String[]) - Static method in class weka.core.BinarySparseInstance
Main method for testing this class.
main(String[]) - Static method in class weka.core.CheckOptionHandler
Main method for using the CheckOptionHandler.
main(String[]) - Static method in class weka.core.ContingencyTables
Main method for testing this class.
main(String[]) - Static method in class weka.core.EuclideanDistance
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.Instances
Main method for this class -- just prints a summary of a set of instances.
main(String[]) - Static method in class weka.core.KDTree
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.Queue
Main method for testing this class.
main(String[]) - Static method in class weka.core.RandomVariates
Main method for testing this class.
main(String[]) - Static method in class weka.core.Range
Main method for testing this class.
main(String[]) - Static method in class weka.core.SparseInstance
Main method for testing this class.
main(String[]) - Static method in class weka.core.SpecialFunctions
Main method for testing this class.
main(String[]) - Static method in class weka.core.Statistics
Main method for testing this class.
main(String[]) - Static method in class weka.core.Utils
Main method for testing this class.
main(String[]) - Static method in class weka.core.converters.ArffLoader
Main method.
main(String[]) - Static method in class weka.core.converters.C45Loader
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.SerializedInstancesLoader
Main method.
main(String[]) - Static method in class weka.core.metrics.BarHillelMetric
Main method for testing this class
main(String[]) - Static method in class weka.core.metrics.BarHillelMetricMatlab
Main method for testing this class
main(String[]) - Static method in class weka.core.metrics.KL
 
main(String[]) - Static method in class weka.core.metrics.WeightedDotP
 
main(String[]) - Static method in class weka.core.metrics.WeightedEuclidean
 
main(String[]) - Static method in class weka.core.metrics.WeightedMahalanobis
 
main(String[]) - Static method in class weka.core.metrics.XingMetric
Main method for testing this class
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.datagenerators.TextSource
 
main(String[]) - Static method in class weka.deduping.metrics.AffineProbMetric
 
main(String[]) - Static method in class weka.deduping.metrics.Porter
For testing, print the stemmed version of a word
main(String[]) - Static method in class weka.estimators.DDConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DKConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DNConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.DiscreteEstimator
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.KKConditionalEstimator
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.MahalanobisEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NDConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NNConditionalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.NormalEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.estimators.PoissonEstimator
Main method for testing this class.
main(String[]) - Static method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
main(String[]) - Static method in class weka.experiment.ActiveLearningCurveCVResultProducer
 
main(String[]) - Static method in class weka.experiment.CrossValidationResultProducer
 
main(String[]) - Static method in class weka.experiment.DedupingPRCurveCVResultProducer
 
main(String[]) - Static method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
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.ExtractionResultProducer
 
main(String[]) - Static method in class weka.experiment.Grapher
Create gnuplot graphs of learning curves.
main(String[]) - Static method in class weka.experiment.InstanceQuery
Test the class from the command line.
main(String[]) - Static method in class weka.experiment.LearningCurveCrossValidationResultProducer
 
main(String[]) - Static method in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
main(String[]) - Static method in class weka.experiment.NoiseGrapher
Create gnuplot graphs of learning curves.
main(String[]) - Static method in class weka.experiment.OutputZipper
Main method for testing this class
main(String[]) - Static method in class weka.experiment.PairedStats
Tests the paired stats object from the command line.
main(String[]) - Static method in class weka.experiment.PairedTTester
Test the class from the command line.
main(String[]) - Static method in class weka.experiment.RemoteEngine
Main method.
main(String[]) - Static method in class weka.experiment.RemoteExperiment
Configures/Runs the Experiment from the command line.
main(String[]) - Static method in class weka.experiment.SemiSupCrossValidationResultProducer
 
main(String[]) - Static method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
main(String[]) - Static method in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
main(String[]) - Static method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
main(String[]) - Static method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
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.filters.AllFilter
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.NullFilter
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.ClassOrder
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.Discretize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.attribute.NominalToBinary
Main method for testing this class.
main(String[]) - Static method in class weka.filters.supervised.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.Add
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddCluster
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddExpression
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.AddNoise
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Copy
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Discretize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.FirstOrder
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.MakeIndicator
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.Normalize
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.NumericTransform
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Obfuscate
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.PKIDiscretize
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.RemoveType
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.RemoveUseless
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.Standardize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToNominal
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.StringToWordVector
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.SwapValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.attribute.UnitVector
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.Randomize
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveFolds
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveMisclassified
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemovePercentage
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveRange
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.RemoveWithValues
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.Resample
Main method for testing this class.
main(String[]) - Static method in class weka.filters.unsupervised.instance.SparseToNonSparse
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.AttributeSelectionPanel
Tests the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeSummaryPanel
Tests out the attribute summary panel from the command line.
main(String[]) - Static method in class weka.gui.AttributeVisualizationPanel
Main method to test this class from command line
main(String[]) - Static method in class weka.gui.GUIChooser
Tests out the GUIChooser environment.
main(String[]) - Static method in class weka.gui.GenericArrayEditor
Tests out the array editor from the command line.
main(String[]) - Static method in class weka.gui.GenericObjectEditor
Tests out the Object editor from the command line.
main(String[]) - Static method in class weka.gui.HierarchyPropertyParser
Tests out the parser.
main(String[]) - Static method in class weka.gui.InstancesSummaryPanel
Tests out the instance summary panel from the command line.
main(String[]) - Static method in class weka.gui.ListSelectorDialog
Tests out the list selector from the command line.
main(String[]) - Static method in class weka.gui.LogPanel
Tests out the log panel from the command line.
main(String[]) - Static method in class weka.gui.PropertySelectorDialog
Tests out the property selector from the command line.
main(String[]) - Static method in class weka.gui.ResultHistoryPanel
Tests out the result history from the command line.
main(String[]) - Static method in class weka.gui.SaveBuffer
Main method for testing this class
main(String[]) - Static method in class weka.gui.SelectedTagEditor
Tests out the selectedtag editor from the command line.
main(String[]) - Static method in class weka.gui.SimpleCLI
Method to start up the simple cli
main(String[]) - Static method in class weka.gui.WekaTaskMonitor
Main method for testing this class
main(String[]) - Static method in class weka.gui.beans.KnowledgeFlow
Main method.
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.boundaryvisualizer.BoundaryPanel
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.EMDataGenerator
Main method for tesing this class
main(String[]) - Static method in class weka.gui.boundaryvisualizer.KDDataGenerator
Main method for tesing this class
main(String[]) - Static method in class weka.gui.experiment.AlgorithmListPanel
Tests out the algorithm list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DatasetListPanel
Tests out the dataset list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.DistributeExperimentPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.Experimenter
Tests out the experiment environment.
main(String[]) - Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.HostListPanel
Tests out the host list panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.ResultsPanel
Tests out the results panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunNumberPanel
Tests out the panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.RunPanel
Tests out the run panel from the command line.
main(String[]) - Static method in class weka.gui.experiment.SetupPanel
Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.experiment.SimpleSetupPanel
Tests out the experiment setup from the command line.
main(String[]) - Static method in class weka.gui.explorer.AssociationsPanel
Tests out the Associator panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.AttributeSelectionPanel
Tests out the attribute selection panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClassifierPanel
Tests out the classifier panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.ClustererPanel
Tests out the clusterer panel from the command line.
main(String[]) - Static method in class weka.gui.explorer.Explorer
Tests out the explorer environment.
main(String[]) - Static method in class weka.gui.explorer.PreprocessPanel
Tests out the instance-preprocessing 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.visualize.AttributePanel
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.LegendPanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.MatrixPanel
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.Plot2D
Main method for testing this class
main(String[]) - Static method in class weka.gui.visualize.VisualizePanel
Main method for testing this class
makeBinaryTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
makeBinaryTipText() - Method in class weka.filters.unsupervised.attribute.Discretize
Returns the tip text for this property
makeCopies(Associator, int) - Static method in class weka.associations.Associator
Creates copies of the current associator.
makeCopies(ASEvaluation, int) - Static method in class weka.attributeSelection.ASEvaluation
Creates copies of the current evaluator.
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.
makeDataFormat() - Method in class weka.datagenerators.TextSource.Table
 
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.
makeDistribution(IBkMetric.NeighborList) - Method in class weka.classifiers.sparse.IBkMetric
Turn the list of nearest neighbors into a probability distribution
makeDistribution(int) - Method in class weka.clusterers.SemiSupClustererEvaluation
Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0;
makeInstance(TextSource.Table) - Method in class weka.datagenerators.TextSource.DataRow
 
makeNewTrainSubset(Instances, Instances, int) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Use current classifier to actively select specified number of instances to be transfered from the active pool to the training subset
makeTestDataset(int, int, int, int, int, boolean) - Method in class weka.classifiers.CheckClassifier
Make a simple set of instances, which can later be modified for use in specific tests.
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.
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.
matchInstance(Instance, Instance) - Method in class weka.clusterers.HAC
Internal method: check if two instances match on their attribute values
matchesTemplate(Object[], Object[]) - Method in class weka.experiment.AveragingResultProducer
Compares a key to a template to see whether they match.
matrix() - Method in class weka.classifiers.trees.j48.Distribution
Returns matrix with distribution of class values.
max() - Method in class weka.classifiers.functions.pace.DoubleVector
Returns the maximum value of all elements
max - Variable in class weka.experiment.Stats
The maximum value seen, or Double.NaN if no values seen
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.
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.
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
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.classifiers.bayes.SemiSupEM
Returns the tip text for this property
maxIterationsTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
maxIterationsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
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
 
maxNumClustersTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property
maxNumSupportPoints - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
maxTrainSize() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Get the maximum size of the training set based on upperSize limit or maximum training set size from the n-fold CV
maxTrainSize() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Get the maximum size of the training set based on upperSize limit or maximum training set size from the n-fold CV
maxTrainSize() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Get the maximum size of the training set based on upperSize limit or maximum training set size from the n-fold CV
maxTrainSize() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Get the maximum size of the training set based on maximum training set size from the n-fold CV
maxTrainSize() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Get the maximum size of the training set based on upperSize limit or maximum training set size from the n-fold CV
maxTrainSize() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
maxTrainSize() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
maxTrainSize() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Get the maximum size of the training set based on pairwise points, using upperSize limit or maximum pairs in training set size from the n-fold CV
maxTrainSize() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
maxWeight() - Method in class weka.deduping.metrics.HashMapVector
Returns the maximum weight of any token in the vector.
maximizationStep() - Method in class weka.deduping.metrics.AffineProbMetric
Maximization step of the EM algorithm
maximumVariancePercentageAllowedTipText() - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Returns the tip text for this property
mean(double[]) - Static method in class weka.core.Utils
Computes the mean for an array of doubles.
mean - Variable in class weka.experiment.Stats
The mean of values at the last calculateDerived() call
meanAbsoluteError() - Method in class weka.classifiers.EnsembleEvaluation
Returns the mean absolute error.
meanAbsoluteError() - Method in class weka.classifiers.Evaluation
Returns the mean absolute error.
meanInstance(Instances) - Method in class weka.classifiers.misc.PrototypeMetric
Compute a mean instance for all the instances in a set
meanOrMode(Instances) - Method in class weka.clusterers.MPCKMeans
Fast version of meanOrMode - streamlined from Instances.meanOrMode for efficiency Does not check for missing attributes, assumes numeric attributes, assumes Sparse instances
meanOrMode(Instances) - Method in class weka.clusterers.PCKMeans
Fast version of meanOrMode - streamlined from Instances.meanOrMode for efficiency Does not check for missing attributes, assumes numeric attributes, assumes Sparse instances
meanOrMode(Instances) - Method in class weka.clusterers.PCSoftKMeans
Fast version of meanOrMode - streamlined from Instances.meanOrMode for efficiency Does not check for missing attributes, assumes numeric attributes, assumes Sparse instances
meanOrMode(Instances) - Method in class weka.clusterers.SeededKMeans
Fast version of meanOrMode - streamlined from Instances.meanOrMode for efficiency Does not check for missing attributes, assumes numeric attributes, assumes Sparse instances
meanOrMode(int) - Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(Attribute) - Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(Instances) - Method in class weka.core.metrics.LearnableMetric
Fast version of meanOrMode - streamlined from Instances.meanOrMode for efficiency Does not check for missing attributes, assumes numeric attributes, assumes Sparse instances
meanPriorAbsoluteError() - Method in class weka.classifiers.EnsembleEvaluation
Returns the mean absolute error of the prior.
meanPriorAbsoluteError() - Method in class weka.classifiers.Evaluation
Returns the mean absolute error of the prior.
meanVectorFull(Instances) - Method in class weka.classifiers.misc.PrototypeMetric
Compute mean vector for non-sparse instances using meanOrMode method on Instances
meanVectorSparse(Instances) - Method in class weka.classifiers.misc.PrototypeMetric
Efficiently compute a mean vector for a set of sparse instances
measureExamplesProcessed() - Method in class weka.classifiers.trees.adtree.ADTree
Returns the number of examples "counted".
measureNodesExpanded() - Method in class weka.classifiers.trees.adtree.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.adtree.ADTree
Calls measure function for leaf size - the number of prediction nodes.
measureNumLeaves() - Method in class weka.classifiers.trees.j48.J48
Returns the number of leaves
measureNumPredictionLeaves() - Method in class weka.classifiers.trees.adtree.ADTree
Calls measure function for prediction leaf size - the number of prediction nodes without children.
measureNumRules() - Method in class weka.classifiers.rules.DecisionTable
Returns the number of rules
measureNumRules() - Method in class weka.classifiers.rules.part.PART
Return the number of rules.
measureNumRules() - Method in class weka.classifiers.trees.j48.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
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
measureTrainEnsembleDiversity() - Method in class weka.classifiers.EnsembleClassifier
 
measureTrainEnsembleError() - Method in class weka.classifiers.EnsembleClassifier
 
measureTrainError() - Method in class weka.classifiers.EnsembleClassifier
 
measureTreeSize() - Method in class weka.classifiers.trees.adtree.ADTree
Calls measure function for tree size - the total number of nodes.
measureTreeSize() - Method in class weka.classifiers.trees.j48.J48
Returns the size of the tree
merge(ADTree) - Method in class weka.classifiers.trees.adtree.ADTree
Merges two trees together.
merge(PredictionNode, ADTree) - Method in class weka.classifiers.trees.adtree.PredictionNode
Merges this node with another.
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.
mergeClusters(int, int) - Method in class weka.clusterers.HAC
Internal method to merge two clusters and update distances
mergeClusters(int, int) - Method in class weka.deduping.BasicDeduper
Internal method to merge two clusters and update distances
mergeInstance(Instance) - Method in class weka.core.BinarySparseInstance
Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.Instance
Merges this instance with the given instance and returns the result.
mergeInstance(Instance) - Method in class weka.core.SparseInstance
Merges this instance with the given instance and returns the result.
mergeInstances(Instances, Instances) - Static method in class weka.core.Instances
Merges two sets of Instances together.
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeInstances(Instance, Instance) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Creates a new instance the same as one instance (the "destination") but with some attribute values copied from another instance (the "source")
mergeStep() - Method in class weka.clusterers.HAC
Internal method that finds two most similar clusters and merges them
mergeStep() - Method in class weka.deduping.BasicDeduper
Internal method that finds two most similar clusters and merges them
metaFormat(Instances) - Method in class weka.classifiers.meta.Stacking
Makes the format for the level-1 data.
metaInstance(Instance) - Method in class weka.classifiers.meta.Stacking
Makes a level-1 instance from the given instance.
methodTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
 
metric - Variable in class weka.experiment.Grapher
The name of the performance metric to plot
metric - Variable in class weka.experiment.NoiseGrapher
The name of the performance metric to plot
metricName() - Method in class weka.classifiers.sparse.IBkMetric
Get the name of the distance metric that is used Avoid the 'get' prefix so that this doesn't show in the dialogs
metricName() - Method in class weka.clusterers.HAC
Get the distance metric name
metricName() - Method in class weka.clusterers.SeededKMeans
Get the distance metric name
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].
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
minIndex(int[]) - Static method in class weka.core.Utils
Returns index of minimum element in a given array of integers.
minIndex(double[]) - Static method in class weka.core.Utils
Returns index of minimum element in a given array of doubles.
minMetricTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
minNumClustersTipText() - Method in class weka.clusterers.XMeans
Returns the tip text for this property
minProb - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the smallest transformation probability
minStdDevTipText() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Returns the tip text for this property
minStdDevTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
minimizeExpectedCostTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
minimumDistance(Instance, Instances) - Method in class weka.classifiers.bayes.SemiSupEM
Return the distance from inst to the closest instance in insts
minsAndMaxs(Instances, double[][], int) - Method in class weka.classifiers.trees.j48.C45Split
Returns the minsAndMaxs of the index.th subset.
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
missingCount - Variable in class weka.core.AttributeStats
The number of missing values
missingMergeTipText() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.GainRatioAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the tip text for this property
missingMergeTipText() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns the tip text for this property
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".
mixingDistribution - Variable in class weka.classifiers.functions.pace.MixtureDistribution
 
momentumTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
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.
multiResultsetFull(int, int) - Method in class weka.experiment.PairedTTester
Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetPercentErrorReduction(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.
multiResultsetTopNPercentErrorReduction(int, int, double) - Method in class weka.experiment.PairedTTester
Creates a comparison table where a base resultset is compared to the other resultsets.
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.
multiply(Matrix) - Method in class weka.core.Matrix
Returns the multiplication of two matrices
multiply(double) - Method in class weka.deduping.metrics.HashMapVector
Destructively multiply the vector by a constant
mutationProbTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
mutualInformation() - Method in class weka.clusterers.SemiSupClustererEvaluation
 

N

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
NEG_MODE_IMPLICIT_NEGATIVES - Static variable in class weka.deduping.PairwiseSelector
 
NEG_MODE_RANDOM_NEGATIVES - Static variable in class weka.deduping.PairwiseSelector
 
NEG_MODE_RANDOM_RECORDS - Static variable in class weka.deduping.PairwiseSelector
 
NEW_BATCH - Static variable in class weka.gui.beans.IncrementalClassifierEvent
 
NGramTokenizer - class weka.deduping.metrics.NGramTokenizer.
This class defines a tokenizer that turns strings into HashMapVectors of n-grams
NGramTokenizer() - Constructor for class weka.deduping.metrics.NGramTokenizer
A default constructor
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
 
NNMMethod - Static variable in class weka.classifiers.functions.pace.MixtureDistribution
The nonnegative-measure-based method
NOISE_FIELD_NAME - Static variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
NOMINAL - Static variable in class weka.core.Attribute
Constant set for nominal attributes.
NONE - Static variable in class weka.core.converters.AbstractLoader
The retrieval modes
NONE - Static variable in class weka.experiment.Grapher
errorBar value for no error bars
NONE - Static variable in class weka.experiment.NoiseGrapher
errorBar value for no error bars
NONE - Static variable in class weka.gui.beans.KnowledgeFlow
 
NONE - Static variable in class weka.gui.visualize.VisualizePanelEvent
No longer used
NORM_CONST - Static variable in class weka.classifiers.bayes.NaiveBayesSimple
Constant for normal distribution.
NORM_EXPECTED_COST_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
 
NORTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
 
NOT_RUNNING - Static variable in class weka.gui.experiment.RunPanel
The message displayed when no experiment is running
NO_COMMAND - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
 
NO_SOURCE - Static variable in class weka.gui.AttributeSummaryPanel
Message shown when no instances have been loaded and no attribute set
NO_SOURCE - Static variable in class weka.gui.InstancesSummaryPanel
Message shown when no instances have been loaded
NO_SOURCE - Static variable in class weka.gui.experiment.ResultsPanel
Message shown when no experimental results have been loaded
NUMERIC - Static variable in class weka.core.Attribute
Constant set for numeric attributes.
NUM_RAND_COLS - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
NaiveBayes - class weka.classifiers.bayes.NaiveBayes.
Class for a Naive Bayes classifier using estimator classes.
NaiveBayes() - Constructor for class weka.classifiers.bayes.NaiveBayes
 
NaiveBayesSimple - class weka.classifiers.bayes.NaiveBayesSimple.
Class for building and using a simple Naive Bayes classifier.
NaiveBayesSimple() - Constructor for class weka.classifiers.bayes.NaiveBayesSimple
 
NaiveBayesSimpleSoft - class weka.classifiers.bayes.NaiveBayesSimpleSoft.
Version of NaiveBayesSimple that supports training on SoftClassifiedInstances and WeightedInstances for use with SemiSupEM
NaiveBayesSimpleSoft() - Constructor for class weka.classifiers.bayes.NaiveBayesSimpleSoft
 
NaiveBayesSimpleSparse - class weka.classifiers.sparse.NaiveBayesSimpleSparse.
Class for building and using a simple Naive Bayes classifier that is adapted for Sparse Instances assuming attribute values are counts of the presence of a descriptive token (e.g.
NaiveBayesSimpleSparse() - Constructor for class weka.classifiers.sparse.NaiveBayesSimpleSparse
 
NaiveBayesSimpleSparseSoft - class weka.classifiers.sparse.NaiveBayesSimpleSparseSoft.
Version of NaiveBayesSimpleSparse that supports training on SoftClassifiedInstances and WeightedInstances for use with SemiSupEM
NaiveBayesSimpleSparseSoft() - Constructor for class weka.classifiers.sparse.NaiveBayesSimpleSparseSoft
 
NaiveBayesUpdateable - class weka.classifiers.bayes.NaiveBayesUpdateable.
Class for a Naive Bayes classifier using estimator classes.
NaiveBayesUpdateable() - Constructor for class weka.classifiers.bayes.NaiveBayesUpdateable
 
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
 
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.
NeuralNetwork - class weka.classifiers.functions.neural.NeuralNetwork.
A Classifier that uses backpropagation to classify instances.
NeuralNetwork() - Constructor for class weka.classifiers.functions.neural.NeuralNetwork
The constructor.
NeuralNetwork.NeuralEnd - class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd.
This inner class is used to connect the nodes in the network up to the data that they are classifying, Note that objects of this class are only suitable to go on the attribute side or class side of the network and not both.
NeuralNetwork.NeuralEnd(String) - Constructor for class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd
 
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
 
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.
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
NoiseCurveCrossValidationResultProducer - class weka.experiment.NoiseCurveCrossValidationResultProducer.
Does a N-fold cross-validation, but generates a Noise Curve by also varying the number amount of Noise.
NoiseCurveCrossValidationResultProducer() - Constructor for class weka.experiment.NoiseCurveCrossValidationResultProducer
 
NoiseGrapher - class weka.experiment.NoiseGrapher.
Class for producing performance graphs for any metric from learning curve results.
NoiseGrapher(String, short) - Constructor for class weka.experiment.NoiseGrapher
Create an initial Grapher and load in data, names of datasets, and set of points on learning curve.
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() - Constructor for class weka.filters.supervised.attribute.NominalToBinary
 
NominalToBinary - class weka.filters.unsupervised.attribute.NominalToBinary.
Converts all nominal attributes into binary numeric attributes.
NominalToBinary() - Constructor for class weka.filters.unsupervised.attribute.NominalToBinary
 
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
 
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.
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
Normalize - class weka.filters.unsupervised.attribute.Normalize.
Normalizes all numeric values in the given dataset.
Normalize() - Constructor for class weka.filters.unsupervised.attribute.Normalize
 
NullFilter - class weka.filters.NullFilter.
A simple instance filter that allows no instances to pass through.
NullFilter() - Constructor for class weka.filters.NullFilter
 
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.
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().
n - Variable in class weka.classifiers.functions.pace.Matrix
Row and column dimensions.
name() - Method in class weka.core.Attribute
Returns the attribute's name.
name() - Method in class weka.core.Option
Returns the option's name.
nameTipText() - Method in class weka.filters.unsupervised.attribute.AddExpression
Returns the tip text for this property
needExponentialFormat(double) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
nestedEstimate(DoubleVector) - Method in class weka.classifiers.functions.pace.NormalMixture
Returns the optimal nested model estimate of a vector.
newColorAttribute(int, Instances) - Method in class weka.gui.visualize.VisualizePanel
Sets the Colors in use for a different attrib if it is not a nominal attrib and or does not have more possible values then this will do nothing.
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(int) - Method in interface weka.classifiers.IterativeClassifier
Performs one iteration.
next - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
next table entry (separate chaining)
next(int) - Method in class weka.classifiers.trees.adtree.ADTree
Performs one iteration.
next(Queue.QueueNode) - Method in class weka.core.Queue.QueueNode
Sets the next node in the queue, and returns it.
next() - Method in class weka.core.Queue.QueueNode
Gets the next node in the queue.
next(int) - Method in class weka.core.RandomVariates
Simply use the method of the super class
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.
nextID() - Static method in class weka.classifiers.trees.j48.ClassifierTree
Gets the next unique node ID.
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.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.
nf - Variable in class weka.classifiers.functions.pace.ExponentialFormat
 
nf - Variable in class weka.classifiers.functions.pace.FloatingPointFormat
 
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.
nodeToString() - Method in class weka.classifiers.trees.m5.RuleNode
Returns a description of this node (debugging purposes)
nominalCounts - Variable in class weka.core.AttributeStats
Counts of each nominal value
nominalLabelsTipText() - Method in class weka.filters.unsupervised.attribute.Add
Returns the tip text for this property
nominalToBinaryFilterTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
nonActivePairwiseInit() - Method in class weka.clusterers.MPCKMeans
Initialization routine for non-active algorithm
nonActivePairwiseInit() - Method in class weka.clusterers.PCKMeans
Initialization routine for non-active algorithm
nonActivePairwiseInit() - Method in class weka.clusterers.PCSoftKMeans
Initialization routine for non-active algorithm
norm(double, int) - Method in class weka.classifiers.bayes.SemiSupEM
Normalizes a given value of a numeric attribute.
norm() - Method in class weka.clusterers.AlgVector
Returns the norm of the vector
norm(double, int) - Method in class weka.clusterers.FarthestFirst
Normalizes a given value of a numeric attribute.
norm() - Method in class weka.core.AlgVector
Returns the norm of the vector
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
normF() - Method in class weka.classifiers.functions.pace.Matrix
Frobenius norm
normInf() - Method in class weka.classifiers.functions.pace.Matrix
Infinity norm
normVector() - Method in class weka.clusterers.AlgVector
Norms this vector to length 1.0
normVector() - Method in class weka.core.AlgVector
Norms this vector to length 1.0
normalDens(double, double, double) - Method in class weka.classifiers.bayes.NaiveBayesSimple
Density function of normal distribution returning log of probability
normalDistribution - Static variable in class weka.classifiers.functions.pace.Maths
Distribution type: noraml
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).
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).
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(Instance) - Method in class weka.clusterers.MPCKMeans
Normalizes Instance or SparseInstance
normalize(double[]) - Static method in class weka.clusterers.MPCKMeans
Normalize an array of double's
normalize(Instance) - Method in class weka.clusterers.PCKMeans
Normalizes Instance or SparseInstance
normalize(Instance) - Method in class weka.clusterers.PCSoftKMeans
Normalizes Instance or SparseInstance
normalize(Instance) - Method in class weka.clusterers.SeededKMeans
Normalizes Instance or SparseInstance
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.neural.NeuralNetwork
 
normalizeAttributesTipText() - Method in class weka.classifiers.misc.Prototype
 
normalizeByWeight(Instance) - Method in class weka.clusterers.MPCKMeans
This function divides every attribute value in an instance by the instance weight -- useful to find the mean of a cluster in Euclidean space
normalizeByWeight(Instance) - Method in class weka.clusterers.PCKMeans
This function divides every attribute value in an instance by the instance weight -- useful to find the mean of a cluster in Euclidean space
normalizeByWeight(Instance) - Method in class weka.clusterers.PCSoftKMeans
This function divides every attribute value in an instance by the instance weight -- useful to find the mean of a cluster in Euclidean space
normalizeByWeight(Instance) - Method in class weka.clusterers.SeededKMeans
This function divides every attribute value in an instance by the instance weight -- useful to find the mean of a cluster in Euclidean space
normalizeEmissionProbs() - Method in class weka.deduping.metrics.AffineProbMetric
Normalize the probabilities of emission editops so that they sum to 1 for each state
normalizeInstance(Instance) - Method in class weka.clusterers.MPCKMeans
Normalizes the values of a normal Instance in L2 norm
normalizeInstance(Instance) - Method in class weka.clusterers.PCKMeans
Normalizes the values of a normal Instance in L2 norm
normalizeInstance(Instance) - Method in class weka.clusterers.PCSoftKMeans
Normalizes the values of a normal Instance in L2 norm
normalizeInstance(Instance) - Method in class weka.clusterers.SeededKMeans
Normalizes the values of a normal Instance
normalizeInstance(Instance) - Method in class weka.core.metrics.Metric
Normalizes the values of a normal Instance
normalizeInstanceWeighted(Instance) - Method in class weka.core.metrics.LearnableMetric
Normalizes the values of an Instance utilizing feature weights
normalizeLogs(double[]) - Static method in class weka.classifiers.bayes.NaiveBayesSimple
Converts an unormalized vector of logs of probabilities into a normalized distribution that sums to one
normalizeNumericClassTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
normalizeSparseInstance(Instance) - Method in class weka.clusterers.MPCKMeans
Normalizes the values of a SparseInstance in L2 norm
normalizeSparseInstance(Instance) - Method in class weka.clusterers.PCKMeans
Normalizes the values of a SparseInstance in L2 norm
normalizeSparseInstance(Instance) - Method in class weka.clusterers.PCSoftKMeans
Normalizes the values of a SparseInstance in L2 norm
normalizeSparseInstance(Instance) - Method in class weka.clusterers.SeededKMeans
Normalizes the values of a SparseInstance
normalizeTipText() - Method in class weka.attributeSelection.MatlabICA
Returns the tip text for this property
normalizeTipText() - Method in class weka.attributeSelection.MatlabNMF
Returns the tip text for this property
normalizeTipText() - Method in class weka.attributeSelection.MatlabPCA
Returns the tip text for this property
normalizeTipText() - Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
normalizeTransitionProbs() - Method in class weka.deduping.metrics.AffineProbMetric
Normalize the probabilities of transitions so that they sum to 1 for each state
normalizeWeights(double[]) - Method in class weka.core.metrics.AttrEvalMetricLearner
Normalize weights
normalizeWeights(double[]) - Method in class weka.core.metrics.GDMetricLearner
Normalize weights
notCoveredInstances() - Method in class weka.classifiers.trees.m5.Rule
Get the instances not covered by this rule
notifyDataListeners(DataSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
notifyDataSetLoaded(DataSetEvent) - Method in class weka.gui.beans.Loader
Notify all Data source listeners that a data set has been loaded
notifyInstanceListeners(InstanceEvent) - Method in class weka.gui.beans.ClassAssigner
 
notifyInstanceListeners(InstanceEvent) - Method in class weka.gui.beans.Filter
 
notifyInstanceLoaded(InstanceEvent) - Method in class weka.gui.beans.Loader
Notify all instance listeners that a new instance is available
notifyInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
 
notifyInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceLoader
 
notifyTestListeners(TestSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
notifyTestSetProduced(TestSetEvent) - Method in class weka.gui.beans.TestSetMaker
Tells all listeners that a test set is available
notifyTestSetProduced(TestSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Notify test set listeners that a test set is available
notifyTrainingListeners(TrainingSetEvent) - Method in class weka.gui.beans.ClassAssigner
 
notifyTrainingSetProduced(TrainingSetEvent) - Method in class weka.gui.beans.CrossValidationFoldMaker
Notify all listeners of a TrainingSet event
notifyTrainingSetProduced(TrainingSetEvent) - Method in class weka.gui.beans.TrainTestSplitMaker
Notify training set listeners that a training set is available
notifyTrainingSetProduced(TrainingSetEvent) - Method in class weka.gui.beans.TrainingSetMaker
Inform training set listeners that a training set is availabel
nrWithLineSearchForAlpha(double[], double[]) - Method in class weka.clusterers.MPCKMeans
Does one NR step, calculates the alpha (using line search) that does not violate positivity constraint of each attribute weight, returns new values of attribute weights
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.
numArguments() - Method in class weka.core.Option
Returns the option's number of arguments.
numAttributes() - Method in class weka.core.Instance
Returns the number of attributes.
numAttributes() - Method in class weka.core.Instances
Returns the number of attributes.
numAttributes() - Method in class weka.core.SparseInstance
Returns the number of attributes.
numBags() - Method in class weka.classifiers.trees.j48.Distribution
Returns number of bags.
numChildren() - Method in class weka.gui.HierarchyPropertyParser
The number of the children nodes.
numClasses() - Method in class weka.classifiers.trees.j48.Distribution
Returns number of classes.
numClasses() - Method in class weka.core.Instance
Returns the number of class labels.
numClasses() - Method in class weka.core.Instances
Returns the number of class labels.
numClustersTipText() - Method in class weka.clusterers.EM
Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.FarthestFirst
Returns the tip text for this property
numClustersTipText() - Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
numColumns() - Method in class weka.core.Matrix
Returns the number of columns in the matrix.
numCommonTokens(String, String) - Static method in class weka.deduping.metrics.SumInstanceMetric
return the number of tokens that two strings have in commmon
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)).
numCrossClusterTruePairs(Cluster, Cluster) - Method in class weka.deduping.BasicDeduper
Given two clusters, calculate the number of true pairs that will be added when the clusters are merged
numDiffClassPairs() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
numDistinctValues(int) - Method in class weka.core.Instances
Returns the number of distinct values of a given attribute.
numDistinctValues(Attribute) - Method in class weka.core.Instances
Returns the number of distinct values of a given attribute.
numElements() - Method in class weka.clusterers.AlgVector
Returns the number of elements in the vector.
numElements() - Method in class weka.core.AlgVector
Returns the number of elements in the vector.
numFalseNegatives(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate number of false negatives with respect to a particular class.
numFalseNegatives(int) - Method in class weka.classifiers.Evaluation
Calculate number of false negatives with respect to a particular class.
numFalsePositives(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate number of false positives 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.
numFoldsTipText() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
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.experiment.DedupingPRCurveCVResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.ExtractionResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveFolds
Returns the tip text for this property
numFoldsTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
numIncorrect() - Method in class weka.classifiers.trees.j48.Distribution
Returns total-numCorrect().
numIncorrect(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns perBag(index)-numCorrect(index).
numInstances() - Method in class weka.classifiers.EnsembleEvaluation
Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
numInstances() - Method in class weka.classifiers.Evaluation
Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
numInstances() - Method in class weka.core.Instances
Returns the number of instances in the dataset.
numInstances() - Method in class weka.core.KDTree
Gets number of instances in KDTree.
numIterationsTipText() - Method in class weka.classifiers.meta.Decorate
Returns the tip text for this property
numLeaves() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns number of leaves in tree structure.
numLeaves(int) - Method in class weka.classifiers.trees.m5.RuleNode
Sets the leaves' numbers
numNeighboursTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
numNodes() - Method in class weka.classifiers.trees.REPTree.Tree
Computes size of the tree.
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.
numOfAllNodes(PredictionNode) - Method in class weka.classifiers.trees.adtree.ADTree
Returns the total number of nodes in a tree.
numOfBoostingIterationsTipText() - Method in class weka.classifiers.trees.adtree.ADTree
 
numOfPredictionLeafNodes(PredictionNode) - Method in class weka.classifiers.trees.adtree.ADTree
Returns the number of leaf nodes in a tree - prediction nodes without children.
numOfPredictionNodes(PredictionNode) - Method in class weka.classifiers.trees.adtree.ADTree
Returns the number of prediction nodes in a tree.
numParameters() - Method in class weka.classifiers.functions.LinearRegression
Get the number of coefficients used in the model
numParameters() - Method in class weka.classifiers.functions.pace.PaceRegression
Get the number of coefficients used in the model
numPendingOutput() - Method in class weka.filters.Filter
Returns the number of instances pending output
numPendingOutput() - Method in class weka.filters.unsupervised.attribute.RemoveType
Returns the number of instances pending output
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
numSameClassPairs() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
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.RaceSearch
Returns the tip text for this property
numToSelectTipText() - Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
numTrueNegatives(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the number of true negatives with respect to a particular class.
numTrueNegatives(int) - Method in class weka.classifiers.Evaluation
Calculate the number of true negatives with respect to a particular class.
numTruePairs(Instances) - Method in class weka.deduping.BasicDeduper
Given a test set, calculate the number of true pairs
numTruePositives(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the number of true positives 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.
numValues() - Method in class weka.core.Attribute
Returns the number of attribute values.
numValues() - Method in class weka.core.Instance
Returns the number of values present.
numValues() - Method in class weka.core.SparseInstance
Returns the number of values in the sparse vector.
numXValFoldsTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
numberAttributesSelected() - Method in class weka.attributeSelection.AttributeSelection
Return the number of attributes selected from the most recent run of attribute selection
numberOfClusters() - Method in class weka.clusterers.Clusterer
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.Cobweb
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.DistributionMetaClusterer
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.HAC
A duplicate function to conform to Clusterer abstract class.
numberOfClusters() - Method in class weka.clusterers.MPCKMeans
A duplicate function to conform to Clusterer abstract class.
numberOfClusters() - Method in class weka.clusterers.PCKMeans
A duplicate function to conform to Clusterer abstract class.
numberOfClusters() - Method in class weka.clusterers.PCSoftKMeans
A duplicate function to conform to Clusterer abstract class.
numberOfClusters() - Method in class weka.clusterers.SeededKMeans
A duplicate function to conform to Clusterer abstract class.
numberOfClusters() - Method in class weka.clusterers.SimpleKMeans
Returns the number of clusters.
numberOfClusters() - Method in class weka.clusterers.XMeans
Returns the number of clusters.
numberOfLinearModels() - Method in class weka.classifiers.trees.m5.RuleNode
Get the number of linear models in the tree
numericDistribution(double[][], double[][][], int, int[], double[], double[][], Instances, double[]) - Method in class weka.classifiers.trees.REPTree.Tree
Computes class distribution for an attribute.
numericStats - Variable in class weka.core.AttributeStats
Stats on numeric value distributions
numericTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
 

O

OFF - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
 
ON - Static variable in interface weka.classifiers.lazy.kstar.KStarConstants
Some usefull constants
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
 
ORDERED - Static variable in class weka.datagenerators.BIRCHCluster
 
ORDERING_DEFAULT - Static variable in class weka.clusterers.PCKMeans
Define possible orderings
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_RANDOM - Static variable in class weka.clusterers.PCKMeans
 
ORDERING_SORTED - Static variable in class weka.clusterers.PCKMeans
 
ORDERING_SYMBOLIC - Static variable in class weka.core.Attribute
Constant set for symbolic attributes.
OUTPUT - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This unit is an output unit.
OVAL - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
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
 
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
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
 
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.
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
 
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.
objectiveFunction() - Method in class weka.clusterers.HAC
returns objective function, needed for compatibility with SemiSupClusterer
objectiveFunction() - Method in class weka.clusterers.MPCKMeans
returns objective function
objectiveFunction() - Method in class weka.clusterers.PCKMeans
returns objective function
objectiveFunction() - Method in class weka.clusterers.PCSoftKMeans
returns objective function
objectiveFunction() - Method in class weka.clusterers.SeededKMeans
returns objective function
objectiveFunction() - Method in interface weka.clusterers.SemiSupClusterer
Returns objective function if it has one, else -1.
objectiveFunction() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
objectiveFunction(double[]) - Method in class weka.core.Optimization
 
objectiveFunctionTipText() - Method in class weka.attributeSelection.MatlabNMF
Returns the tip text for this property
obtainVotes(Instance) - Method in class weka.classifiers.functions.SMO
Returns an array of votes for the given instance.
occList - Variable in class weka.deduping.metrics.TokenInfo
A list of TokenOccurences giving documents where this token occurs
oldBestInstancesForActiveLearning(int) - Method in class weka.clusterers.SeededKMeans
 
oldEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
Computes entropy of distribution before splitting.
onDemandDirectoryTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
onDemandDirectoryTipText() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
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.
onUnit(Graphics, int, int, int, int) - Method in class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd
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.
openHelpFrame() - Method in class weka.gui.PropertySheetPanel
 
openObject() - Method in class weka.gui.GenericObjectEditor.GOEPanel
Opens an object from a file selected by the user.
optionsPanel - Variable in class weka.gui.visualize.MatrixPanel
The panel that contains all the buttons and tools, i.e.
orderAdded - Variable in class weka.classifiers.trees.adtree.Splitter
The number this node was in the order of nodes added to the tree
ordering() - Method in class weka.core.Attribute
Returns the ordering of the attribute.
origDist - Variable in class weka.gui.visualize.MatrixPanel
For selecting same class distribution in the subsample as in the input
originalValue(double) - Method in class weka.filters.supervised.attribute.ClassOrder
Return the original internal class value given the randomized class value, i.e.
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.
outputFileTipText() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.CSVResultListener
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.ExtractionResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
outputFileTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the tip text for this property
outputFormat() - Method in class weka.filters.Filter
Deprecated. use getOutputFormat() instead.
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
 
outputFormatPeek() - Method in class weka.filters.Filter
Returns a reference to the current output format without copying it.
outputPeek() - Method in class weka.filters.Filter
Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.filters.unsupervised.attribute.RemoveType
Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.gui.streams.InstanceJoiner
Output an instance after filtering but do not remove from the output queue.
outputPeek() - Method in class weka.gui.streams.InstanceLoader
 
outputPeek() - Method in interface weka.gui.streams.InstanceProducer
 
outputValue(NeuralNode) - Method in class weka.classifiers.functions.neural.LinearUnit
This function calculates what the output value should be.
outputValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this to get the output value of this unit.
outputValue(NeuralNode) - Method in interface weka.classifiers.functions.neural.NeuralMethod
This function calculates what the output value should be.
outputValue(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd
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.

P

P0 - Static variable in class weka.core.Statistics
COEFFICIENTS FOR METHOD normalInverse() *
P1 - Static variable in class weka.core.Statistics
 
P2 - Static variable in class weka.core.Statistics
 
PAIRS_EASIEST - Static variable in class weka.core.metrics.HardPairwiseSelector
 
PAIRS_HARDEST - Static variable in class weka.core.metrics.HardPairwiseSelector
 
PAIRS_INTERVAL - Static variable in class weka.core.metrics.HardPairwiseSelector
 
PAIRS_RANDOM - Static variable in class weka.core.metrics.HardPairwiseSelector
 
PART - class weka.classifiers.rules.part.PART.
Class for generating a PART decision list.
PART() - Constructor for class weka.classifiers.rules.part.PART
 
PCKMeans - class weka.clusterers.PCKMeans.
Pairwise constrained k means clustering class.
PCKMeans() - Constructor for class weka.clusterers.PCKMeans
 
PCKMeans(Metric) - Constructor for class weka.clusterers.PCKMeans
 
PCSoftKMeans - class weka.clusterers.PCSoftKMeans.
Pairwise constrained k means clustering class.
PCSoftKMeans() - Constructor for class weka.clusterers.PCSoftKMeans
 
PCSoftKMeans(Metric) - Constructor for class weka.clusterers.PCSoftKMeans
 
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
 
PLUS_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
PMMethod - Static variable in class weka.classifiers.functions.pace.MixtureDistribution
The probability-measure-based method
POLYGON - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
POS_MODE_RANDOM_POSITIVES - Static variable in class weka.deduping.PairwiseSelector
 
POS_MODE_RANDOM_RECORDS - Static variable in class weka.deduping.PairwiseSelector
The record pair selection method
POS_MODE_STATIC_ACTIVE - Static variable in class weka.deduping.PairwiseSelector
 
PRECISION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
 
PROB_COST_FUNC_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
 
PROCESSING - Static variable in class weka.experiment.TaskStatusInfo
 
PROPERTIES - Static variable in class weka.experiment.DatabaseUtils
Properties associated with the database connection
PROPERTY_FILE - Static variable in class weka.experiment.DatabaseUtils
The name of the properties file
PROPERTY_FILE - Static variable in class weka.gui.GenericObjectEditor
The name of the properties file
PROPERTY_FILE - Static variable in class weka.gui.beans.KnowledgeFlow
Location of the property file for the KnowledgeFlow
PROPERTY_FILE - Static variable in class weka.gui.visualize.VisualizeUtils
The name of the properties file
PRUNETYPE_LOGLIKELIHOOD - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
PRUNETYPE_NONE - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
The pruning types
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.
PaceMatrix - class weka.classifiers.functions.pace.PaceMatrix.
Class for matrix manipulation used for pace regression.
PaceMatrix(int, int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct an m-by-n PACE matrix of zeros.
PaceMatrix(int, int, double) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct an m-by-n constant PACE matrix.
PaceMatrix(double[][]) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PACE matrix from a 2-D array.
PaceMatrix(double[][], int, int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PACE matrix quickly without checking arguments.
PaceMatrix(double[], int) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PaceMatrix from a one-dimensional packed array
PaceMatrix(DoubleVector) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PaceMatrix with a single column from a DoubleVector
PaceMatrix(Matrix) - Constructor for class weka.classifiers.functions.pace.PaceMatrix
Construct a PaceMatrix from a Matrix
PaceRegression - class weka.classifiers.functions.pace.PaceRegression.
Class for building pace regression linear models and using them for prediction.
PaceRegression() - Constructor for class weka.classifiers.functions.pace.PaceRegression
 
Pair - class weka.core.Pair.
Class for defining pairs of numbers.
Pair(int, double) - Constructor for class weka.core.Pair
 
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.
PairedTTester - class weka.experiment.PairedTTester.
Calculates T-Test statistics on data stored in a set of instances.
PairedTTester() - Constructor for class weka.experiment.PairedTTester
 
PairwiseSelector - class weka.core.metrics.PairwiseSelector.
Abstract PairwiseSelector class.
PairwiseSelector() - Constructor for class weka.core.metrics.PairwiseSelector
 
PairwiseSelector - class weka.deduping.PairwiseSelector.
PairwiseSelector class.
PairwiseSelector() - Constructor for class weka.deduping.PairwiseSelector
A default constructor
PairwiseSelector.ReverseComparator - class weka.deduping.PairwiseSelector.ReverseComparator.
We will need this reverse comparator class to traverse a TreeSet backwards
PairwiseSelector.ReverseComparator() - Constructor for class weka.deduping.PairwiseSelector.ReverseComparator
 
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
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
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
 
Porter - class weka.deduping.metrics.Porter.
The Porter stemmer for reducing words to their base stem form.
Porter() - Constructor for class weka.deduping.metrics.Porter
 
Prediction - interface weka.classifiers.evaluation.Prediction.
Encapsulates a single evaluatable prediction: the predicted value plus the actual class value.
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.
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.
PrincipalComponents - class weka.attributeSelection.PrincipalComponents.
Class for performing principal components analysis/transformation.
PrincipalComponents() - Constructor for class weka.attributeSelection.PrincipalComponents
 
Prism - class weka.classifiers.rules.Prism.
Class for building and using a PRISM classifier.
Prism() - Constructor for class weka.classifiers.rules.Prism
 
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.
Prototype - class weka.classifiers.misc.Prototype.
Class for building and using a simple prototype classifier.
Prototype() - Constructor for class weka.classifiers.misc.Prototype
 
PrototypeMetric - class weka.classifiers.misc.PrototypeMetric.
Prototype learner for purely real-valued instances that uses a general weka.core.metrics.Metric.
PrototypeMetric() - Constructor for class weka.classifiers.misc.PrototypeMetric
 
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) - 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.
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.
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.AttributeVisualizationPanel
Paints this component
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.beans.BeanVisual
 
paintComponent(Graphics) - Method in class weka.gui.beans.KnowledgeFlow.BeanLayout
 
paintComponent(Graphics) - Method in class weka.gui.treevisualizer.TreeVisualizer
Updates the screen contents.
paintComponent(Graphics) - Method in class weka.gui.visualize.AttributePanel.AttributeSpacing
paints all the visible instances to the panel , and recalculates their position if need be.
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.
paintNominal(Graphics) - Method in class weka.gui.visualize.ClassPanel
Renders the legend for a nominal colouring attribute
paintNumeric(Graphics) - Method in class weka.gui.visualize.ClassPanel
Renders the legend for a numeric colouring attribute
paintValue(Graphics, Rectangle) - Method in class weka.gui.CostMatrixEditor
Paints a graphical representation of the object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.FileEditor
Paints a representation of the current Object.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericArrayEditor
Paints a representation of the current classifier.
paintValue(Graphics, Rectangle) - Method in class weka.gui.GenericObjectEditor
Paints a representation of the current Object.
pairwiseCoupling(double[][], double[][]) - Method in class weka.classifiers.functions.SMO
Implements pairwise coupling.
pairwiseFMeasure() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
pairwisePrecision() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
pairwiseRecall() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
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
parentValue() - Method in class weka.gui.HierarchyPropertyParser
The value in the parent node.
parseDate(String) - Method in class weka.core.Attribute
 
parsePlotPoints(String) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.ExtractionResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Parse a string of double separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
parsePlotPoints(String) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Parse a string of doubles separated by commas or spaces into a sorted array of doubles
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
 
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.EnsembleEvaluation
Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctCorrect() - Method in class weka.classifiers.Evaluation
Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
pctIncorrect() - Method in class weka.classifiers.EnsembleEvaluation
Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctIncorrect() - Method in class weka.classifiers.Evaluation
Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
pctUnclassified() - Method in class weka.classifiers.EnsembleEvaluation
Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
pctUnclassified() - Method in class weka.classifiers.Evaluation
Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
peek() - Method in class weka.core.Queue
Gets object from the front of the queue.
perBag(int) - Method in class weka.classifiers.trees.j48.Distribution
Returns number of (possibly fractional) instances in given bag.
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.
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
percentToEliminatePerIterationTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
percentageTipText() - Method in class weka.filters.unsupervised.instance.RemovePercentage
Returns the tip text for this property
performRequest(String) - Method in class weka.gui.beans.Classifier
Perform a particular request
performRequest(String) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Perform the named request
performRequest(String) - Method in class weka.gui.beans.CrossValidationFoldMaker
Perform the named request
performRequest(String) - Method in class weka.gui.beans.DataVisualizer
Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.Filter
Perform the named request
performRequest(String) - Method in class weka.gui.beans.GraphViewer
Perform the named request
performRequest(String) - Method in class weka.gui.beans.Loader
Perform the named request
performRequest(String) - Method in class weka.gui.beans.StripChart
Describe performRequest method here.
performRequest(String) - Method in class weka.gui.beans.TextViewer
Perform the named request
performRequest(String) - Method in class weka.gui.beans.TrainTestSplitMaker
Perform the named request
performRequest(String) - Method in interface weka.gui.beans.UserRequestAcceptor
Perform the named request
performTest() - Method in class weka.gui.experiment.ResultsPanel
Carries out a t-test using the current configuration.
place(Node) - Method in interface weka.gui.treevisualizer.NodePlace
The function to call to postion the tree that starts at Node r
place(Node) - Method in class weka.gui.treevisualizer.PlaceNode1
Call this function to have each node in the tree starting at 'r' placed in a visual (not logical, they already are) tree position.
place(Node) - Method in class weka.gui.treevisualizer.PlaceNode2
The Funtion to call to have the nodes arranged.
plotOutput() - Method in class weka.gui.experiment.ResultsPanel
Plot the currently selected output buffer
plotPoint(int) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Return the number of training examples for the ith point on the curve for plotPoints as specified.
plotPoint(int) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Return the number of training examples for the ith point on the curve for plotPoints as specified.
plotPoint(int) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Return the number of training examples for the ith point on the curve for plotPoints as specified.
plotPoint(int) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Return the amount of noise for the ith point on the curve for plotPoints as specified.
plotPoint(int) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Return the number of training examples for the ith point on the curve for plotPoints as specified.
plotPoint(int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Return the number of training examples for the ith point on the curve for plotPoints as specified.
plotPoint(int) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Return the number of training examples for the ith point on the curve for plotPoints as specified.
plotPoint(int) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Return the number of training examples for the ith point on the curve for plotPoints as specified.
plotPoint(int) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Return the number of training examples for the ith point on the curve for plotPoints as specified.
plotPointsTipText() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.ExtractionResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
plotPointsTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the tip text for this property
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
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.
points - Variable in class weka.classifiers.functions.pace.DiscreteFunction
 
points - Variable in class weka.experiment.Grapher
Ordered array of points on learning in number of training examples
points - Variable in class weka.experiment.NoiseGrapher
Ordered array of points on learning in number of training examples
pop() - Method in class weka.core.Queue
Pops an object from the front of the queue.
populateNegStrPairSet(StringMetric, TreeSet, int) - Method in class weka.deduping.PairwiseSelector
Populate a provided treeset with a sufficient population of negative StringPair's
populateNegativePairSet(Metric, TreeSet) - Method in class weka.core.metrics.HardPairwiseSelector
Populate a treeset with all negative TrainingPair's
populatePosStrPairSet(StringMetric, TreeSet, int) - Method in class weka.deduping.PairwiseSelector
Populate a provided treeset with all positive StringPair's
populatePositivePairSet(Metric, TreeSet) - Method in class weka.core.metrics.HardPairwiseSelector
Populate a treeset with all positive TrainingPair's
populationSizeTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
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 - Variable in class weka.core.metrics.TrainingPair
 
positive - Variable in class weka.deduping.InstancePair
 
positive - Variable in class weka.deduping.StringPair
 
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(int[]) - Method in class weka.attributeSelection.ASEvaluation
Provides a chance for a attribute evaluator to do any special post processing of the selected attribute set.
postProcess(int[]) - Method in class weka.attributeSelection.CfsSubsetEval
Calls locallyPredictive in order to include locally predictive attributes (if requested).
postProcess() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
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() - 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(ResultProducer) - Method in class weka.experiment.CSVResultListener
Perform any postprocessing.
postProcess() - Method in class weka.experiment.CrossValidationResultProducer
Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultListener
Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess() - Method in class weka.experiment.DatabaseResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Perform any postprocessing.
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.ExtractionResultProducer
Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.InstancesResultListener
Perform any postprocessing.
postProcess() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Perform any postprocessing.
postProcess(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
When this method is called, it indicates that no more results will be sent that need to be grouped together in any way.
postProcess() - Method in class weka.experiment.LearningRateResultProducer
When this method is called, it indicates that no more requests to generate results for the current experiment will be sent.
postProcess() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.RandomSplitResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.RemoteExperiment
overides the one in Experiment
postProcess(ResultProducer) - Method in interface weka.experiment.ResultListener
Perform any postprocessing.
postProcess() - Method in interface weka.experiment.ResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Perform any postprocessing.
postProcess() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Perform any postprocessing.
posteriorsArray - Variable in class weka.classifiers.lazy.LBR
 
potential(int, double, double[], double[], boolean) - Method in class weka.classifiers.rules.RuleStats
Calculate the potential to decrease DL of the ruleset, i.e.
prePlot(Graphics) - Method in interface weka.gui.visualize.Plot2DCompanion
Something to be drawn before the plot itself
prePlot(Graphics) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Renders the polygons if necessary
preProcess() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.AveragingResultProducer
Prepare for the results to be received.
preProcess() - Method in class weka.experiment.AveragingResultProducer
Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.CSVResultListener
Prepare for the results to be received.
preProcess() - Method in class weka.experiment.CrossValidationResultProducer
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.DatabaseResultProducer
Prepare for the results to be received.
preProcess() - Method in class weka.experiment.DatabaseResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Prepare to generate results.
preProcess() - Method in class weka.experiment.ExtractionResultProducer
Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.InstancesResultListener
Prepare for the results to be received.
preProcess() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Prepare to generate results.
preProcess(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
Prepare for the results to be received.
preProcess() - Method in class weka.experiment.LearningRateResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.RandomSplitResultProducer
Prepare to generate results.
preProcess(ResultProducer) - Method in interface weka.experiment.ResultListener
Prepare for the results to be received.
preProcess() - Method in interface weka.experiment.ResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Prepare to generate results.
preProcess() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Prepare to generate results.
precision(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the precision with respect to a particular class.
precision(int) - Method in class weka.classifiers.Evaluation
Calculate the precision with respect to a particular class.
predicted() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets the predicted class value.
predicted() - Method in class weka.classifiers.evaluation.NumericPrediction
Gets the predicted class value.
predicted() - Method in interface weka.classifiers.evaluation.Prediction
Gets the predicted class value.
predictionText(Classifier, Instance, int) - Method in class weka.gui.explorer.ClassifierPanel
 
predictionValueForInstance(Instance, PredictionNode, double) - Method in class weka.classifiers.trees.adtree.ADTree
Returns the class prediction value (vote) for an instance.
prefix() - Method in class weka.classifiers.trees.j48.ClassifierTree
Returns tree in prefix order.
prefix() - Method in class weka.classifiers.trees.j48.J48
Returns tree in prefix order.
prefix() - Method in interface weka.core.Matchable
Returns a string that describes a tree representing the object in prefix order.
prepareData() - Method in class weka.experiment.PairedTTester
Separates the instances into resultsets and by dataset/run.
prepareEngine() - Method in class weka.clusterers.assigners.LPAssigner
Create octave m-file
prepareMatlab(String) - Method in class weka.attributeSelection.MatlabICA
Create matlab m-file for ICA
prepareMatlab() - Method in class weka.attributeSelection.MatlabPCA
Create matlab m-file for PCA
prepareMatlab(String) - Method in class weka.core.metrics.BarHillelMetricMatlab
Create matlab m-file for ICA
prepareMatlabScript() - Method in class weka.core.metrics.MatlabMetricLearner
Create matlab m-file for PCA
prepareOctave(String) - Method in class weka.core.metrics.BarHillelMetric
Create octave m-file for ICA
prepareOctave(String) - Method in class weka.core.metrics.XingMetric
Create octave m-file for ICA
print() - Method in class weka.classifiers.bayes.ADNode
 
print(String) - Method in class weka.classifiers.bayes.VaryNode
 
print(int, 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(NumberFormat, int) - Method in class weka.classifiers.functions.pace.Matrix
Print the matrix to stdout.
print(PrintWriter, NumberFormat, int) - Method in class weka.classifiers.functions.pace.Matrix
Print the matrix to the output stream.
print() - Method in class weka.deduping.metrics.HashMapVector
Print out the vector showing the tokens and their weights
print3dMatrix(double[][][]) - Static method in class weka.deduping.metrics.AffineProbMetric
 
printAlignmentMatrix(String, String, int, double[][][]) - Method in class weka.deduping.metrics.AffineProbMetric
 
printAllModels() - Method in class weka.classifiers.trees.m5.RuleNode
Print all the linear models at the learf (debugging purposes)
printAttributeSummary(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Print out a short summary string for the dataset characteristics
printCluster(int) - Method in class weka.clusterers.HAC
Outputs the specified cluster
printClusters() - Method in class weka.clusterers.HAC
Outputs the current clustering
printClusters() - Method in class weka.clusterers.MPCKMeans
Prints clusters
printClusters() - Method in class weka.clusterers.PCKMeans
Prints clusters
printClusters() - Method in class weka.clusterers.PCSoftKMeans
Prints clusters
printClusters() - Method in class weka.clusterers.SeededKMeans
Prints clusters
printFeatures() - Method in class weka.classifiers.rules.DecisionTable
Returns a string description of the features selected
printIndexClusters() - Method in class weka.clusterers.MPCKMeans
Outputs the current clustering
printIndexClusters() - Method in class weka.clusterers.PCKMeans
Outputs the current clustering
printIndexClusters() - Method in class weka.clusterers.PCSoftKMeans
Outputs the current clustering
printIndexClusters() - Method in class weka.clusterers.SeededKMeans
Outputs the current clustering
printIntClusters() - Method in class weka.clusterers.HAC
Outputs the current clustering
printIntClusters() - Method in class weka.deduping.BasicDeduper
Outputs the current clustering
printLeafModels() - Method in class weka.classifiers.trees.m5.RuleNode
print all leaf models
printList() - Method in class weka.classifiers.sparse.IBkMetric.NeighborList
Prints out the contents of the neighborlist
printMatrices(String, String) - Method in class weka.deduping.metrics.AffineProbMetric
print out the three matrices
printNodeLinearModel() - Method in class weka.classifiers.trees.m5.RuleNode
print the linear model at this node
printOpProbs() - Method in class weka.deduping.metrics.AffineProbMetric
print out some data in case things go wrong
printOptions(String[]) - Static method in class weka.core.CheckOptionHandler
Prints the given options to a string.
printRanges(Instances, double[][]) - Static method in class weka.clusterers.XMeans
Function should be in the Instances class!! Prints a range.
printRanges(double[][]) - Static method in class weka.core.Instances
prints the ranges.
printRanges(Instances, double[][]) - Static method in class weka.core.Instances
Prints a range to standard output
printTopAttributes(double[], int, int) - Method in class weka.core.metrics.GDMetricLearner
Print the heaviest-weighted attributes for a given set of weights
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
priorEntropy() - Method in class weka.classifiers.EnsembleEvaluation
Calculate the entropy of the prior distribution
priorEntropy() - Method in class weka.classifiers.Evaluation
Calculate the entropy of the prior distribution
priorVal(double[][]) - Method in class weka.classifiers.trees.REPTree.Tree
Computes value of splitting criterion before split.
priorVal(double[][]) - Method in class weka.classifiers.trees.RandomTree
Computes value of splitting criterion before split.
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.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.ChisqMixture
Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
probabilityMatrix(DoubleVector, PaceMatrix) - Method in class weka.classifiers.functions.pace.NormalMixture
Contructs the probability matrix for mixture estimation, given a set of support points and a set of intervals.
processColour(String, Color) - Static method in class weka.gui.visualize.VisualizeUtils
Parses a string containing either a named colour or r,g,b values.
processData() - Method in class weka.experiment.Grapher
Read in data for the current values of dataset and metric by indexing for each scheme+options name an array of Stats objects for each point on the learning curve
processData() - Method in class weka.experiment.NoiseGrapher
Read in data for the current values of dataset and metric by indexing for each scheme+options name an array of Stats objects for each point on the learning curve
processResults(Object[], double) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Given an array containing the overall results of a deduping experiment, produce an array containing results for a specific recall level
processResults(Object[], double) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Given an array containing the overall results of a deduping experiment, produce an array containing results for a specific recall level
projectInstance(Instance) - Method in class weka.core.metrics.WeightedMahalanobis
given an instance, project it using the weights matrix and store it in the hash
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
prune(Instances, boolean) - Method in class weka.classifiers.rules.JRip.RipperRule
Prune all the possible final sequences of the rule using the pruning data.
prune() - Method in class weka.classifiers.trees.j48.C45PruneableClassifierTree
Prunes a tree using C4.5's pruning procedure.
prune() - Method in class weka.classifiers.trees.j48.PruneableClassifierTree
Prunes a tree.
prune() - Method in class weka.classifiers.trees.m5.RuleNode
Recursively prune the tree
pruneEnd() - Method in class weka.classifiers.rules.part.C45PruneableDecList
Prunes the end of the rule.
pruneEnd() - Method in class weka.classifiers.rules.part.PruneableDecList
Prunes the end of the rule.
pruneItemSets(FastVector, Hashtable) - Static method in class weka.associations.ItemSet
Prunes a set of (k)-item sets using the given (k-1)-item sets.
pruneLastModel() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
pruneRules(FastVector[], double) - Static method in class weka.associations.ItemSet
Prunes a set of rules.
pruneToK(int) - Method in class weka.classifiers.sparse.IBkMetric.NeighborList
Prunes the list to contain the k nearest neighbors.
pruningTypeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
purity() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
push(Object) - Method in class weka.core.Queue
Appends an object to the back of the queue.
push(Instance) - Method in class weka.filters.Filter
Adds an output instance to the queue.
put(Object, Object) - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
putAll(Map) - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
putResultInTable(String, ResultProducer, Object[], Object[]) - Method in class weka.experiment.DatabaseUtils
Executes a database query to insert a result for the supplied key into the database.

Q

Q0 - Static variable in class weka.core.Statistics
 
Q1 - Static variable in class weka.core.Statistics
 
Q2 - Static variable in class weka.core.Statistics
 
QBag - class weka.classifiers.meta.QBag.
This class implements Query-by-Bagging based on Abe and Mamitsuka (ICML 98).
QBag() - Constructor for class weka.classifiers.meta.QBag
 
QBoost - class weka.classifiers.meta.QBoost.
This class implements Query-by-Boosting based on Abe and Mamitsuka (ICML 98).
QBoost() - Constructor for class weka.classifiers.meta.QBoost
 
Queue - class weka.core.Queue.
Class representing a FIFO queue.
Queue() - Constructor for class weka.core.Queue
 
Queue.QueueNode - class weka.core.Queue.QueueNode.
Represents one node in the queue.
Queue.QueueNode(Object) - Constructor for class weka.core.Queue.QueueNode
Creates a queue node with the given contents
queryTipText() - Method in class weka.experiment.InstanceQuery
Returns the tip text for this property
quote(String) - Static method in class weka.core.Utils
Quotes a string if it contains special characters.

R

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
RANDOMIZED - Static variable in class weka.datagenerators.BIRCHCluster
 
RANGE_BOUNDS - Static variable in class weka.classifiers.meta.ThresholdSelector
 
RANGE_NONE - Static variable in class weka.classifiers.meta.ThresholdSelector
 
RDG1 - class weka.datagenerators.RDG1.
Class to generate data randomly by producing a decision list.
RDG1() - Constructor for class weka.datagenerators.RDG1
 
RECALL_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducer
 
RECALL_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
RECALL_FIELD_NAME - Static variable in class weka.experiment.ExtractionResultProducer
 
RECALL_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
 
RECTANGLE - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
RELATION_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
The name of the relation used in cost curve datasets
RELATION_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
The name of the relation used in threshold curve datasets
REMOVE_CHILDREN - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
 
REPTree - class weka.classifiers.trees.REPTree.
Fast decision tree learner.
REPTree() - Constructor for class weka.classifiers.trees.REPTree
 
REPTree.Tree - class weka.classifiers.trees.REPTree.Tree.
An inner class for building and storing the tree structure
REPTree.Tree() - Constructor for class weka.classifiers.trees.REPTree.Tree
 
RIGHT - Static variable in class weka.classifiers.trees.m5.Rule
 
RMNAssigner - class weka.clusterers.assigners.RMNAssigner.
 
RMNAssigner() - Constructor for class weka.clusterers.assigners.RMNAssigner
 
RMNAssigner(MPCKMeans) - Constructor for class weka.clusterers.assigners.RMNAssigner
 
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
RUN_FIELD_NAME - Static variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.ActiveLearningCurveCVResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
RUN_FIELD_NAME - Static variable in class weka.experiment.ExtractionResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.LearningCurveCrossValidationResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.SemiSupCrossValidationResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
RUN_FIELD_NAME - Static variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
R_HIGH - Static variable in class weka.clusterers.XMeans
 
R_LOW - Static variable in class weka.clusterers.XMeans
Index in ranges for LOW and HIGH and WIDTH
R_MAX - Static variable in class weka.classifiers.lazy.IBk
 
R_MAX - Static variable in class weka.core.DistanceFunction
 
R_MAX - Static variable in class weka.core.Instances
 
R_MAX - Static variable in class weka.core.KDTree
 
R_MIN - Static variable in class weka.classifiers.lazy.IBk
Index in ranges for LOW and HIGH and WIDTH
R_MIN - Static variable in class weka.core.DistanceFunction
Index in ranges for MIN and MAX and WIDTH
R_MIN - Static variable in class weka.core.Instances
Index in ranges for MIN and MAX and WIDTH
R_MIN - Static variable in class weka.core.KDTree
Index in ranges for LOW and HIGH and WIDTH
R_WIDTH - Static variable in class weka.classifiers.lazy.IBk
 
R_WIDTH - Static variable in class weka.clusterers.XMeans
 
R_WIDTH - Static variable in class weka.core.DistanceFunction
 
R_WIDTH - Static variable in class weka.core.Instances
 
R_WIDTH - Static variable in class weka.core.KDTree
 
RaceSearch - class weka.attributeSelection.RaceSearch.
Class for performing a racing search.
RaceSearch() - Constructor for class weka.attributeSelection.RaceSearch
 
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
 
RacedIncrementalLogitBoost.Committee - class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee.
 
RacedIncrementalLogitBoost.Committee(int) - Constructor for class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
RandomAssigner - class weka.clusterers.assigners.RandomAssigner.
 
RandomAssigner() - Constructor for class weka.clusterers.assigners.RandomAssigner
 
RandomForest - class weka.classifiers.trees.RandomForest.
Class for constructing random forests.
RandomForest() - Constructor for class weka.classifiers.trees.RandomForest
 
RandomPairwiseSelector - class weka.core.metrics.RandomPairwiseSelector.
RandomPairwiseSelector class.
RandomPairwiseSelector() - Constructor for class weka.core.metrics.RandomPairwiseSelector
A default constructor
RandomSearch - class weka.attributeSelection.RandomSearch.
Class for performing a random search.
RandomSearch() - Constructor for class weka.attributeSelection.RandomSearch
Constructor
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
Randomizable - interface weka.core.Randomizable.
Interface to something that has random behaviour that is able to be seeded with an integer.
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
 
Range - class weka.core.Range.
Class representing a range of cardinal numbers.
Range() - Constructor for class weka.core.Range
Default constructor.
Range(String) - Constructor for class weka.core.Range
Constructor to set initial range.
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
 
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
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.
RegressionBVDecompose - class weka.classifiers.RegressionBVDecompose.
Class for performing a Bias-Variance decomposition on any regressor based on the method specified in:
RegressionBVDecompose() - Constructor for class weka.classifiers.RegressionBVDecompose
 
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 discretized.
RegressionByDiscretization() - Constructor for class weka.classifiers.meta.RegressionByDiscretization
 
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.
ReliefFAttributeEval - class weka.attributeSelection.ReliefFAttributeEval.
Class for Evaluating attributes individually using ReliefF.
ReliefFAttributeEval() - Constructor for class weka.attributeSelection.ReliefFAttributeEval
Constructor
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
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
 
Remove - class weka.filters.unsupervised.attribute.Remove.
An instance filter that deletes a range of attributes from the dataset.
Remove() - Constructor for class weka.filters.unsupervised.attribute.Remove
 
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
 
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
 
RemovePercentage - class weka.filters.unsupervised.instance.RemovePercentage.
This filter outputs a given percentage of a dataset.
RemovePercentage() - Constructor for class weka.filters.unsupervised.instance.RemovePercentage
 
RemoveRange - class weka.filters.unsupervised.instance.RemoveRange.
This filter takes a dataset and outputs a subset of it.
RemoveRange() - Constructor for class weka.filters.unsupervised.instance.RemoveRange
 
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
 
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
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
 
Resample - class weka.filters.supervised.instance.Resample.
Produces a random subsample of a dataset.
Resample() - Constructor for class weka.filters.supervised.instance.Resample
 
Resample - class weka.filters.unsupervised.instance.Resample.
Produces a random subsample of a dataset.
Resample() - Constructor for class weka.filters.unsupervised.instance.Resample
 
ResultHistoryPanel - class weka.gui.ResultHistoryPanel.
A component that accepts named stringbuffers and displays the name in a list box.
ResultHistoryPanel(JTextComponent) - Constructor for class weka.gui.ResultHistoryPanel
Create the result history object
ResultHistoryPanel.RKeyAdapter - class 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
 
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.
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.
Ridor - class weka.classifiers.rules.Ridor.
The implementation of a RIpple-DOwn Rule learner.
Ridor() - Constructor for class weka.classifiers.rules.Ridor
 
RtoP(double[], int) - Static method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Convert from function responses to probabilities
Rule - class weka.classifiers.rules.Rule.
Abstract class of generic rule
Rule() - Constructor for class weka.classifiers.rules.Rule
 
Rule - class weka.classifiers.trees.m5.Rule.
Generates a single m5 tree or rule
Rule() - Constructor for class weka.classifiers.trees.m5.Rule
Constructor declaration
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
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.
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(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
random - Variable in class weka.classifiers.meta.TestEnsembleClassifier
 
randomClassDistribution(Random) - Method in class weka.core.SoftClassifiedFullInstance
Return a random class distribution
randomClassDistribution(Random) - Method in class weka.core.SoftClassifiedSparseInstance
Return a random class distribution
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
 
randomSeedTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
randomSeedTipText() - Method in class weka.classifiers.trees.adtree.ADTree
 
randomSeedTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the tip text for this property
randomSubset(int, int) - Static method in class weka.clusterers.HAC
get an array of random indeces out of n possible values.
randomSubset(int, int) - Static method in class weka.core.metrics.HardPairwiseSelector
get an array of numIdxs random indeces out of n possible values.
randomSubset(int, int) - Static method in class weka.deduping.PairwiseSelector
get an array random indeces out of n possible values.
randomWidthFactorTipText() - Method in class weka.classifiers.meta.MultiClassClassifier
 
randomize(Random) - Method in class weka.core.Instances
Shuffles the instances in the set so that they are ordered randomly.
randomizeDataTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
rangeCorrectionTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
rangeLower(String) - Method in class weka.core.Range
Translates a range into it's lower index.
rangeSingle(String) - Method in class weka.core.Range
Translates a single string selection into it's internal 0-based equivalent
rangeUpper(String) - Method in class weka.core.Range
Translates a range into it's upper index.
rangesSet() - Method in class weka.core.Instances
Check if ranges are set.
rankTipText() - Method in class weka.attributeSelection.MatlabNMF
Returns the tip text for this property
rankedAttributes() - Method in class weka.attributeSelection.AttributeSelection
get the final ranking of the attributes.
rankedAttributes() - Method in class weka.attributeSelection.ForwardSelection
Produces a ranked list of attributes.
rankedAttributes() - Method in class weka.attributeSelection.RaceSearch
 
rankedAttributes() - Method in interface weka.attributeSelection.RankedOutputSearch
Returns a X by 2 list of attribute indexes and corresponding evaluations from best (highest) to worst.
rankedAttributes() - Method in class weka.attributeSelection.Ranker
Sorts the evaluated attribute list
rawOutputTipText() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.ExtractionResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
rawOutputTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the tip text for this property
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.
read(BufferedReader) - Static method in class weka.classifiers.functions.pace.Matrix
Read a matrix from a stream.
readColumnVectors(String) - Method in class weka.attributeSelection.MatlabICA
Read column vectors from a text file
readColumnVectors(String, int) - Method in class weka.attributeSelection.MatlabPCA
Read column vectors from a text file
readHeader(StreamTokenizer) - Method in class weka.core.Instances
Reads and stores header of an ARFF file.
readInstance(Reader) - Method in class weka.core.Instances
Reads a single instance from the reader and appends it to the dataset.
readInstances() - Method in class weka.datagenerators.TextSource
Reads all documents and converts them all to sparse vectors.
readMatrix(String) - Method in class weka.core.metrics.BarHillelMetric
Read column vectors from a text file
readMatrix(String) - Method in class weka.core.metrics.BarHillelMetricMatlab
Read column vectors from a text file
readMatrix(String) - Method in class weka.core.metrics.XingMetric
Read column vectors from a text file
readOldFormat(Reader) - Method in class weka.classifiers.CostMatrix
Loads a cost matrix in the old format from a reader.
readPrediction(File) - Method in class weka.classifiers.sparse.SVMlight
Read the prediction of SVM-light
readProperties(String) - Static method in class weka.core.Utils
Reads properties that inherit from three locations.
readVector(double[], String) - Method in class weka.attributeSelection.MatlabNMF
Read a column vector from a text file
readVector(String) - Method in class weka.attributeSelection.MatlabPCA
Read a column vector from a text file
readVector(String) - Method in class weka.core.metrics.MatlabMetricLearner
Read a column vector from a text file
readVectors(String, int) - Method in class weka.attributeSelection.MatlabNMF
Read column vectors from a text file
realCount - Variable in class weka.core.AttributeStats
The number of real-like values (i.e.
recall(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the recall with respect to a particular class.
recall(int) - Method in class weka.classifiers.Evaluation
Calculate the recall with respect to a particular class.
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
reduceDimensionality(Instances) - Method in class weka.attributeSelection.AttributeSelection
reduce the dimensionality of a set of instances to include only those attributes chosen by the last run of attribute selection.
reduceDimensionality(Instance) - Method in class weka.attributeSelection.AttributeSelection
reduce the dimensionality of a single instance to include only those attributes chosen by the last run of attribute selection.
reduceMatrix(double[][]) - Static method in class weka.core.ContingencyTables
Reduces a matrix by deleting all zero rows and columns.
reducedErrorPrune() - Method in class weka.classifiers.trees.REPTree.Tree
Prunes the tree using the hold-out data (bottom-up).
reevaluateModel(String, Classifier, Instances) - Method in class weka.gui.explorer.ClassifierPanel
Re-evaluates the named classifier with the current test set.
reevaluateModel(String, Clusterer, Instances, int[]) - Method in class weka.gui.explorer.ClustererPanel
Re-evaluates the named clusterer with the current test set.
refreshFreqTipText() - Method in class weka.gui.beans.StripChart
GUI Tip text
registerClass(String) - Method in class weka.datagenerators.TextSource
 
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.
relationName() - Method in class weka.core.Instances
Returns the relation's name.
relativeAbsoluteError() - Method in class weka.classifiers.EnsembleEvaluation
Returns the relative absolute error.
relativeAbsoluteError() - Method in class weka.classifiers.Evaluation
Returns the relative absolute error.
relativeDL(int, double, boolean) - Method in class weka.classifiers.rules.RuleStats
The description length (DL) of the ruleset relative to if the rule in the given position is deleted, which is obtained by:
MDL if the rule exists - MDL if the rule does not exist
Note the minimal possible DL of the ruleset is calculated(i.e.
remoteExperimentStatus(RemoteExperimentEvent) - Method in interface weka.experiment.RemoteExperimentListener
Called when progress has been made in a remote experiment
remove(Object) - Method in class weka.core.ProtectedProperties
Overrides a method to prevent the properties from being modified.
remove() - Method in class weka.gui.beans.BeanConnection
Remove this connection
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.AbstractDataSource
Remove a listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.ClassAssigner
 
removeDataSourceListener(DataSourceListener) - Method in interface weka.gui.beans.DataSource
Remove a data source listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Filter
Remove a data source listener
removeDataSourceListener(DataSourceListener) - Method in class weka.gui.beans.Loader
Remove a listener
removeElementAt(int) - Method in class weka.core.FastVector
Deletes an element from this vector.
removeFileExtension(String) - Static method in class weka.experiment.Grapher
Return the name of a file with the extension removed
removeFileExtension(String) - Static method in class weka.experiment.NoiseGrapher
Return the name of a file with the extension removed
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.AbstractDataSource
Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.ClassAssigner
 
removeInstanceListener(InstanceListener) - Method in interface weka.gui.beans.DataSource
Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.Filter
Remove an instance listener
removeInstanceListener(InstanceListener) - Method in class weka.gui.beans.Loader
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
 
removeInstances(Instances, int) - Method in class weka.classifiers.meta.ActiveDecorate
Removes a specified number of instances from the given set of instances.
removeInstances(Instances, int) - Method in class weka.classifiers.meta.Crate
Removes a specified number of instances from the given set of instances.
removeInstances(Instances, int) - Method in class weka.classifiers.meta.DEC
 
removeInstances(Instances, int) - Method in class weka.classifiers.meta.Decorate
Removes a specified number of instances from the given set of instances.
removeInstances(Instances, int) - Method in class weka.classifiers.meta.Fable
Removes a specified number of instances from the given set of instances.
removeInstances(Instances, int) - Method in class weka.classifiers.meta.SemiSupDecorate
 
removeLabels(Instances) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Remove all class labels from given data.
removeLast() - Method in class weka.classifiers.rules.RuleStats
Remove the last rule in the ruleset as well as it's stats.
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.
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
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.GenericArrayEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.GenericObjectEditor
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.PropertySheetPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.SetInstancesPanel
Removes a PropertyChangeListener.
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.BeanVisual
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassAssignerCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.ClassifierCustomizer
Remove a property change listener
removePropertyChangeListener(PropertyChangeListener) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer
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.LoaderCustomizer
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.TrainTestSplitMakerCustomizer
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(PropertyChangeListener) - Method in class weka.gui.explorer.PreprocessPanel
Removes a PropertyChangeListener.
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.AbstractTestSetProducer
Remove a listener for test sets
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Remove a test set listener
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.ClassAssigner
 
removeTestSetListener(TestSetListener) - Method in class weka.gui.beans.Filter
Remove a test set listener
removeTestSetListener(TestSetListener) - Method in interface weka.gui.beans.TestSetProducer
Remove a listener for test set events
removeTextListener(TextListener) - Method in class weka.gui.beans.Classifier
Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Remove a text listener
removeTextListener(TextListener) - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Remove a text listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.ClassAssigner
 
removeTrainingSetListener(TrainingSetListener) - Method in class weka.gui.beans.Filter
Remove a training set listener
removeTrainingSetListener(TrainingSetListener) - Method in interface weka.gui.beans.TrainingSetProducer
Remove a training set listener
renameAttribute(int, String) - Method in class weka.core.Instances
Renames an attribute.
renameAttribute(Attribute, String) - Method in class weka.core.Instances
Renames an attribute.
renameAttributeValue(int, int, String) - Method in class weka.core.Instances
Renames the value of a nominal (or string) attribute value.
renameAttributeValue(Attribute, String, String) - Method in class weka.core.Instances
Renames the value of a nominal (or string) attribute value.
reorganizeTrainForActiveLearning(ArrayList, int, InstancePair[], Instances) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
reorganizeTrainForActiveLearning(Instances, int, int[]) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
replaceMissingValues(double[]) - Method in class weka.core.BinarySparseInstance
Does nothing, since we don't support missing values.
replaceMissingValues(double[]) - Method in class weka.core.Instance
Replaces all missing values in the instance with the values contained in the given array.
replaceMissingValues(double[]) - Method in class weka.core.SparseInstance
Replaces all missing values in the instance with the values contained in the given array.
replaceSubstring(String, String, String) - Static method in class weka.core.Utils
Replaces with a new string, all occurrences of a string from another string.
reportFrequencyTipText() - Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
resample(Random) - Method in class weka.core.Instances
Creates a new dataset of the same size using random sampling with replacement.
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() - 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.NeuralNetwork.NeuralEnd
Call this to reset the value and error for this unit, ready for the next run.
reset() - Method in class weka.classifiers.functions.neural.NeuralNode
Call this to reset the value and error for this unit, ready for the next run.
reset() - Method in class weka.core.converters.ArffLoader
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.CSVLoader
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() - Static method in class weka.gui.beans.BeanConnection
Reset the list of connections
reset(JComponent) - Static method in class weka.gui.beans.BeanInstance
Reset the list of beans
resetAttIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Resets the boolean value in AttIndexes array
resetAttIndexTo(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR.Indexes
Resets the boolean value in AttIndexes array based on another set of Indexes
resetClusterer() - Method in class weka.clusterers.HAC
Reset all values that have been learned
resetClusterer() - Method in class weka.clusterers.MPCKMeans
Reset all values that have been learned
resetClusterer() - Method in class weka.clusterers.PCKMeans
Reset all values that have been learned
resetClusterer() - Method in class weka.clusterers.PCSoftKMeans
Reset all values that have been learned
resetClusterer() - Method in class weka.clusterers.SeededKMeans
Reset all values that have been learned
resetClusterer() - Method in interface weka.clusterers.SemiSupClusterer
Reset all values that have been learned
resetConsumed() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
resetDatasetBasedOn(LBR.Indexes) - Method in class weka.classifiers.lazy.LBR.Indexes
Resets the boolean values in Attribute and Instance array to reflect an empty dataset withthe same attributes set as in the incoming Indexes Object
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Sets distribution associated with model.
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.C45Split
Sets distribution associated with model.
resetDistribution(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Sets distribution associated with model.
resetHistory() - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Clears any instances from the history queue.
resetID() - Static method in class weka.classifiers.trees.j48.ClassifierTree
Resets the unique node ID counter (e.g.
resetInstanceIndex(boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Resets the boolean value in the Instance Indexes array to a specified value
resetMetric() - Method in class weka.core.metrics.BarHillelMetric
Reset all values that have been learned
resetMetric() - Method in class weka.core.metrics.BarHillelMetricMatlab
Reset all values that have been learned
resetMetric() - Method in class weka.core.metrics.KL
Reset all values that have been learned
resetMetric() - Method in class weka.core.metrics.LearnableMetric
Reset all values that have been learned
resetMetric() - Method in class weka.core.metrics.WeightedDotP
Reset all values that have been learned
resetMetric() - Method in class weka.core.metrics.WeightedEuclidean
Reset all values that have been learned
resetMetric() - Method in class weka.core.metrics.WeightedMahalanobis
Reset all values that have been learned
resetMetric() - Method in class weka.core.metrics.XingMetric
Reset all values that have been learned
resetObjective() - Method in class weka.clusterers.MPCKMeans
reset the value of the objective function and all of its components
resetOccurrences() - Method in class weka.deduping.metrics.AffineProbMetric
reset the number of occurrences of all ops in the set
resetOptions() - Method in class weka.associations.Apriori
Resets the options to the default values.
resetOptions() - Method in class weka.attributeSelection.BestFirst
Reset options to default values
resetOptions() - Method in class weka.attributeSelection.CfsSubsetEval
 
resetOptions() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.ClassifierSubsetEval
reset to defaults
resetOptions() - Method in class weka.attributeSelection.GainRatioAttributeEval
reset options to default values
resetOptions() - Method in class weka.attributeSelection.InfoGainAttributeEval
Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.OneRAttributeEval
rests to defaults.
resetOptions() - Method in class weka.attributeSelection.RaceSearch
Reset the search method.
resetOptions() - Method in class weka.attributeSelection.RankSearch
Reset the search method.
resetOptions() - Method in class weka.attributeSelection.Ranker
Resets stuff to default values
resetOptions() - Method in class weka.attributeSelection.ReliefFAttributeEval
Reset options to their default values
resetOptions() - Method in class weka.attributeSelection.SVMAttributeEval
Resets options to defaults.
resetOptions() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
set options to default values
resetOptions() - Method in class weka.attributeSelection.WrapperSubsetEval
 
resetOptions() - Method in class weka.classifiers.bayes.NaiveBayesSimple
Reset to default options
resetOptions() - Method in class weka.classifiers.bayes.SemiSupEM
Reset to default options
resetOptions() - Method in class weka.classifiers.rules.DecisionTable
Resets the options.
resetOptions() - Method in class weka.clusterers.EM
Reset to default options
resetOptions() - Method in class weka.filters.supervised.attribute.AttributeSelection
set options to their default values
resetQueue() - Method in class weka.filters.Filter
Clears the output queue.
resetStatistics() - Method in class weka.deduping.BasicDeduper
Reset the current statistics
resetTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
resultProducerTipText() - Method in class weka.experiment.AveragingResultProducer
Returns the tip text for this property
resultProducerTipText() - Method in class weka.experiment.DatabaseResultProducer
Returns the tip text for this property
resultProducerTipText() - Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
resultsetKey() - Method in class weka.experiment.PairedTTester
Creates a key that maps resultset numbers to their descriptions.
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
returnLeaves(FastVector[]) - Method in class weka.classifiers.trees.m5.RuleNode
Return a list containing all the leaves in the tree
rev() - Method in class weka.classifiers.functions.pace.DoubleVector
Returns the reverse vector
reverseCopy(Set) - Method in class weka.core.metrics.HardPairwiseSelector
Given a set, return a TreeSet whose items are accessed in descending order
reverseCopy(Set) - Method in class weka.deduping.PairwiseSelector
Given a set, return a TreeSet whose items are accessed in descending order
rightNode() - Method in class weka.classifiers.trees.m5.RuleNode
Get the right child of this node
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split
Prints the condition satisfied by instances in a subset.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Prints left side of condition satisfied by instances in subset index.
rightSide(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit
Does nothing because no condition has to be satisfied.
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.
rootMeanPriorSquaredError() - Method in class weka.classifiers.EnsembleEvaluation
Returns the root mean prior squared error.
rootMeanPriorSquaredError() - Method in class weka.classifiers.Evaluation
Returns the root mean prior squared error.
rootMeanSquaredError() - Method in class weka.classifiers.EnsembleEvaluation
Returns the root mean squared error.
rootMeanSquaredError() - Method in class weka.classifiers.Evaluation
Returns the root mean squared error.
rootMeanSquaredError() - Method in class weka.classifiers.trees.m5.RuleNode
Get the root mean squared error at this node
rootRelativeSquaredError() - Method in class weka.classifiers.EnsembleEvaluation
Returns the root relative squared error if the class is numeric.
rootRelativeSquaredError() - Method in class weka.classifiers.Evaluation
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
rulesetForOneClass(double, Instances, double, double) - Method in class weka.classifiers.rules.JRip
Build a ruleset for the given class according to the given data
runBasicTest(boolean, boolean, boolean, int, boolean, boolean, int, int, int, FastVector) - Method in class weka.classifiers.CheckClassifier
Runs a text on the datasets with the given characteristics.
runCommand(String) - Method in class weka.gui.SimpleCLI
Executes a simple cli command.
runEM() - Method in class weka.clusterers.PCSoftKMeans
Actual KMeans function
runEngine() - Method in class weka.clusterers.assigners.LPAssigner
Run octave in command line with a given argument
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
runKMeans() - Method in class weka.clusterers.MPCKMeans
Actual KMeans function
runKMeans() - Method in class weka.clusterers.PCKMeans
Actual KMeans function
runMatlab(String, String) - Static method in class weka.attributeSelection.MatlabICA
Run matlab in command line with a given argument
runMatlab(String) - Static method in class weka.attributeSelection.MatlabNMF
Run matlab in command line with a given argument
runMatlab(String, String) - Method in class weka.attributeSelection.MatlabPCA
Run matlab in command line with a given argument
runMatlab(String, String) - Static method in class weka.core.metrics.BarHillelMetricMatlab
Run matlab in command line with a given argument
runMatlab(String, String) - Method in class weka.core.metrics.MatlabMetricLearner
Run matlab in command line with a given argument
runOctave(String, String) - Static method in class weka.core.metrics.BarHillelMetric
Run octave in command line with a given argument
runOctave(String, String) - Static method in class weka.core.metrics.XingMetric
Run octave in command line with a given argument

S

SEARCHPATH_ALL - Static variable in class weka.classifiers.trees.adtree.ADTree
The search modes
SEARCHPATH_HEAVIEST - Static variable in class weka.classifiers.trees.adtree.ADTree
 
SEARCHPATH_RANDOM - Static variable in class weka.classifiers.trees.adtree.ADTree
 
SEARCHPATH_ZPURE - Static variable in class weka.classifiers.trees.adtree.ADTree
 
SEEDING_CONSTRAINED - Static variable in class weka.clusterers.SeededKMeans
 
SEEDING_SEEDED - Static variable in class weka.clusterers.SeededKMeans
 
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
 
SEND_INSTANCES - Static variable in class weka.gui.treevisualizer.TreeDisplayEvent
Command to remove instances from this node and send them to the VisualizePanel.
SFEntropyGain() - Method in class weka.classifiers.EnsembleEvaluation
Returns the total SF, which is the null model entropy minus the scheme entropy.
SFEntropyGain() - Method in class weka.classifiers.Evaluation
Returns the total SF, which is the null model entropy minus the scheme entropy.
SFMeanEntropyGain() - Method in class weka.classifiers.EnsembleEvaluation
Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanEntropyGain() - Method in class weka.classifiers.Evaluation
Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
SFMeanPriorEntropy() - Method in class weka.classifiers.EnsembleEvaluation
Returns the entropy per instance for the null model
SFMeanPriorEntropy() - Method in class weka.classifiers.Evaluation
Returns the entropy per instance for the null model
SFMeanSchemeEntropy() - Method in class weka.classifiers.EnsembleEvaluation
Returns the entropy per instance for the scheme
SFMeanSchemeEntropy() - Method in class weka.classifiers.Evaluation
Returns the entropy per instance for the scheme
SFPriorEntropy() - Method in class weka.classifiers.EnsembleEvaluation
Returns the total entropy for the null model
SFPriorEntropy() - Method in class weka.classifiers.Evaluation
Returns the total entropy for the null model
SFSchemeEntropy() - Method in class weka.classifiers.EnsembleEvaluation
Returns the total entropy for the scheme
SFSchemeEntropy() - Method in class weka.classifiers.Evaluation
Returns the total entropy for the scheme
SIGNLOWER - Static variable in class weka.classifiers.lazy.LBR
 
SINE - Static variable in class weka.datagenerators.BIRCHCluster
 
SINGLE_LINK - Static variable in class weka.clusterers.HAC
cluster similarity type
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
 
SMOOTHING_DIRICHLET - Static variable in class weka.core.metrics.KL
 
SMOOTHING_JELINEK_MERCER - Static variable in class weka.core.metrics.KL
 
SMOOTHING_UNSMOOTHED - Static variable in class weka.core.metrics.KL
Different smoothing methods for obtaining probability distributions from frequencies
SOME_OTHER_FAILURE - Static variable in class weka.experiment.RemoteExperiment
 
SOUTH_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
 
SQRTH - Static variable in class weka.core.Statistics
 
SQTPI - Static variable in class weka.core.Statistics
 
STD_DEV - Static variable in class weka.experiment.Grapher
errorBar value for error bars using standard deviations
STD_DEV - Static variable in class weka.experiment.NoiseGrapher
errorBar value for error bars using standard deviations
STEP_FIELD_NAME - Static variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.ActiveLearningCurveCVResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.LearningCurveCrossValidationResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.LearningRateResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.SemiSupCrossValidationResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
STEP_FIELD_NAME - Static variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
STRING - Static variable in class weka.core.Attribute
Constant set for attributes with string values.
STRING_PAIRS_EASIEST - Static variable in class weka.deduping.PairwiseSelector
 
STRING_PAIRS_HARDEST - Static variable in class weka.deduping.PairwiseSelector
 
STRING_PAIRS_RANDOM - Static variable in class weka.deduping.PairwiseSelector
String pair selection method
SVMAttributeEval - class weka.attributeSelection.SVMAttributeEval.
Class for Evaluating attributes individually by using the SVM classifier.
SVMAttributeEval() - Constructor for class weka.attributeSelection.SVMAttributeEval
Constructor
SVM_MODE_CLASSIFICATION - Static variable in class weka.classifiers.sparse.SVMlight
SVM-light can work in classification, regression and preference ranking modes
SVM_MODE_PREFERENCE_RANKING - Static variable in class weka.classifiers.sparse.SVMlight
 
SVM_MODE_REGRESSION - Static variable in class weka.classifiers.sparse.SVMlight
 
SVMlight - class weka.classifiers.sparse.SVMlight.
A wrapper for SVMlight package by Thorsten Joachims For more information, see
SVMlight() - Constructor for class weka.classifiers.sparse.SVMlight
A default constructor
SaveBuffer - class weka.gui.SaveBuffer.
This class handles the saving of StringBuffers to files.
SaveBuffer(Logger, Component) - Constructor for class weka.gui.SaveBuffer
Constructor
Scoreable - interface weka.classifiers.bayes.Scoreable.
Interface for allowing to score a classifier
SeededKMeans - class weka.clusterers.SeededKMeans.
Seeded k means clustering class.
SeededKMeans() - Constructor for class weka.clusterers.SeededKMeans
 
SeededKMeans(Metric) - Constructor for class weka.clusterers.SeededKMeans
 
Seeder - class weka.clusterers.Seeder.
 
Seeder(HashMap) - Constructor for class weka.clusterers.Seeder
 
Seeder(Instances, Instances) - Constructor for class weka.clusterers.Seeder
Constructor
SelectAttributes(ASEvaluation, String[]) - Static method in class weka.attributeSelection.AttributeSelection
Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.
SelectAttributes(Instances) - Method in class weka.attributeSelection.AttributeSelection
Perform attribute selection on the supplied training instances.
SelectAttributes(ASEvaluation, String[], Instances) - Static method in class weka.attributeSelection.AttributeSelection
Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
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
 
SemiSupClassifier - interface weka.classifiers.SemiSupClassifier.
Interface to a classifier that can exploit unlabeled data.
SemiSupClassifierSplitEvaluator - class weka.experiment.SemiSupClassifierSplitEvaluator.
A SplitEvaluator that produces results for a semi-supervised classification scheme on a nominal class attribute.
SemiSupClassifierSplitEvaluator() - Constructor for class weka.experiment.SemiSupClassifierSplitEvaluator
 
SemiSupClusterer - interface weka.clusterers.SemiSupClusterer.
 
SemiSupClustererEvaluation - class weka.clusterers.SemiSupClustererEvaluation.
Class for evaluating clustering models - extends ClusterEvaluation.java
SemiSupClustererEvaluation(Instances, int, int) - Constructor for class weka.clusterers.SemiSupClustererEvaluation
 
SemiSupClustererEvaluation(ArrayList, Instances, int, int) - Constructor for class weka.clusterers.SemiSupClustererEvaluation
 
SemiSupClustererSplitEvaluator - class weka.experiment.SemiSupClustererSplitEvaluator.
A SplitEvaluator that produces results for a semi-supervised clustering scheme on a nominal class attribute.
SemiSupClustererSplitEvaluator() - Constructor for class weka.experiment.SemiSupClustererSplitEvaluator
No args constructor.
SemiSupCrossValidationResultProducer - class weka.experiment.SemiSupCrossValidationResultProducer.
Does a N-fold cross-validation for semi-supervised learning schemes.
SemiSupCrossValidationResultProducer() - Constructor for class weka.experiment.SemiSupCrossValidationResultProducer
 
SemiSupDecorate - class weka.classifiers.meta.SemiSupDecorate.
Class for creating Semi-Supervised Diverse Ensembles of a Classifier Valid options are:
SemiSupDecorate() - Constructor for class weka.classifiers.meta.SemiSupDecorate
 
SemiSupEM - class weka.classifiers.bayes.SemiSupEM.
Semi supervised learner that uses EM initialized with labeled data and then runs EM iterations on the unlabeled data to improve the model.
SemiSupEM() - Constructor for class weka.classifiers.bayes.SemiSupEM
Simple constructor, must set options using command line or GUI
SemiSupIncompleteLabelCurveCVResultProducer - class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer.
N-fold cross-validation learning curve for semi-supervised learners (clusterers and classifiers), where labeled data is not present for all the categories
SemiSupIncompleteLabelCurveCVResultProducer() - Constructor for class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
SemiSupLearningCurveCVResultProducer - class weka.experiment.SemiSupLearningCurveCVResultProducer.
N-fold cross-validation learning curve for semi-supervised learners (clusterers and classifiers)
SemiSupLearningCurveCVResultProducer() - Constructor for class weka.experiment.SemiSupLearningCurveCVResultProducer
 
SemiSupPairActiveCurveCVResultProducer - class weka.experiment.SemiSupPairActiveCurveCVResultProducer.
N-fold cross-validation learning curve for pairwise active learning in semi-supervised learners (clusterers and classifiers)
SemiSupPairActiveCurveCVResultProducer() - Constructor for class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
SemiSupPointActiveCurveCVResultProducer - class weka.experiment.SemiSupPointActiveCurveCVResultProducer.
N-fold cross-validation learning curve for point-wise active learning in semi-supervised learners (clusterers and classifiers)
SemiSupPointActiveCurveCVResultProducer() - Constructor for class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
SemiSupSplitEvaluator - interface weka.experiment.SemiSupSplitEvaluator.
Interface to a split evaluator that can exploit unlabeled data.
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.
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.
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(Experiment) - Constructor for class weka.gui.experiment.SetupPanel
Creates the setup panel with the supplied initial experiment.
SetupPanel() - Constructor for class weka.gui.experiment.SetupPanel
Creates the setup panel with no initial experiment.
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
 
SimpleAssigner - class weka.clusterers.assigners.SimpleAssigner.
 
SimpleAssigner() - Constructor for class weka.clusterers.assigners.SimpleAssigner
Default constructors
SimpleAssigner(MPCKMeans) - Constructor for class weka.clusterers.assigners.SimpleAssigner
Initialize with a clusterer
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
 
SimpleSetupPanel - class weka.gui.experiment.SimpleSetupPanel.
This panel controls the configuration of an experiment.
SimpleSetupPanel(Experiment) - Constructor for class weka.gui.experiment.SimpleSetupPanel
Creates the setup panel with the supplied initial experiment.
SimpleSetupPanel() - Constructor for class weka.gui.experiment.SimpleSetupPanel
Creates the setup panel with no initial experiment.
SoftClassifiedFullInstance - class weka.core.SoftClassifiedFullInstance.
An Instance that has a probability distribution across class values.
SoftClassifiedFullInstance(Instance, Random) - Constructor for class weka.core.SoftClassifiedFullInstance
Constructor that copies the attribute values and the weight from the given instance and gives SoftInstance random class probabilities generated by the given randomizer.
SoftClassifiedFullInstance(Instance) - Constructor for class weka.core.SoftClassifiedFullInstance
Constructor that copies the attribute values and the weight from the given instance and gives SoftInstance random class probabilities that assign all probability (1) to the instance's given class
SoftClassifiedFullInstance() - Constructor for class weka.core.SoftClassifiedFullInstance
 
SoftClassifiedInstance - interface weka.core.SoftClassifiedInstance.
An Instance that has a probability distribution across class values.
SoftClassifiedInstances - class weka.core.SoftClassifiedInstances.
An set of Instances that has a probability distribution across class values.
SoftClassifiedInstances(Instances, Random) - Constructor for class weka.core.SoftClassifiedInstances
Create a set of SoftClassifiedInstances from a given set of Instances but with random class probabilities.
SoftClassifiedInstances(Instances) - Constructor for class weka.core.SoftClassifiedInstances
Create a set of SoftClassifiedInstances from a given set of Instances with hard class probabilities using existing class values.
SoftClassifiedSparseInstance - class weka.core.SoftClassifiedSparseInstance.
An Instance that has a probability distribution across class values.
SoftClassifiedSparseInstance(SparseInstance, Random) - Constructor for class weka.core.SoftClassifiedSparseInstance
Constructor that copies the attribute values and the weight from the given instance and gives SoftInstance random class probabilities generated by the given randomizer.
SoftClassifiedSparseInstance(SparseInstance) - Constructor for class weka.core.SoftClassifiedSparseInstance
Constructor that copies the attribute values and the weight from the given instance and gives SoftInstance random class probabilities that assign all probability (1) to the instance's given class
SoftClassifiedSparseInstance() - Constructor for class weka.core.SoftClassifiedSparseInstance
 
SoftClassifier - interface weka.classifiers.SoftClassifier.
Interface to a classifier that supports soft classified training data that are SoftClassifiedInstances that have probabilistic class labels
SortedAssigner - class weka.clusterers.assigners.SortedAssigner.
 
SortedAssigner() - Constructor for class weka.clusterers.assigners.SortedAssigner
 
Sourcable - interface weka.classifiers.Sourcable.
Interface for classifiers that can be converted to Java source.
SparseInstance - class weka.core.SparseInstance.
Class for storing an instance as a sparse vector.
SparseInstance() - Constructor for class weka.core.SparseInstance
 
SparseInstance(Instance) - Constructor for class weka.core.SparseInstance
Constructor that generates a sparse instance from the given instance.
SparseInstance(SparseInstance) - Constructor for class weka.core.SparseInstance
Constructor that copies the info from the given instance.
SparseInstance(double, double[]) - Constructor for class weka.core.SparseInstance
Constructor that generates a sparse instance from the given parameters.
SparseInstance(double, double[], int[], int) - Constructor for class weka.core.SparseInstance
Constructor that inititalizes instance variable with given values.
SparseInstance(int) - Constructor for class weka.core.SparseInstance
Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.
SparseToNonSparse - class 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
 
SpecialFunctions - class weka.core.SpecialFunctions.
Class implementing some mathematical functions.
SpecialFunctions() - Constructor for class weka.core.SpecialFunctions
 
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
 
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.
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
 
SpreadSubsample - class weka.filters.supervised.instance.SpreadSubsample.
Produces a random subsample of a dataset.
SpreadSubsample() - Constructor for class weka.filters.supervised.instance.SpreadSubsample
 
Stacking - class weka.classifiers.meta.Stacking.
Implements stacking.
Stacking() - Constructor for class weka.classifiers.meta.Stacking
 
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
 
StartSetHandler - interface weka.attributeSelection.StartSetHandler.
Interface for search methods capable of doing something sensible given a starting set of attributes.
Statistics - class weka.core.Statistics.
Class implementing some distributions, tests, etc.
Statistics() - Constructor for class weka.core.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.classifiers.trees.j48.Stats
 
Stats - class weka.experiment.Stats.
A class to store simple statistics
Stats() - Constructor for class weka.experiment.Stats
 
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
 
StreamableFilter - interface weka.filters.StreamableFilter.
Interface for filters can work with a stream of instances.
StringMetric - class weka.deduping.metrics.StringMetric.
An abstract class that returns a measure of similarity between strings
StringMetric() - Constructor for class weka.deduping.metrics.StringMetric
 
StringPair - class weka.deduping.StringPair.
This is a basic class for a training pair
StringPair(String, String, boolean, double) - Constructor for class weka.deduping.StringPair
 
StringReference - class weka.deduping.metrics.StringReference.
A simple data structure for storing a reference to a document file that includes information on the length of its document vector.
StringReference(String, HashMapVector, double) - Constructor for class weka.deduping.metrics.StringReference
 
StringReference(String, HashMapVector) - Constructor for class weka.deduping.metrics.StringReference
Create a reference to this document, initializing its length to 0
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.
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
 
SubsetEvaluator - class weka.attributeSelection.SubsetEvaluator.
Abstract attribute subset evaluator.
SubsetEvaluator() - Constructor for class weka.attributeSelection.SubsetEvaluator
 
SumInstanceMetric - class weka.deduping.metrics.SumInstanceMetric.
SumInstanceMetric class simply adds values returned by StringMetrics on individual fields
SumInstanceMetric() - Constructor for class weka.deduping.metrics.SumInstanceMetric
A default constructor
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.
SupervisedFilter - interface weka.filters.SupervisedFilter.
Interface for filters that make use of a class attribute.
SwapValues - class weka.filters.unsupervised.attribute.SwapValues.
Swaps two values of a nominal attribute.
SwapValues() - Constructor for class weka.filters.unsupervised.attribute.SwapValues
 
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
SysErrLog - class weka.gui.SysErrLog.
This Logger just sends messages to System.err.
SysErrLog() - Constructor for class weka.gui.SysErrLog
 
sampleSizeTipText() - Method in class weka.attributeSelection.ReliefFAttributeEval
Returns the tip text for this property
sampleUnlabeledData(int, Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
Sample the pool of unlabeled data with replacement.
save(StringBuffer) - Method in class weka.gui.SaveBuffer
Save a buffer
saveBuffer() - Method in class weka.gui.experiment.ResultsPanel
Save the currently selected result buffer to a file.
saveBuffer(String) - Method in class weka.gui.explorer.AssociationsPanel
Save the currently selected associator output to a file.
saveBuffer(String) - Method in class weka.gui.explorer.AttributeSelectionPanel
Save the named buffer to a file.
saveBuffer(String) - Method in class weka.gui.explorer.ClassifierPanel
Save the currently selected classifier output to a file.
saveBuffer(String) - Method in class weka.gui.explorer.ClustererPanel
Save the currently selected clusterer output to a file.
saveClassifier(String, Classifier, Instances) - Method in class weka.gui.explorer.ClassifierPanel
Saves the currently selected classifier
saveClusterer(String, Clusterer, Instances, int[]) - Method in class weka.gui.explorer.ClustererPanel
Saves the currently selected clusterer
saveInstanceDataTipText() - Method in class weka.classifiers.trees.adtree.ADTree
 
saveInstanceDataTipText() - Method in class weka.clusterers.Cobweb
Returns the tip text for this property
saveInstancesToFile(File, Instances) - Method in class weka.gui.explorer.PreprocessPanel
Saves the current instances to the supplied file.
saveObject(Object) - Method in class weka.gui.GenericObjectEditor.GOEPanel
Opens an object from a file selected by the user.
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.
scalarMultiply(double) - Method in class weka.clusterers.AlgVector
Computes the scalar product of this vector with a scalar
scalarMultiply(double) - Method in class weka.core.AlgVector
Computes the scalar product of this vector with a scalar
schemeMap - Variable in class weka.experiment.Grapher
Map from scheme + options name to result data in the form of an array of Stats's, one for each learning curve point in points
schemeMap - Variable in class weka.experiment.NoiseGrapher
Map from scheme + options name to result data in the form of an array of Stats's, one for each learning curve point in points
scoreTypeTipText() - Method in class weka.classifiers.bayes.BayesNet
 
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ASSearch
Searches the attribute subset/ranking space.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.BestFirst
Searches the attribute subset space by best first search
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ExhaustiveSearch
Searches the attribute subset space using an exhaustive search.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.ForwardSelection
Searches the attribute subset space by forward selection.
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.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 using a genetic algorithm.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.RankSearch
Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.
search(ASEvaluation, Instances) - Method in class weka.attributeSelection.Ranker
Kind of a dummy search algorithm.
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.
searchPathTipText() - Method in class weka.classifiers.trees.adtree.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.classifiers.meta.AttributeSelectedClassifier
Returns the tip text for this property
second - Variable in class weka.clusterers.InstancePair
second instance index, always <= first
second - Variable in class weka.core.Pair
 
secondInstanceProduced(InstanceEvent) - Method in class weka.gui.streams.InstanceJoiner
 
secondInstanceProduced(InstanceEvent) - Method in interface weka.gui.streams.SerialInstanceListener
 
seedClusterer(HashMap) - Method in class weka.clusterers.HAC
Read the seeds from a hastable, where every key is an instance and every value is: a FastVector of Doubles: [(Double) probInCluster0 ...
seedClusterer(HashMap) - Method in class weka.clusterers.MPCKMeans
Read the seeds from a hastable, where every key is an instance and every value is: the cluster assignment of that instance seedVector vector containing seeds
seedClusterer(HashMap) - Method in class weka.clusterers.PCKMeans
Read the seeds from a hastable, where every key is an instance and every value is: the cluster assignment of that instance seedVector vector containing seeds
seedClusterer(HashMap) - Method in class weka.clusterers.PCSoftKMeans
Read the seeds from a hastable, where every key is an instance and every value is: the cluster assignment of that instance seedVector vector containing seeds
seedClusterer(HashMap) - Method in class weka.clusterers.SeededKMeans
Read the seeds from a hastable, where every key is an instance and every value is: the cluster assignment of that instance seedVector vector containing seeds
seedClusterer(HashMap) - Method in interface weka.clusterers.SemiSupClusterer
Seed the clusterer using specified seeding
seedTipText() - Method in class weka.attributeSelection.GeneticSearch
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.WrapperSubsetEval
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.bayes.SemiSupEM
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.meta.CostSensitiveClassifier
 
seedTipText() - Method in class weka.classifiers.meta.Decorate
Returns the tip text for this property
seedTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
seedTipText() - Method in class weka.classifiers.meta.ThresholdSelector
 
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.clusterers.SimpleKMeans
Returns the tip text for this property
seedTipText() - Method in class weka.clusterers.XMeans
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
seedUnseenClassesTipText() - Method in class weka.classifiers.bayes.SemiSupEM
 
seedable() - Method in class weka.clusterers.MPCKMeans
We can have clusterers that don't utilize seeding
seedable() - Method in class weka.clusterers.PCKMeans
We can have clusterers that don't utilize seeding
seedable() - Method in class weka.clusterers.PCSoftKMeans
We can have clusterers that don't utilize seeding
seedable() - Method in class weka.clusterers.SeededKMeans
We can have clusterers that don't utilize seeding
selectAttributesCVSplit(Instances) - Method in class weka.attributeSelection.AttributeSelection
Select attributes for a split of the data.
selectIndexProbabilistically(double[]) - Method in class weka.classifiers.meta.ActiveDecorate
Given cumulative probabilities select a nominal attribute value index
selectIndexProbabilistically(double[]) - Method in class weka.classifiers.meta.Crate
Given cumulative probabilities select a nominal attribute value index
selectIndexProbabilistically(double[]) - Method in class weka.classifiers.meta.Decorate
Given cumulative probabilities select a nominal attribute value index
selectIndexProbabilistically(double[]) - Method in class weka.classifiers.meta.Fable
Given cumulative probabilities select a nominal attribute value index
selectInstances(Instances, int) - Method in interface weka.classifiers.ActiveLearner
Given a set of unlabeled examples, select a specified number of examples to be labeled.
selectInstances(Instances, int) - Method in class weka.classifiers.meta.ActiveDecorate
Given a set of unlabeled examples, select a specified number of examples to be labeled.
selectInstances(Instances, int) - Method in class weka.classifiers.meta.Fable
Given a set of unlabeled examples, select a specified number of examples to be labeled.
selectInstances(Instances, int) - Method in class weka.classifiers.meta.QBag
Given a set of unlabeled examples, select a specified number of examples to be labeled.
selectInstances(Instances, int) - Method in class weka.classifiers.meta.QBoost
Given a set of unlabeled examples, select a specified number of examples to be labeled.
selectInstancesForFeatures(Instances, int) - Method in interface weka.classifiers.ActiveFeatureAcquirer
Given a set of unlabeled examples, select a specified number of examples to be labeled.
selectInstancesForFeatures(Instances, int) - Method in class weka.classifiers.meta.Fable
Given a set of unlabeled examples, select a specified number of examples to be labeled.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances) - Method in class weka.classifiers.trees.j48.ModelSelection
Selects a model for the given dataset.
selectModel(Instances, Instances) - Method in class weka.classifiers.trees.j48.ModelSelection
Selects a model for the given train data using the given test data
selectNominalValue(double[]) - Method in class weka.classifiers.meta.DEC
Given cummaltive probabilities select a nominal value index
selectNominalValue(double[]) - Method in class weka.classifiers.meta.SemiSupDecorate
Given cummaltive probabilities select a nominal value index
selectProperty() - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Gets the user to select a property of the current resultproducer.
selectThreshold(double) - Method in class weka.classifiers.meta.DEC
Set threshold for relabeling based on user specified threhsold or on error of current committee
selectThreshold(double) - Method in class weka.classifiers.meta.SemiSupDecorate
Set threshold for relabeling based on user specified threhsold or on error of current committee
selectWeightQuantile(Instances, double) - Method in class weka.classifiers.meta.AdaBoostM1
Select only instances with weights that contribute to the specified quantile of the weight distribution
selectWeightQuantile(Instances, double) - Method in class weka.classifiers.meta.LogitBoost
Select only instances with weights that contribute to the specified quantile of the weight distribution
selectWeightQuantile(Instances, double) - Method in class weka.classifiers.meta.MultiBoostAB
Select only instances with weights that contribute to the specified quantile of the weight distribution
selectWeightQuantile(Instances, double) - Method in class weka.classifiers.meta.QBoost
Select only instances with weights that contribute to the specified quantile of the weight distribution
selectedAttributes() - Method in class weka.attributeSelection.AttributeSelection
get the final selected set of attributes.
selectionThresholdTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
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.MixtureDistribution
Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
separable(DoubleVector, int, int, double) - Method in class weka.classifiers.functions.pace.NormalMixture
Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1
separatingThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
separatingThreshold - Variable in class weka.classifiers.functions.pace.NormalMixture
 
seq(int, int) - Static method in class weka.classifiers.functions.pace.IntVector
Generates an IntVector that stores all integers inclusively between two integers.
set(int) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
set a bit in the chromosome
set(int, double) - Method in class weka.classifiers.functions.pace.DoubleVector
Set a single element.
set(double) - Method in class weka.classifiers.functions.pace.DoubleVector
Set all elements to a value
set(int, int, double) - Method in class weka.classifiers.functions.pace.DoubleVector
Set some elements to a value
set(int, int, double[], int) - Method in class weka.classifiers.functions.pace.DoubleVector
Set some elements using a 2-D array
set(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector
Set the elements using a DoubleVector
set(int, int, DoubleVector, int) - Method in class weka.classifiers.functions.pace.DoubleVector
Set some elements using a DoubleVector.
set(int) - Method in class weka.classifiers.functions.pace.IntVector
Sets the value of an element.
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.
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.Matrix
Set a single element.
set(int, int) - Method in class weka.core.DynamicArrayOfPosInt
Stores the value in the specified position in the array.
set(TextSource.Int, TextSource.Real) - Method in class weka.datagenerators.TextSource.DataRow
 
setAblationLevel(double) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
setActive(boolean) - Method in class weka.clusterers.MPCKMeans
set the active level of the clusterer
setActive(boolean) - Method in class weka.clusterers.PCKMeans
set the active level of the clusterer
setAcuity(double) - Method in class weka.clusterers.Cobweb
set the acuity.
setAdditionalMeasures(String[]) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set 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.CrossValidationResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DatabaseResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DeduperSplitEvaluator
Does nothing, since deduping evaluation does not allow additional measures
setAdditionalMeasures(String[]) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.ExtractionResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.ExtractionSplitEvaluator
Does nothing, since extraction evaluation does not allow additional measures
setAdditionalMeasures(String[]) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
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.NoiseCurveCrossValidationResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RandomSplitResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.RegressionSplitEvaluator
Set a list of method names for additional measures to look for in Classifiers.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.ResultProducer
Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Does nothing, since cluster evaluation does not allow additional measures
setAdditionalMeasures(String[]) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set a list of method names for additional measures to look for in SplitEvaluators.
setAdditionalMeasures(String[]) - Method in interface weka.experiment.SplitEvaluator
Sets a list of method names for additional measures to look for in SplitEvaluators.
setAdjustWeights(boolean) - Method in class weka.filters.supervised.instance.SpreadSubsample
Sets whether the instance weights will be adjusted to maintain total weight per class.
setAdvanceDataSetFirst(boolean) - Method in class weka.experiment.Experiment
Set the value of m_AdvanceDataSetFirst.
setAlgorithm(SelectedTag) - Method in class weka.clusterers.MPCKMeans
Set the KMeans algorithm.
setAlgorithm(SelectedTag) - Method in class weka.clusterers.PCKMeans
Set the KMeans algorithm.
setAlgorithm(SelectedTag) - Method in class weka.clusterers.PCSoftKMeans
Set the KMeans algorithm.
setAlgorithm(SelectedTag) - Method in class weka.clusterers.SeededKMeans
Set the KMeans algorithm.
setAllExplore(boolean) - Method in class weka.clusterers.PCKMeans
Set m_AllExplore
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.
setAlpha(double) - Method in class weka.classifiers.meta.Crate
Set the value of Alpha.
setAlpha(double) - Method in class weka.core.metrics.KL
Set the initial value of the smoothing parameter alpha in DITC smoothing
setAlphaDecayRate(double) - Method in class weka.core.metrics.KL
Set the initial value of the smoothing parameter alphaDecayRate in DITC smoothing
setAnimated() - Method in class weka.gui.beans.BeanVisual
Set the animated version of the icon
setArffFile(String) - Method in class weka.gui.streams.InstanceLoader
 
setArffFile(String) - Method in class weka.gui.streams.InstanceSavePanel
 
setArray(int[]) - Method in class weka.classifiers.functions.pace.IntVector
Sets the internal one-dimensional array.
setArtificialSize(double) - Method in class weka.classifiers.meta.ActiveDecorate
Sets factor that determines number of artificial examples to generate.
setArtificialSize(double) - Method in class weka.classifiers.meta.Crate
Sets factor that determines number of artificial examples to generate.
setArtificialSize(double) - Method in class weka.classifiers.meta.Decorate
Sets factor that determines number of artificial examples to generate.
setArtificialSize(double) - Method in class weka.classifiers.meta.Fable
Sets factor that determines number of artificial examples to generate.
setAsText(String) - Method in class weka.gui.CostMatrixEditor
Some objects can be represented as text, but a cost matrix cannot.
setAsText(String) - Method in class weka.gui.GenericArrayEditor
Returns null as we don't support getting/setting values as text.
setAsText(String) - Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting/setting values as text.
setAsText(String) - Method in class weka.gui.SelectedTagEditor
Sets the current property value as text.
setAssigner(MPCKMeansAssigner) - Method in class weka.clusterers.MPCKMeans
 
setAttIndex(int, boolean) - Method in class weka.classifiers.lazy.LBR.Indexes
Changes the boolean value at the specified index in the AttIndexes array
setAttIndexSet(int) - Method in class weka.filters.unsupervised.attribute.AddNoise
Sets the Index of the Attribute to be changed.
setAttList_Irr(boolean[]) - Method in class weka.datagenerators.RDG1
Sets the array that defines which of the attributes are seen to be irrelevant.
setAttrIdxs(int[]) - Method in class weka.core.metrics.Metric
Specifies a list of attributes which will be used by the metric
setAttrIdxs(int, int) - Method in class weka.core.metrics.Metric
Specifies an interval of attributes which will be used by the metric
setAttrIdxs(int[]) - Method in class weka.deduping.metrics.InstanceMetric
Specifies a list of attributes which will be used by the metric
setAttrIdxs(int, int) - Method in class weka.deduping.metrics.InstanceMetric
Specifies an interval of attributes which will be used by the metric
setAttribute(int) - Method in class weka.gui.AttributeSummaryPanel
Sets the attribute that statistics will be displayed for.
setAttribute(int) - Method in class weka.gui.AttributeVisualizationPanel
Tells the panel which attribute to visualize.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RaceSearch
Set the attribute evaluator to use for generating the ranking.
setAttributeEvaluator(ASEvaluation) - Method in class weka.attributeSelection.RankSearch
Set the attribute evaluator to use for generating the ranking.
setAttributeIndex(int) - Method in class weka.filters.unsupervised.attribute.Add
Set the index where the attribute will be inserted
setAttributeIndex(int) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Sets index of of the attribute used.
setAttributeIndex(int) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Sets index of the attribute used.
setAttributeIndex(int) - Method in class weka.filters.unsupervised.attribute.StringToNominal
Sets index of the attribute used.
setAttributeIndex(int) - Method in class weka.filters.unsupervised.attribute.SwapValues
Sets index of the attribute used.
setAttributeIndex(int) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Sets attribute to be used for selection
setAttributeIndices(String) - Method in class weka.filters.supervised.attribute.Discretize
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Copy
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Set which attributes are to be transformed (or kept if invert is true).
setAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.Remove
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.supervised.attribute.Discretize
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Copy
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Set which attributes are to be transformed (or kept if invert is true)
setAttributeIndicesArray(int[]) - Method in class weka.filters.unsupervised.attribute.Remove
Set which attributes are to be deleted (or kept if invert is true)
setAttributeName(String) - Method in class weka.filters.unsupervised.attribute.Add
Set the new attribute's name
setAttributeSelectionMethod(SelectedTag) - Method in class weka.classifiers.functions.LinearRegression
Sets the method used to select attributes for use in the linear regression.
setAttributeSelectionMethod(SelectedTag) - Method in class weka.classifiers.lazy.LWR
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.
setAttributeTypeString(String) - 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
setAutoBounds(boolean) - Method in class weka.classifiers.sparse.SVMlight
Set whether min/max margins are determined automatically
setAutoBuild(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork
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.
setBagSizePercent(int) - Method in class weka.classifiers.meta.QBag
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.
setBaseClassifiers(Classifier[]) - Method in class weka.classifiers.meta.Stacking
Sets the list of possible classifers to choose from.
setBaseExperiment(Experiment) - Method in class weka.experiment.RemoteExperiment
Set the base experiment.
setBeanInstances(Vector, JComponent) - Static method in class weka.gui.beans.BeanInstance
Describe setBeanInstances method here.
setBeta(double) - Method in class weka.classifiers.functions.Winnow
Set the value of Beta.
setBias(double) - Method in class weka.classifiers.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.
setBiased(boolean) - Method in class weka.classifiers.sparse.SVMlight
Set whether the hyperplane is biased (i.e.
setBinPath(String) - Method in class weka.classifiers.sparse.SVMlight
Set the path for the binary files
setBinValue(double) - Method in class weka.clusterers.XMeans
Sets the distance e value between true and false of binary attributes and "same" and "different" of nominal attributes
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Binarize numeric attributes.
setBinarizeNumericAttributes(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval
Binarize numeric attributes.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.supervised.attribute.NominalToBinary
Sets if binary attributes are to be treates as nominal ones.
setBinaryAttributesNominal(boolean) - Method in class weka.filters.unsupervised.attribute.NominalToBinary
Sets if binary attributes are to be treates as nominal ones.
setBinarySplits(boolean) - Method in class weka.classifiers.rules.part.PART
Set the value of binarySplits.
setBinarySplits(boolean) - Method in class weka.classifiers.trees.j48.J48
Set the value of binarySplits.
setBins(int) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets the number of bins to divide each selected numeric attribute into
setBins(int) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Ignored
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
setBlueClassValue(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the class value index for the blue colour
setBounds(Instances) - Method in class weka.classifiers.sparse.SVMlight
Set the bounds using "extreme" training examples - TODO!
setBufferedMode(boolean) - Method in class weka.classifiers.sparse.SVMlight
Set SVM-light to operate via in/out bufffers or via temporary files
setBuildLogisticModels(boolean) - Method in class weka.classifiers.functions.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.sparse.SVMlight
Set the trade-off between training error and margin (default 0 corresponds to [avg.
setC1(double) - Method in class weka.classifiers.sparse.SVMlight
Set parameter c in sigmoid/poly kernel
setCVisible(boolean) - Method in class weka.gui.treevisualizer.Node
Sets all the children of this node either to visible or invisible
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.
setCalculateStdDevs(boolean) - Method in class weka.experiment.AveragingResultProducer
Set the value of CalculateStdDevs.
setCancelButton(boolean) - Method in class weka.gui.GenericObjectEditor.GOEPanel
Enables/disables the cancel button.
setCannotLinkWeight(double) - Method in class weka.clusterers.MPCKMeans
Set the cannot link constraint weight
setCannotLinkWeight(double) - Method in class weka.clusterers.PCKMeans
Set the cannot link constraint weight
setCannotLinkWeight(double) - Method in class weka.clusterers.PCSoftKMeans
Set the cannot link constraint weight
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
setCapacity(int) - Method in class weka.core.FastVector
Sets the vector's capacity to the given value.
setCaseInsensitive(boolean) - Method in class weka.deduping.metrics.Tokenizer
Turn case sensitivity on/off
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.TwoWayNominalSplit
Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode) - Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit
Sets the child for a branch of the split.
setChromosome(BitSet) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
set the chromosome
setCindex(int, double, double) - Method in class weka.gui.visualize.AttributePanel
Set the index of the attribute by which to colour the data.
setCindex(int) - Method in class weka.gui.visualize.AttributePanel
Set the index of the attribute by which to colour the data.
setCindex(int) - Method in class weka.gui.visualize.ClassPanel
Set the index of the attribute to display coloured labels for
setCindex(int) - Method in class weka.gui.visualize.Plot2D
Set the index of the attribute to use for colouring
setCindex(int) - Method in class weka.gui.visualize.PlotData2D
Set the colouring index of the data
setCindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set the index of the attribute to use for colouring
setClampProb(double) - Method in class weka.deduping.metrics.AffineProbMetric
Set the clamping probability value
setClass(Attribute) - Method in class weka.core.Instances
Sets the class attribute.
setClass(TextSource.Real) - Method in class weka.datagenerators.TextSource.DataRow
 
setClassColumn(String) - Method in class weka.gui.beans.ClassAssigner
 
setClassDistribution(double[]) - Method in class weka.core.SoftClassifiedFullInstance
Set the class distribution for this instance
setClassDistribution(double[]) - Method in interface weka.core.SoftClassifiedInstance
Set the class distribution for this instance
setClassDistribution(double[]) - Method in class weka.core.SoftClassifiedSparseInstance
Set the class distribution for this instance
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.
setClassForIRStatistics(int) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Set the value of ClassForIRStatistics.
setClassIndex(int) - Method in class weka.classifiers.BVDecompose
Sets index of attribute to discretize on
setClassIndex(int) - Method in class weka.classifiers.RegressionBVDecompose
Sets index of attribute to discretize on
setClassIndex(int) - Method in class weka.core.Instances
Sets the class index of the set.
setClassIndex(int) - Method in class weka.core.metrics.Metric
Specify which attribute is the class attribute
setClassIndex(int) - Method in class weka.deduping.metrics.InstanceMetric
Specify which attribute is the class attribute
setClassIndex(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the attribute on which misclassifications are based.
setClassMissing() - Method in class weka.core.Instance
Sets the class value of an instance to be "missing".
setClassName(String) - Method in class weka.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
setClassProbability(int, double) - Method in class weka.core.SoftClassifiedFullInstance
Set the probability the instance is in the given class
setClassProbability(int, double) - Method in interface weka.core.SoftClassifiedInstance
Set the probability the instance is in the given class
setClassProbability(int, double) - Method in class weka.core.SoftClassifiedSparseInstance
Set the probability the instance is in the given class
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.
setClassifier(Classifier) - Method in class weka.attributeSelection.ClassifierSubsetEval
Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.attributeSelection.WrapperSubsetEval
Set the classifier to use for accuracy estimation
setClassifier(Classifier) - Method in class weka.classifiers.BVDecompose
Set the classifiers being analysed
setClassifier(Classifier) - Method in class weka.classifiers.CheckClassifier
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.RegressionBVDecompose
Set the classifiers being analysed
setClassifier(SoftClassifier) - Method in class weka.classifiers.bayes.SemiSupEM
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.meta.ActiveDecorate
Set the base classifier for Decorate.
setClassifier(Classifier) - Method in class weka.classifiers.meta.AdaBoostM1
Set the classifier for boosting.
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.Bagging
Set the classifier for bagging.
setClassifier(Classifier) - Method in class weka.classifiers.meta.CVParameterSelection
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.meta.ClassificationViaRegression
Set the base classifier.
setClassifier(Classifier) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Sets the distribution classifier
setClassifier(Classifier) - Method in class weka.classifiers.meta.Crate
Set the base classifier for Crate.
setClassifier(Classifier) - Method in class weka.classifiers.meta.DEC
Set the classifier for bagging.
setClassifier(Classifier) - Method in class weka.classifiers.meta.Decorate
Set the base classifier for Decorate.
setClassifier(Classifier) - Method in class weka.classifiers.meta.DistributionMetaClassifier
Sets the classifier to wrap.
setClassifier(Classifier) - Method in class weka.classifiers.meta.Fable
Set the base classifier for Decorate.
setClassifier(Classifier) - Method in class weka.classifiers.meta.FilteredClassifier
Sets the classifier
setClassifier(Classifier) - Method in class weka.classifiers.meta.LogitBoost
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.meta.MetaCost
Sets the distribution classifier
setClassifier(Classifier) - Method in class weka.classifiers.meta.MultiBoostAB
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.meta.QBag
Set the classifier for bagging.
setClassifier(Classifier) - Method in class weka.classifiers.meta.QBoost
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.meta.RegressionByDiscretization
Set the classifier for boosting.
setClassifier(Classifier) - Method in class weka.classifiers.meta.SemiSupDecorate
Set the classifier for bagging.
setClassifier(Classifier) - Method in class weka.core.metrics.ClassifierMetricLearner
Set the classifier
setClassifier(DistributionClassifier) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Set the classifier
setClassifier(DistributionClassifier) - Method in class weka.deduping.metrics.KernelVSMetric
Set the classifier
setClassifier(Classifier) - Method in class weka.experiment.ClassifierSplitEvaluator
Sets the classifier.
setClassifier(Classifier) - Method in class weka.experiment.RegressionSplitEvaluator
Sets the classifier.
setClassifier(Classifier) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the classifier to classify instances with.
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(DistributionClassifier) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the classifier to use.
setClassifier(Classifier) - Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer
Set a classifier to use
setClassifierName(String) - Method in class weka.experiment.ClassifierSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifierName(String) - Method in class weka.experiment.RegressionSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifiers(Classifier[]) - Method in class weka.classifiers.meta.MultiScheme
Sets the list of possible classifers to choose from.
setClearEachDataset(boolean) - Method in class weka.gui.streams.InstanceViewer
 
setClusterer(Clusterer) - Method in class weka.clusterers.ClusterEvaluation
set the clusterer
setClusterer(Clusterer) - Method in class weka.clusterers.DistributionMetaClusterer
Sets the clusterer to wrap.
setClusterer(MPCKMeans) - Method in class weka.clusterers.assigners.MPCKMeansAssigner
Set the clusterer
setClusterer(Clusterer) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Sets the clusterer.
setClusterer(Clusterer) - Method in class weka.extraction.ClusteringExtractor
Set the clusterer
setClusterer(Clusterer) - Method in class weka.filters.unsupervised.attribute.AddCluster
Sets the clusterer to assign clusters with.
setClustererName(String) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Set the Clusterer to use, given it's class name.
setColor(Color) - Method in class weka.gui.treevisualizer.Node
Set the value of color.
setColourIndex(int) - Method in class weka.gui.visualize.VisualizePanel
Sets the index used for colouring.
setColours(FastVector) - Method in class weka.gui.visualize.AttributePanel
Sets a list of colours to use for colouring data points
setColours(FastVector) - Method in class weka.gui.visualize.ClassPanel
Set a list of colours to use for colouring labels
setColours(FastVector) - Method in class weka.gui.visualize.Plot2D
Set a list of colours to use when colouring points according to class values or cluster numbers
setColours(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set a list of colours to use for plotting 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
setComboSizes() - Method in class weka.gui.experiment.ResultsPanel
Sets the combo-boxes to a fixed size so they don't take up too much room that would be better devoted to the test output box
setComplexityParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval
Set the value of C for SMO
setConcentration(double) - Method in class weka.clusterers.SeededKMeans
Set the concentration
setConfidenceFactor(float) - Method in class weka.classifiers.rules.part.PART
Set the value of CF.
setConfidenceFactor(float) - Method in class weka.classifiers.trees.j48.J48
Set the value of CF.
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
setConnections(Vector) - Static method in class weka.gui.beans.BeanConnection
Describe setConnections method here.
setConsequent(double) - Method in class weka.classifiers.rules.JRip.RipperRule
 
setConstraintWeight(double) - Method in class weka.clusterers.assigners.RMNAssigner
Get/Set m_constraintWeight
setConversionType(SelectedTag) - Method in class weka.core.metrics.BarHillelMetric
Set the type of distance to similarity conversion.
setConversionType(SelectedTag) - Method in class weka.core.metrics.BarHillelMetricMatlab
Set the type of distance to similarity conversion.
setConversionType(SelectedTag) - Method in class weka.core.metrics.KL
Set the type of distance to similarity conversion.
setConversionType(SelectedTag) - Method in class weka.core.metrics.WeightedDotP
Set the type of similarity to distance conversion.
setConversionType(SelectedTag) - Method in class weka.core.metrics.WeightedEuclidean
Set the type of distance to similarity conversion.
setConversionType(SelectedTag) - Method in class weka.core.metrics.WeightedMahalanobis
Set the type of distance to similarity conversion.
setConversionType(SelectedTag) - Method in class weka.core.metrics.XingMetric
Set the type of distance to similarity conversion.
setConversionType(SelectedTag) - Method in class weka.deduping.metrics.AffineProbMetric
Set the type of similarity to distance conversion.
setConversionType(SelectedTag) - Method in class weka.deduping.metrics.JaccardMetric
Set the type of similarity to distance conversion.
setConversionType(SelectedTag) - Method in class weka.deduping.metrics.KernelVSMetric
Set the type of similarity to distance conversion.
setConversionType(SelectedTag) - Method in class weka.deduping.metrics.VectorSpaceMetric
Set the type of similarity to distance conversion.
setCostFactor(double) - Method in class weka.classifiers.sparse.SVMlight
Set cost-factor, by which training errors on positive examples outweight errors on negative examples
setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Sets the misclassification cost matrix.
setCostMatrix(CostMatrix) - Method in class weka.classifiers.meta.MetaCost
Sets the misclassification cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag) - Method in class weka.classifiers.meta.MetaCost
Sets the source location of the cost matrix.
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 weka.classifiers.sparse.IBkMetric
Sets whether hold-one-out cross-validation will be used to select the best k value
setCrossoverProb(double) - Method in class weka.attributeSelection.GeneticSearch
set the probability of crossover
setCurrentInstance(Instance) - Method in class weka.gui.beans.IncrementalClassifierEvent
Set the current instance for this event
setCurrentSize(int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set CurrentSize
setCustomColour(Color) - Method in class weka.gui.visualize.PlotData2D
Set a custom colour to use for this plot.
setCutOffFactor(double) - Method in class weka.clusterers.XMeans
Sets a new cutoff factor.
setCutoff(double) - Method in class weka.clusterers.Cobweb
set the cutoff
setD(int) - Method in class weka.classifiers.sparse.SVMlight
Set parameter d in polynomial kernel
setData(Instances) - Method in class weka.classifiers.rules.RuleStats
Set the data of the stats, overwriting the old one if any
setData() - Method in class weka.experiment.Grapher
Load data for graph in from the given Experiment result file in arff format
setData() - Method in class weka.experiment.NoiseGrapher
Load data for graph in from the given Experiment result file in arff format
setDataCreationMethod(int) - Method in class weka.classifiers.meta.DEC
Sets method to use for creating artificial data
setDataCreationMethod(int) - Method in class weka.classifiers.meta.SemiSupDecorate
Sets method to use for creating artificial data
setDataFileName(String) - Method in class weka.classifiers.BVDecompose
Sets the maximum number of boost iterations
setDataFileName(String) - Method in class weka.classifiers.RegressionBVDecompose
Sets the maximum number of boost iterations
setDataGenerator(DataGenerator) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the data generator to use for generating new instances
setDataPoint(double[]) - Method in class weka.gui.beans.ChartEvent
Set the data point
setDatabaseURL(String) - Method in class weka.experiment.DatabaseUtils
Set the value of DatabaseURL.
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() - Method in class weka.experiment.Grapher
Set array of points on learning curve from Key_Dataset values in data
setDatasets() - Method in class weka.experiment.NoiseGrapher
Set array of points on learning curve from Key_Dataset values in data
setDatasets(DefaultListModel) - Method in class weka.experiment.RemoteExperiment
Set the datasets to use in the experiment
setDebug(boolean) - Method in class weka.attributeSelection.RaceSearch
Set whether verbose output should be generated.
setDebug(boolean) - Method in class weka.classifiers.BVDecompose
Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.CheckClassifier
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.RegressionBVDecompose
Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.bayes.SemiSupEM
Set debug mode - verbose output
setDebug(boolean) - Method in class weka.classifiers.functions.LeastMedSq
sets whether or not debugging output shouild be printed
setDebug(boolean) - Method in class weka.classifiers.functions.LinearRegression
Controls whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.functions.pace.PaceRegression
Controls whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.lazy.IBk
Set the value of Debug.
setDebug(boolean) - Method in class weka.classifiers.lazy.LWR
Sets whether debugging output should be produced
setDebug(boolean) - Method in class weka.classifiers.meta.ActiveDecorate
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.AdaBoostM1
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.CVParameterSelection
Sets debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.Crate
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.Decorate
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.Fable
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.LogitBoost
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.MultiBoostAB
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.MultiScheme
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.QBoost
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set debugging mode
setDebug(boolean) - Method in class weka.classifiers.meta.RegressionByDiscretization
Sets whether debugging output will be printed
setDebug(boolean) - Method in class weka.classifiers.rules.JRip
 
setDebug(boolean) - Method in class weka.classifiers.sparse.IBkMetric
Set the value of Debug.
setDebug(boolean) - Method in class weka.classifiers.sparse.SVMlight
Turn debugging output on/off
setDebug(boolean) - Method in class weka.classifiers.trees.RandomTree
Set the value of Debug.
setDebug(boolean) - Method in class weka.clusterers.EM
Set debug mode - verbose output
setDebug(boolean) - Method in class weka.core.Optimization
Set whether in debug mode
setDebug(boolean) - Method in class weka.datagenerators.ClusterGenerator
Sets the debug flag.
setDebug(boolean) - Method in class weka.datagenerators.Generator
Sets the debug flag.
setDebug(boolean) - Method in class weka.deduping.BasicDeduper
Turn debugging output on/off
setDebug(boolean) - Method in class weka.deduping.PairwiseSelector
Turn debugging output on/off
setDebug(boolean) - Method in class weka.filters.unsupervised.attribute.AddExpression
Set debug mode.
setDebug(boolean) - Method in class weka.gui.streams.InstanceCounter
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceJoiner
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceLoader
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceSavePanel
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceTable
 
setDebug(boolean) - Method in class weka.gui.streams.InstanceViewer
 
setDebugLevel(int) - Method in class weka.clusterers.XMeans
Sets the debug level.
setDebugLevel(int) - Method in class weka.core.KDTree
Sets the debug level.
setDebugVektorsFile(String) - Method in class weka.clusterers.XMeans
Sets a file name for a file that has the random vektors stored.
setDecay(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
setDeduper(Deduper) - Method in class weka.experiment.DeduperSplitEvaluator
Sets the deduper.
setDeduperName(String) - Method in class weka.experiment.DeduperSplitEvaluator
Set the Deduper to use, given it's class name.
setDefaultOptions() - Method in class weka.datagenerators.BIRCHCluster
Sets all options to their default values.
setDefaultPerturb(double) - Method in class weka.clusterers.MPCKMeans
Set default perturbation value
setDefaultPerturb(double) - Method in class weka.clusterers.PCKMeans
Set default perturbation value
setDefaultPerturb(double) - Method in class weka.clusterers.PCSoftKMeans
Set default perturbation value
setDefaultPerturb(double) - Method in class weka.clusterers.SeededKMeans
Set default perturbation value
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.deduping.metrics.WordTokenizer
Specify which delimiters to use
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.
setDerived(int) - Method in class weka.gui.AttributeSummaryPanel
Sets the gui elements for fields that are stored in the AttributeStats structure.
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.ActiveDecorate
Sets the desired size of the committee.
setDesiredSize(int) - Method in class weka.classifiers.meta.Crate
Sets the desired size of the committee.
setDesiredSize(int) - Method in class weka.classifiers.meta.DEC
Sets the desired size of the committee.
setDesiredSize(int) - Method in class weka.classifiers.meta.Decorate
Sets the desired size of the committee.
setDesiredSize(int) - Method in class weka.classifiers.meta.Fable
Sets the desired size of the committee.
setDesiredSize(int) - Method in class weka.classifiers.meta.SemiSupDecorate
Sets the desired size of the committee.
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
setDistMult(double) - Method in class weka.datagenerators.BIRCHCluster
Sets the distance multiplier.
setDistanceF(DistanceFunction) - Method in class weka.clusterers.XMeans
gets the "binary" distance value
setDistanceFunction(DistanceFunction) - Method in class weka.core.KDTree
Sets the distance function.
setDistanceWeighting(SelectedTag) - Method in class weka.classifiers.lazy.IBk
Sets the distance weighting method used.
setDistanceWeighting(SelectedTag) - Method in class weka.classifiers.sparse.IBkMetric
Sets the distance weighting method used.
setDistributionClassifier(DistributionClassifier) - Method in class weka.classifiers.meta.MultiClassClassifier
Set the base classifier.
setDistributionClassifier(DistributionClassifier) - Method in class weka.classifiers.meta.OrdinalClassClassifier
Set the base classifier.
setDistributionClassifier(DistributionClassifier) - Method in class weka.classifiers.meta.ThresholdSelector
Set the DistributionClassifier for which threshold is set.
setDistributionSpread(double) - Method in class weka.filters.supervised.instance.SpreadSubsample
Sets the value for the distribution spread
setDoActive(boolean) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of m_DoActive.
setDoActive(boolean) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the value of m_DoActive.
setDoXval(boolean) - Method in class weka.clusterers.ClusterEvaluation
set whether or not to do cross validation
setElement(int, double) - Method in class weka.clusterers.AlgVector
Sets an element of the matrix to the given value.
setElement(int, double) - Method in class weka.core.AlgVector
Sets an element of the matrix to the given value.
setElement(int, int, double) - Method in class weka.core.Matrix
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.
setElements(double[]) - Method in class weka.clusterers.AlgVector
Sets the elements of the vector to values of the given array.
setElements(double[]) - Method in class weka.core.AlgVector
Sets the elements of the vector to values of the given array.
setEliminateColinearAttributes(boolean) - Method in class weka.classifiers.functions.LinearRegression
Set the value of EliminateColinearAttributes.
setEliminateColinearAttributes(boolean) - Method in class weka.classifiers.lazy.LWR
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.
setEngineType(SelectedTag) - Method in class weka.clusterers.assigners.LPAssigner
Set the engine type
setEntropicAutoBlend(boolean) - Method in class weka.classifiers.lazy.kstar.KStar
Set whether entropic blending is to be used.
setEpsilon(double) - Method in class weka.classifiers.functions.SMO
Set the value of epsilon.
setEpsilon(double) - Method in class weka.core.metrics.GDMetricLearner
Set the convergence criterion
setEpsilonParameter(double) - Method in class weka.attributeSelection.SVMAttributeEval
Set the value of P for SMO
setErrorMeasure(int) - Method in class weka.classifiers.meta.Crate
Set the value of errorMeasure.
setEstimator(SelectedTag) - Method in class weka.classifiers.functions.pace.PaceRegression
Sets the estimator.
setEta(double) - Method in class weka.clusterers.MPCKMeans
Set the initial value of gradient descent eta
setEtaDecayRate(double) - Method in class weka.clusterers.MPCKMeans
Set the initial value of the decay rate of gradient descent eta
setEvaluationMode(SelectedTag) - Method in class weka.classifiers.meta.ThresholdSelector
Sets the evaluation mode used.
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.AttributeSelection
set the attribute/subset evaluator
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.MatlabICA
Sets the attribute evaluator
setEvaluator(ASEvaluation) - Method in class weka.attributeSelection.MatlabNMF
Sets the attribute evaluator
setEvaluator(ASEvaluation) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Sets the attribute evaluator
setEvaluator(ASEvaluation) - Method in class weka.core.metrics.AttrEvalMetricLearner
Set the evaluator
setEvaluator(ASEvaluation) - Method in class weka.filters.supervised.attribute.AttributeSelection
set a string holding the name of a attribute/subset evaluator
setExclusive(boolean) - Method in class weka.classifiers.rules.ConjunctiveRule
 
setExecutionStatus(int) - Method in class weka.experiment.TaskStatusInfo
Set the execution status of this Task.
setExpScalingFactor(double) - Method in class weka.clusterers.assigners.RMNAssigner
Get/Set m_expScalingFactor
setExpectedResultsPerAverage(int) - Method in class weka.experiment.AveragingResultProducer
Set the value of ExpectedResultsPerAverage.
setExperiment(Experiment) - Method in class weka.experiment.RemoteExperimentSubTask
Set the experiment for this sub task
setExperiment(Experiment) - Method in class weka.gui.experiment.AlgorithmListPanel
Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.DatasetListPanel
Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.DistributeExperimentPanel
Sets the experiment to be configured.
setExperiment(Experiment) - Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Sets the experiment which will have the custom properties edited.
setExperiment(RemoteExperiment) - Method in class weka.gui.experiment.HostListPanel
Tells the panel to act on a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.ResultsPanel
Tells the panel to use a new experiment.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunNumberPanel
Sets the experiment to be configured.
setExperiment(Experiment) - Method in class weka.gui.experiment.RunPanel
Sets the experiment the panel operates on.
setExperiment(Experiment) - Method in class weka.gui.experiment.SetupPanel
Sets the experiment to configure.
setExperiment(Experiment) - Method in class weka.gui.experiment.SimpleSetupPanel
Sets the experiment to configure.
setExponent(double) - Method in class weka.classifiers.functions.SMO
Set the value of exponent.
setExponent(double) - Method in class weka.classifiers.functions.VotedPerceptron
Set the value of exponent.
setExpression(String) - Method in class weka.filters.unsupervised.attribute.AddExpression
Set the expression to apply
setExternal(boolean) - Method in class weka.core.metrics.LearnableMetric
Set the value of m_external
setExtraPhase1RunFraction(double) - Method in class weka.clusterers.SeededKMeans
Set the number of extra phase1 runs
setExtractor(Extractor) - Method in class weka.experiment.ExtractionSplitEvaluator
Sets the extractor.
setExtractor(Extractor) - Method in class weka.extraction.ClusteringExtractor
Set the extractor
setExtractorName(String) - Method in class weka.experiment.ExtractionSplitEvaluator
Set the Extractor to use, given it's class name.
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
setFeatureSpaceNormalization(boolean) - Method in class weka.classifiers.functions.SMO
Set whether feature space is normalized.
setFile(File) - Method in class weka.core.converters.ArffLoader
sets the source File
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.classifiers.meta.FilteredClassifier
Sets the filter
setFilter(Filter) - Method in class weka.gui.beans.Filter
Set the filter to be wrapped by this bean
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.
setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
Set the value of FindNumBins.
setFindNumBins(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Set the value of FindNumBins.
setFirstValueIndex(int) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Sets index of the first value used.
setFirstValueIndex(int) - Method in class weka.filters.unsupervised.attribute.SwapValues
Sets index of the first value used.
setFitness(double) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
sets the scaled fitness
setFlags() - Method in class weka.core.Range
Sets the flags array.
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.
setFoldCreationMode(SelectedTag) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Set the mode of creating folds
setFoldCreationMode(SelectedTag) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Set the mode of creating folds
setFolds(int) - Method in class weka.attributeSelection.AttributeSelection
set the number of folds for cross validation
setFolds(int) - Method in class weka.attributeSelection.WrapperSubsetEval
Set the number of folds to use for accuracy estimation
setFolds(int) - Method in class weka.classifiers.rules.ConjunctiveRule
The access functions for parameters
setFolds(int) - Method in class weka.classifiers.rules.JRip
 
setFolds(int) - Method in class weka.classifiers.rules.Ridor
Set and get members for parameters
setFolds(int) - Method in class weka.clusterers.ClusterEvaluation
set the number of folds to use for cross validation
setFolds(int) - Method in class weka.gui.beans.CrossValidationFoldMaker
Set the number of folds for the cross validation
setFoldsType(SelectedTag) - Method in class weka.attributeSelection.RaceSearch
Set the xfold type
setFormat(Instances) - Method in class weka.datagenerators.ClusterGenerator
Sets the format of the dataset that is to be generated.
setFormat(Instances) - Method in class weka.datagenerators.Generator
Sets the format of the dataset that is to be generated.
setFraction(boolean) - Method in class weka.experiment.PairedTTester
Set to true if fractions are specified for learning curves
setFromExpEnabled() - Method in class weka.gui.experiment.ResultsPanel
Updates whether the current experiment is of a type that we can determine the results destination.
setFunctionValue(int, double) - Method in class weka.classifiers.functions.pace.DiscreteFunction
Sets a particular function value
setGUI(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork
This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.
setGamma(double) - Method in class weka.classifiers.functions.SMO
Set the value of gamma.
setGamma(double) - Method in class weka.classifiers.sparse.SVMlight
Set parameter gamma in rbf kernel
setGapExtendCost(double) - Method in class weka.deduping.metrics.AffineMetric
Set the gap extension cost
setGapStartCost(double) - Method in class weka.deduping.metrics.AffineMetric
Set the gap opening cost
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.RaceSearch
Records whether the user has requested a ranked list of attributes.
setGenerateRanking(boolean) - Method in interface weka.attributeSelection.RankedOutputSearch
Sets whether or not ranking is to be performed.
setGenerateRanking(boolean) - Method in class weka.attributeSelection.Ranker
This is a dummy set method---Ranker is ONLY capable of producing a ranked list of attributes for attribute evaluators.
setGenerateRules(boolean) - Method in class weka.classifiers.trees.m5.M5Base
Generate rules (decision list) rather than a tree
setGlobalBlend(int) - Method in class weka.classifiers.lazy.kstar.KStar
Set the global blend parameter
setGreenClassValue(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the class value index for the green colour
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.
setHardVoteAssignment(boolean) - Method in class weka.classifiers.meta.QBag
Set the value of m_HardVoteAssignment.
setHeader(int) - Method in class weka.gui.AttributeSummaryPanel
Sets the labels for fields we can determine just from the instance header.
setHiddenLayers(String) - Method in class weka.classifiers.functions.neural.NeuralNetwork
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
setICAapproach(SelectedTag) - Method in class weka.attributeSelection.MatlabICA
set ICA approach
setICAfunction(SelectedTag) - Method in class weka.attributeSelection.MatlabICA
set ICA function
setIgnoredAttributeIndices(String) - Method in class weka.filters.unsupervised.attribute.AddCluster
Sets the ranges of attributes to be ignored.
setInitAsNaiveBayes(boolean) - Method in class weka.classifiers.bayes.BayesNet
Method declaration
setInputCenterFile(String) - Method in class weka.clusterers.XMeans
Sets the name of the file to read the list of centers from.
setInputFormat(Instances) - Method in class weka.filters.AllFilter
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.Filter
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.NullFilter
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.ClassOrder
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.Discretize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.attribute.NominalToBinary
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.Resample
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.SpreadSubsample
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Add
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddCluster
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddExpression
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.AddNoise
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Copy
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.FirstOrder
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.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.Normalize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericToBinary
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Obfuscate
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Remove
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveType
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.RemoveUseless
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.Standardize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToNominal
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.SwapValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.attribute.UnitVector
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.Randomize
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemovePercentage
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveRange
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.Resample
Sets the format of the input instances.
setInputFormat(Instances) - Method in class weka.filters.unsupervised.instance.SparseToNonSparse
Sets the format of the input instances.
setInputOrder(int) - Method in class weka.datagenerators.BIRCHCluster
Sets the input order.
setInstNums(String) - Method in class weka.datagenerators.BIRCHCluster
Sets the upper and lower boundary for instances per cluster.
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
setInstanceOrdering(SelectedTag) - Method in class weka.clusterers.PCKMeans
Set the instance ordering
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.clusterers.HAC
Sets training instances
setInstances(Instances) - Method in class weka.clusterers.MPCKMeans
Sets training instances
setInstances(Instances) - Method in class weka.clusterers.PCKMeans
Sets training instances
setInstances(Instances) - Method in class weka.clusterers.PCSoftKMeans
Sets training instances
setInstances(Instances) - Method in class weka.clusterers.SeededKMeans
Sets training instances
setInstances(Instances) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
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.CrossValidationResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.DatabaseResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.ExtractionResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.LearningRateResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.PairedTTester
Set the value of Instances.
setInstances(Instances) - Method in class weka.experiment.RandomSplitResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in interface weka.experiment.ResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Sets the dataset that results will be obtained for.
setInstances(Instances) - Method in class weka.gui.AttributeListPanel
Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.AttributeSelectionPanel
Sets the instances who's attribute names will be displayed.
setInstances(Instances) - Method in class weka.gui.AttributeSummaryPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.AttributeVisualizationPanel
Sets the instances for use
setInstances(Instances) - Method in class weka.gui.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.boundaryvisualizer.BoundaryVisualizer
Set the training instances
setInstances(Instances) - Method in class weka.gui.experiment.ResultsPanel
Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.
setInstances(Instances) - Method in class weka.gui.explorer.AssociationsPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.AttributeSelectionPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.ClassifierPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.ClustererPanel
Tells the panel to use a new set of instances.
setInstances(Instances) - Method in class weka.gui.explorer.PreprocessPanel
Tells the panel to use a new base 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.visualize.ClassPanel
Set the instances.
setInstances(Instances) - Method in class weka.gui.visualize.MatrixPanel
This method changes the Instances object of this class to a new one.
setInstances(Instances) - Method in class weka.gui.visualize.Plot2D
Sets the master plot from a set of instances
setInstances(Instances) - Method in class weka.gui.visualize.VisualizePanel
Tells the panel to use a new set of instances.
setInstancesFromDB(InstanceQuery) - Method in class weka.gui.explorer.PreprocessPanel
Loads instances from a database
setInstancesFromDBQ() - 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.
setInstancesFromDBaseQuery() - Method in class weka.gui.experiment.ResultsPanel
Queries the user enough to make a database query to retrieve experiment results.
setInstancesFromDatabaseTable(String) - Method in class weka.gui.experiment.ResultsPanel
Queries a database to load results from the specified table name.
setInstancesFromExp(Experiment) - Method in class weka.gui.experiment.ResultsPanel
Examines the supplied experiment to determine the results destination and attempts to load the results.
setInstancesFromFile(File) - Method in class weka.gui.SetInstancesPanel
Loads results from a set of instances contained in the supplied file.
setInstancesFromFile(File) - Method in class weka.gui.experiment.ResultsPanel
Loads results from a set of instances contained in the supplied file.
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.SetInstancesPanel
Loads instances from a URL.
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.
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.AbstractTimeSeries
Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Copy
Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
Sets whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.NumericTransform
Set whether selected columns should be transformed or not.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.Remove
Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.attribute.RemoveType
Set whether selected columns should be removed or kept.
setInvertSelection(boolean) - Method in class weka.filters.unsupervised.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.
setIsFraction() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Determines if the points specified are fractions of the total number of examples
setIsFraction() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Determines if the points specified are fractions of the total number of examples
setIsFraction() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Determines if the points specified are fractions of the total number of examples
setIsFraction() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Determines if the points specified are fractions of the total number of examples
setIsFraction() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Determines if the points specified are fractions of the total number of examples
setIsFraction() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Determines if the points specified are fractions of the total number of examples
setIsFraction() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Determines if the points specified are fractions of the total number of examples
setIsFraction() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Determines if the points specified are fractions of the total number of examples
setIsRandom(boolean) - Method in class weka.classifiers.functions.LeastMedSq
Sets whether sample selection is random
setIsRandomNeighborhoods(boolean) - Method in class weka.clusterers.MPCKMeans
Set value of m_IsRandomNeighborhoods
setIsTransductive(boolean) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set the value of IsTransductive.
setIsTransductive(boolean) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set the value of IsTransductive.
setIsTransductive(boolean) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of IsTransductive.
setIsTransductive(boolean) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the value of IsTransductive.
setIterations(int) - Method in class weka.attributeSelection.MatlabNMF
Sets the number of iterations for gradient descent.
setJitter(int) - Method in class weka.gui.visualize.Plot2D
Set level of jitter and repaint the plot using the new jitter value
setJitter(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set level of jitter and repaint the plot using the new jitter value
setKDTree(KDTree) - Method in class weka.classifiers.lazy.IBk
Sets the KDTree class.
setKDTree(KDTree) - Method in class weka.clusterers.XMeans
Sets the KDTree class.
setKNN(int) - Method in class weka.classifiers.lazy.IBk
Set the number of neighbours the learner is to use.
setKNN(int) - Method in class weka.classifiers.lazy.LWR
Sets the number of neighbours used for kernel bandwidth setting.
setKNN(int) - Method in class weka.classifiers.sparse.IBkMetric
Set the number of neighbours the learner is to use.
setKValue(int) - Method in class weka.classifiers.trees.RandomTree
Set the value of K.
setKernelType(SelectedTag) - Method in class weka.classifiers.sparse.SVMlight
Set the kernel type for SVM-light
setKeyFieldName(String) - Method in class weka.experiment.AveragingResultProducer
Set the value of KeyFieldName.
setLambda(double) - Method in class weka.classifiers.bayes.SemiSupEM
 
setLambdaJM(double) - Method in class weka.core.metrics.KL
Set the lambda parameter for Jelinek-Mercer smoothing
setLearningCurve(boolean) - Method in class weka.experiment.PairedTTester
Set to true if learning curves are to be analyzed
setLearningRate(double) - Method in class weka.classifiers.functions.neural.NeuralNetwork
The learning rate can be set using this command.
setLearningRate(double) - Method in class weka.core.metrics.GDMetricLearner
Set the learning rate
setLegendText(Vector) - Method in class weka.gui.beans.ChartEvent
Set the legend text vector
setLengthNormalized(boolean) - Method in class weka.core.metrics.WeightedDotP
Set normalization by instance length to be on or off
setLikelihoodThreshold(double) - Method in class weka.classifiers.meta.LogitBoost
Set the value of Precision.
setLink(boolean, int) - Method in class weka.classifiers.functions.neural.NeuralNetwork.NeuralEnd
Call this function to set What this end unit represents.
setLinkingType(SelectedTag) - Method in class weka.clusterers.HAC
Set the type of clustering
setLoadEigenValuesFromFile(boolean) - Method in class weka.attributeSelection.MatlabICA
set m_loadEigenValuesFromFile
setLoadEigenVectorsFromFile(boolean) - Method in class weka.attributeSelection.MatlabICA
set m_loadEigenVectorsFromFile
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
setLog(Logger) - Method in class weka.gui.beans.AbstractEvaluator
Set a logger
setLog(Logger) - Method in class weka.gui.beans.AbstractTestSetProducer
Set a logger
setLog(Logger) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Set a log for this bean
setLog(Logger) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Set a logger
setLog(Logger) - Method in interface weka.gui.beans.BeanCommon
Set a logger
setLog(Logger) - Method in class weka.gui.beans.ClassAssigner
 
setLog(Logger) - Method in class weka.gui.beans.Classifier
Set a logger
setLog(Logger) - Method in class weka.gui.beans.Filter
Set a logger
setLog(Logger) - Method in class weka.gui.beans.StripChart
Set a logger
setLog(Logger) - Method in class weka.gui.explorer.AssociationsPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.AttributeSelectionPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.ClassifierPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.ClustererPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.explorer.PreprocessPanel
Sets the Logger to receive informational messages
setLog(Logger) - Method in class weka.gui.visualize.VisualizePanel
Sets the Logger to receive informational messages
setLogTermWeight(double) - Method in class weka.clusterers.MPCKMeans
Set the value of the weight assigned to log term in the objective function
setLowerBoundMinSupport(double) - Method in class weka.associations.Apriori
Set the value of lowerBoundMinSupport.
setLowerOrderTerms(boolean) - Method in class weka.classifiers.functions.SMO
Set whether lower-order terms are to be used.
setLowerSize(int) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Set the value of LowerSize.
setLowerSize(int) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set the value of LowerSize.
setLowerSize(int) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set the value of LowerSize.
setLowerSize(int) - Method in class weka.experiment.LearningRateResultProducer
Set the value of LowerSize.
setLowerSize(int) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set the value of LowerSize.
setLowerSize(int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set the value of LowerSize.
setLowerSize(int) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set the value of LowerSize.
setLowerSize(int) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of LowerSize.
setLowerSize(int) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the value of LowerSize.
setM(double) - Method in class weka.classifiers.bayes.NaiveBayesSimple
Set Laplace m parameter that controls amouont of smoothing
setM(double) - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Set Laplace m parameter that controls amouont of smoothing
setMDLTheoryWeight(double) - Method in class weka.classifiers.rules.RuleStats
Set the weight of theory in MDL calcualtion
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.Plot2D
Set the master plot.
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Clears all existing plots and sets a new master plot
setMasterPlot(PlotData2D) - Method in class weka.gui.visualize.VisualizePanel
Set the master plot for the visualize panel
setMatchCost(double) - Method in class weka.deduping.metrics.AffineMetric
Set the match cost
setMatchMissingValues(boolean) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Sets whether missing values are counted as a match.
setMatrix(int, int, int, int, Matrix) - Method in class weka.classifiers.functions.pace.Matrix
Set a submatrix.
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, int, double) - Method in class weka.classifiers.functions.pace.PaceMatrix
Set the submatrix A[i0:i1][j0:j1] with a same value
setMatrix(int, int, int, DoubleVector) - Method in class weka.classifiers.functions.pace.PaceMatrix
Set the submatrix A[i0:i1][j] with the values stored in a DoubleVector
setMatrix(double[], boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Set the whole matrix from a 1-D array
setMax(double) - Method in class weka.gui.beans.ChartEvent
Set the max y value
setMaxBlankIterations(int) - Method in class weka.clusterers.MPCKMeans
Set the maximum number of blank iterations (those where no points are moved)
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
setMaxImplicitCommonTokenFraction(double) - Method in class weka.deduping.PairwiseSelector
Set the maximum fraction of common tokens that instances can have to be included as implicit negatives
setMaxInstInLeaf(int) - Method in class weka.core.KDTree
Sets the maximum number of instances in a leaf.
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.classifiers.bayes.SemiSupEM
Set the maximum number of iterations to perform
setMaxIterations(int) - Method in class weka.classifiers.meta.AdaBoostM1
Set the maximum number of boost iterations
setMaxIterations(int) - Method in class weka.classifiers.meta.LogitBoost
Set the maximum number of boost iterations
setMaxIterations(int) - Method in class weka.classifiers.meta.MultiBoostAB
Set the maximum number of boost iterations
setMaxIterations(int) - Method in class weka.classifiers.meta.QBoost
Set the maximum number of boost iterations
setMaxIterations(int) - Method in class weka.clusterers.EM
Set the maximum number of iterations to perform
setMaxIterations(int) - Method in class weka.clusterers.MPCKMeans
Set the maximum number of iterations
setMaxIterations(int) - Method in class weka.clusterers.XMeans
Sets the maximum number of iterations to perform.
setMaxIterations(int) - Method in class weka.core.metrics.GDMetricLearner
Set the maximum number of update iterations rate
setMaxIterations(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the maximum number of cleansing iterations to perform - < 1 means go until fully cleansed
setMaxIts(int) - Method in class weka.classifiers.functions.Logistic
Set the value of MaxIts.
setMaxK(int) - Method in class weka.classifiers.functions.VotedPerceptron
Set the value of maxK.
setMaxKMeans(int) - Method in class weka.clusterers.XMeans
Set the maximum number of iterations to perform in KMeans
setMaxKMeansForChildren(int) - Method in class weka.clusterers.XMeans
Sets the maximum number of iterations KMeans that is performed on the child centers.
setMaxMargin(double) - Method in class weka.classifiers.sparse.SVMlight
Set the maxMargin that an SVM can return
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
setMaxNumClusters(int) - Method in class weka.clusterers.XMeans
Sets the maximum number of clusters to generate.
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.
setMaxTimesPointsMoved(int) - Method in class weka.clusterers.assigners.RandomAssigner
 
setMaxTimesPointsMoved(int) - Method in class weka.clusterers.assigners.SimpleAssigner
 
setMaxTimesPointsMoved(int) - Method in class weka.clusterers.assigners.SortedAssigner
 
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.
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 weka.classifiers.sparse.IBkMetric
Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
setMergeThreshold(double) - Method in class weka.clusterers.HAC
Set the merge threshold
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.
setMetric(Metric) - Method in class weka.classifiers.misc.PrototypeMetric
Set the distance metric
setMetric(Metric) - Method in class weka.classifiers.sparse.IBkMetric
Set the distance metric
setMetric(Metric) - Method in class weka.clusterers.HAC
Set the distance metric
setMetric(Metric) - Method in class weka.clusterers.MPCKMeans
Set the distance metric
setMetric(Metric) - Method in class weka.clusterers.PCKMeans
Set the distance metric
setMetric(Metric) - Method in class weka.clusterers.PCSoftKMeans
Set the distance metric
setMetric(Metric) - Method in class weka.clusterers.SeededKMeans
Set the distance metric
setMetric(Metric) - Method in interface weka.clusterers.SemiSupClusterer
Set the clusterer metric
setMetric(InstanceMetric) - Method in class weka.deduping.BasicDeduper
Set the InstanceMetric that is used
setMetric(StringMetric) - Method in class weka.deduping.metrics.SumInstanceMetric
Set the baseline metric
setMetricLearner(MetricLearner) - Method in class weka.core.metrics.KL
Set the distance metric learner
setMetricLearner(MetricLearner) - Method in class weka.core.metrics.WeightedDotP
Set the distance metric learner
setMetricLearner(MetricLearner) - Method in class weka.core.metrics.WeightedEuclidean
Set the distance metric learner
setMetricName(String) - Method in class weka.classifiers.sparse.IBkMetric
Set the distance metric
setMetricName(String) - Method in class weka.clusterers.SeededKMeans
Set the distance metric
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
setMinBoxRelWidth(double) - Method in class weka.core.KDTree
Sets the minimum relative box width.
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
setMinCommonTokens(int) - Method in class weka.deduping.metrics.SumInstanceMetric
Set the minimum number of common tokens that is required from objects to be considered for distance computation
setMinInstNum(int) - Method in class weka.datagenerators.BIRCHCluster
Sets the lower boundary for instances per cluster.
setMinMargin(double) - Method in class weka.classifiers.sparse.SVMlight
Set the minMargin that an SVM can return
setMinMax(Instances) - Method in class weka.classifiers.bayes.SemiSupEM
Compute and store min max values for each numeric feature
setMinMetric(double) - Method in class weka.associations.Apriori
Set the value of minConfidence.
setMinNo(double) - Method in class weka.classifiers.rules.ConjunctiveRule
 
setMinNo(double) - Method in class weka.classifiers.rules.JRip
 
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.
setMinNumClusters(int) - Method in class weka.clusterers.XMeans
Sets the minimum number of clusters to generate.
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.M5Base
Set the minumum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.Rule
Set the minumum number of instances to allow at a leaf node
setMinNumInstances(double) - Method in class weka.classifiers.trees.m5.RuleNode
Set the minumum number of instances to allow at a leaf node
setMinNumObj(int) - Method in class weka.classifiers.rules.part.PART
Set the value of minNumObj.
setMinNumObj(int) - Method in class weka.classifiers.trees.j48.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.classifiers.bayes.NaiveBayesSimple
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.
setMinTokenLength(int) - Method in class weka.deduping.metrics.WordTokenizer
Set the minimum token length
setMinVarianceProp(double) - Method in class weka.classifiers.trees.REPTree
Set the value of MinVarianceProp.
setMinimizeExpectedCost(boolean) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Set the value of MinimizeExpectedCost.
setMissing(int) - Method in class weka.core.Instance
Sets a specific value to be "missing".
setMissing(Attribute) - Method in class weka.core.Instance
Sets a specific value to be "missing".
setMissingMerge(boolean) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.GainRatioAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.InfoGainAttributeEval
distribute the counts for missing values across observed values
setMissingMerge(boolean) - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
distribute the counts for missing values across observed values
setMissingMode(SelectedTag) - Method in class weka.classifiers.lazy.kstar.KStar
Sets the method to use for handling missing values.
setMissingMode(int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set the missing value mode.
setMissingSeperate(boolean) - Method in class weka.attributeSelection.CfsSubsetEval
Treat missing as a seperate value
setMixingDistribution(DiscreteFunction) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Sets the mixing distribution
setMode(SelectedTag) - Method in class weka.classifiers.sparse.SVMlight
Set the mode of the SVM
setMode(SelectedTag) - Method in class weka.extraction.ClusteringExtractor
Set the clustering mode
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.neural.NeuralNetwork
The momentum can be set using this command.
setMovePointsTillAssignmentStabilizes(boolean) - Method in class weka.clusterers.PCKMeans
Set m_MovePointsTillAssignmentStabilizes
setMovePointsTillAssignmentStabilizes(boolean) - Method in class weka.clusterers.assigners.RandomAssigner
Get/Set m_MovePointsTillAssignmentStabilizes
setMovePointsTillAssignmentStabilizes(boolean) - Method in class weka.clusterers.assigners.SimpleAssigner
Get/Set m_MovePointsTillAssignmentStabilizes
setMovePointsTillAssignmentStabilizes(boolean) - Method in class weka.clusterers.assigners.SortedAssigner
Get/Set m_MovePointsTillAssignmentStabilizes
setMustLinkWeight(double) - Method in class weka.clusterers.MPCKMeans
Set the must link constraint weight
setMustLinkWeight(double) - Method in class weka.clusterers.PCKMeans
Set the must link constraint weight
setMustLinkWeight(double) - Method in class weka.clusterers.PCSoftKMeans
Set the must link constraint weight
setMutationProb(double) - Method in class weka.attributeSelection.GeneticSearch
set the probability of mutation
setN(int) - Method in class weka.deduping.metrics.NGramTokenizer
Set the gram length
setName(String) - Method in class weka.filters.unsupervised.attribute.AddExpression
Set the name for the new attribute.
setName(String) - Method in class weka.gui.visualize.VisualizePanel
Set a name for this plot
setNegStringMode(SelectedTag) - Method in class weka.deduping.PairwiseSelector
Set the selection mode for negative string examples
setNegativesMode(SelectedTag) - Method in class weka.core.metrics.HardPairwiseSelector
Set the selection mode for negatives
setNegativesMode(SelectedTag) - Method in class weka.deduping.PairwiseSelector
Set the selection mode for negatives
setNoNormalization(boolean) - Method in class weka.classifiers.lazy.IBk
Set whether normalization is turned off.
setNoPruning(boolean) - Method in class weka.classifiers.trees.REPTree
Set the value of NoPruning.
setNoiseRate(double) - Method in class weka.datagenerators.BIRCHCluster
Sets the percentage of noise set.
setNominal() - Method in class weka.gui.visualize.ClassPanel
Sets the legend to be for a nominal variable
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.neural.NeuralNetwork
 
setNormalize(boolean) - Method in class weka.attributeSelection.MatlabICA
Set whether input data will be normalized.
setNormalize(boolean) - Method in class weka.attributeSelection.MatlabNMF
Set whether input data will be normalized.
setNormalize(boolean) - Method in class weka.attributeSelection.MatlabPCA
Set whether input data will be normalized.
setNormalize(boolean) - Method in class weka.attributeSelection.PrincipalComponents
Set whether input data will be normalized.
setNormalize(boolean) - Method in class weka.core.KDTree
Sets the flag for normalizing the widths of a KDTree Node by the width of the dimension in the universe.
setNormalizeAttributes(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
setNormalizeAttributes(boolean) - Method in class weka.classifiers.misc.Prototype
 
setNormalizeNumericClass(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
setNormalized(boolean) - Method in class weka.deduping.metrics.AffineMetric
Set the distance to be normalized by the sum of the string's lengths
setNormalized(boolean) - Method in class weka.deduping.metrics.AffineProbMetric
Set the distance to be normalized by the sum of the string's lengths
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
 
setNumAttributes(int) - Method in class weka.datagenerators.ClusterGenerator
Sets the number of attributes the dataset should have.
setNumAttributes(int) - Method in class weka.datagenerators.Generator
Sets the number of attributes the dataset should have.
setNumAttributesUsed() - Method in class weka.core.EuclideanDistance
Computes and sets the number of attributes used.
setNumBins(int) - Method in class weka.classifiers.meta.RegressionByDiscretization
Sets the number of bins the class attribute will be discretized into.
setNumClasses(int) - Method in class weka.datagenerators.Generator
Sets the number of classes the dataset should have.
setNumClusters(int) - Method in class weka.clusterers.EM
Set the number of clusters (-1 to select by CV).
setNumClusters(int) - Method in class weka.clusterers.FarthestFirst
set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.HAC
Set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.MPCKMeans
Set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.PCKMeans
Set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.PCSoftKMeans
Set the number of clusters to generate
setNumClusters(int) - Method in class weka.clusterers.SeededKMeans
Set the number of clusters to generate
setNumClusters(int) - Method in interface weka.clusterers.SemiSupClusterer
Set the number of clusters.
setNumClusters(int) - Method in class weka.clusterers.SimpleKMeans
set the number of clusters to generate
setNumClusters(int) - Method in class weka.datagenerators.ClusterGenerator
Sets the number of clusters the dataset should have.
setNumCycles(int) - Method in class weka.datagenerators.BIRCHCluster
Sets the the number of cycles.
setNumExamples(int) - Method in class weka.datagenerators.Generator
Sets the number of examples, given by option.
setNumExamplesAct(int) - Method in class weka.datagenerators.ClusterGenerator
Sets the number of examples the dataset should have.
setNumExamplesAct(int) - Method in class weka.datagenerators.Generator
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.
setNumFolds(int) - Method in class weka.classifiers.functions.SMO
Set the value of numFolds.
setNumFolds(int) - Method in class weka.classifiers.meta.CVParameterSelection
Set the number of folds used for cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.LogitBoost
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.classifiers.meta.MultiScheme
Sets the number of folds for cross-validation.
setNumFolds(int) - Method in class weka.classifiers.meta.Stacking
Sets the number of folds for the cross-validation.
setNumFolds(int) - Method in class weka.classifiers.rules.part.PART
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.trees.j48.J48
Set the value of numFolds.
setNumFolds(int) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.CrossValidationResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.ExtractionResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the value of NumFolds.
setNumFolds(int) - Method in class weka.filters.supervised.instance.StratifiedRemoveFolds
Sets the number of folds the dataset is split into.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveFolds
Sets the number of folds the dataset is split into.
setNumFolds(int) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the number of cross-validation folds to use - < 2 means no cross-validation.
setNumIndependentComponents(int) - Method in class weka.attributeSelection.MatlabICA
set number of Independent Components
setNumIrrelevant(int) - Method in class weka.datagenerators.RDG1
Sets the number of irrelevant attributes.
setNumIterations(int) - Method in class weka.classifiers.functions.VotedPerceptron
Set the value of NumIterations.
setNumIterations(int) - Method in class weka.classifiers.functions.Winnow
Set the value of numIterations.
setNumIterations(int) - Method in class weka.classifiers.meta.ActiveDecorate
Sets the max number of Decorate iterations to run.
setNumIterations(int) - Method in class weka.classifiers.meta.Bagging
Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.classifiers.meta.Crate
Sets the max number of Crate iterations to run.
setNumIterations(int) - Method in class weka.classifiers.meta.DEC
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.meta.Fable
Sets the max number of Decorate iterations to run.
setNumIterations(int) - Method in class weka.classifiers.meta.MetaCost
Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.classifiers.meta.QBag
Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.classifiers.meta.SemiSupDecorate
Sets the number of bagging iterations
setNumIterations(int) - Method in class weka.deduping.metrics.AffineProbMetric
Set the number of training iterations
setNumIterations() - Method in class weka.deduping.metrics.AffineProbMetric
Get the number of training iterations
setNumNegPairs(int) - Method in class weka.core.metrics.ClassifierMetricLearner
Set the number of different-class training pairs
setNumNegPairs(int) - Method in class weka.core.metrics.GDMetricLearner
Set the number of different-class training pairs
setNumNegPairs(int) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Set the number of different-class training pairs
setNumNegPairs(int) - Method in class weka.deduping.metrics.SumInstanceMetric
Set the number of different-class training pairs
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.ADTree
Sets the number of boosting iterations.
setNumPosDiffInstances(int) - Method in class weka.core.metrics.LearnableMetric
Set the number of positive instances to be used for training
setNumPosPairs(int) - Method in class weka.core.metrics.ClassifierMetricLearner
Set the number of same-class training pairs
setNumPosPairs(int) - Method in class weka.core.metrics.GDMetricLearner
Set the number of same-class training pairs
setNumPosPairs(int) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Set the number of same-class training pairs that is desired
setNumPosPairs(int) - Method in class weka.deduping.metrics.SumInstanceMetric
Set the number of same-class training pairs
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.
setNumStringParts(int) - Method in class weka.deduping.metrics.KernelVSMetric
Specify the number of string parts
setNumSubCmtys(int) - Method in class weka.classifiers.meta.MultiBoostAB
Set the number of sub committees to use
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.RaceSearch
Specify the number of attributes to select from the ranked list (if generating a ranking).
setNumToSelect(int) - Method in interface weka.attributeSelection.RankedOutputSearch
Specify the number of attributes to select from the ranked list.
setNumToSelect(int) - Method in class weka.attributeSelection.Ranker
Specify the number of attributes to select from the ranked list.
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.
setNumberOfSamplesFromEachGeneratingModel(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set how many samples to take from each generating model for use in computing the colour of a pixel
setNumeric(boolean) - Method in class weka.filters.unsupervised.attribute.MakeIndicator
Sets if the new Attribute is to be numeric.
setNumeric() - Method in class weka.gui.visualize.ClassPanel
Sets the legend to be for a numeric variable
setObjFunConvergenceDifference(double) - Method in class weka.clusterers.MPCKMeans
Set the minimum value of the objective function difference required for convergence
setObjFunConvergenceDifference(double) - Method in class weka.clusterers.PCKMeans
Set the minimum value of the objective function difference required for convergence
setObjFunConvergenceDifference(double) - Method in class weka.clusterers.PCSoftKMeans
Set the minimum value of the objective function difference required for convergence
setObjFunConvergenceDifference(double) - Method in class weka.clusterers.SeededKMeans
Set the minimum value of the objective function difference required for convergence
setObject(Object) - Method in class weka.gui.GenericObjectEditor
Sets the current Object.
setObject(Object) - Method in class weka.gui.beans.ClassAssignerCustomizer
Set the bean to be edited
setObject(Object) - Method in class weka.gui.beans.ClassifierCustomizer
Set the classifier object to be edited
setObject(Object) - Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer
Set the object to be edited
setObject(Object) - Method in class weka.gui.beans.FilterCustomizer
Set the filter bean to be edited
setObject(Object) - Method in class weka.gui.beans.LoaderCustomizer
Set the loader to be customized
setObject(Object) - Method in class weka.gui.beans.StripChartCustomizer
Set the StripChart object to be customized
setObject(Object) - Method in class weka.gui.beans.TrainTestSplitMakerCustomizer
Set the TrainTestSplitMaker to be customized
setObjective(double) - Method in class weka.attributeSelection.GeneticSearch.GABitSet
sets the objective merit value
setObjectiveFunction(int) - Method in class weka.attributeSelection.MatlabNMF
Sets the objective function.
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.classifiers.meta.CostSensitiveClassifier
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.classifiers.meta.MetaCost
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Sets the directory that will be searched for cost files when loading on demand.
setOptimizations(int) - Method in class weka.classifiers.rules.JRip
 
setOptimizeBins(boolean) - Method in class weka.classifiers.meta.RegressionByDiscretization
Sets whether the discretizer optimizes the number of bins
setOptions(String[]) - Method in class weka.associations.Apriori
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.BestFirst
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.CfsSubsetEval
Parses and sets a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ClassifierSubsetEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.ExhaustiveSearch
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.GainRatioAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.GeneticSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.InfoGainAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.MatlabICA
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.MatlabNMF
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.MatlabPCA
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.PrincipalComponents
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RaceSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RandomSearch
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.RankSearch
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.ReliefFAttributeEval
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.SymmetricalUncertAttributeEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.attributeSelection.WrapperSubsetEval
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.BVDecompose
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.RegressionBVDecompose
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.BayesNetK2
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.NaiveBayesSimple
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.bayes.SemiSupEM
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.LinearRegression
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.Logistic
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.SMO
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.VotedPerceptron
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.Winnow
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.neural.NeuralNetwork
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.functions.pace.PaceRegression
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.lazy.LWR
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.lazy.kstar.KStar
Parses a given list of options.
setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Sets the options.
setOptions(int, int, int) - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Set options.
setOptions(String[]) - Method in class weka.classifiers.meta.ActiveDecorate
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AdaBoostM1
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AdditiveRegression
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Bagging
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.CVParameterSelection
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.ClassificationViaRegression
Sets 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.Crate
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.DEC
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.DistributionMetaClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Fable
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.LogitBoost
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MetaCost
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiBoostAB
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiClassClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.MultiScheme
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.OrdinalClassClassifier
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.QBag
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.QBoost
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.RegressionByDiscretization
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.SemiSupDecorate
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.Stacking
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.meta.ThresholdSelector
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.misc.Prototype
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.misc.PrototypeMetric
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.rules.ConjunctiveRule
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.DecisionTable
Parses the options for this object.
setOptions(String[]) - Method in class weka.classifiers.rules.JRip
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.Ridor
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.rules.part.PART
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.sparse.IBkMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.sparse.SVMlight
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.RandomForest
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.RandomTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.adtree.ADTree
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.j48.J48
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.m5.M5Base
Parses a given list of options.
setOptions(String[]) - Method in class weka.classifiers.trees.m5.M5P
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.Cobweb
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.DistributionMetaClusterer
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.HAC
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.MPCKMeans
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.PCKMeans
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.PCSoftKMeans
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.SeededKMeans
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.SimpleKMeans
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.XMeans
Parses a given list of options.
setOptions(String[]) - Method in class weka.clusterers.assigners.LPAssigner
 
setOptions(String[]) - Method in class weka.clusterers.assigners.RMNAssigner
 
setOptions(String[]) - Method in class weka.clusterers.assigners.RandomAssigner
 
setOptions(String[]) - Method in class weka.clusterers.assigners.SimpleAssigner
 
setOptions(String[]) - Method in class weka.clusterers.assigners.SortedAssigner
 
setOptions(String[]) - Method in class weka.core.KDTree
Parses a given list of options.
setOptions(String[]) - Method in interface weka.core.OptionHandler
Sets the OptionHandler's options using the given list.
setOptions(String[]) - Method in class weka.core.metrics.AttrEvalMetricLearner
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.BarHillelMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.BarHillelMetricMatlab
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.ClassifierMetricLearner
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.GDMetricLearner
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.HardPairwiseSelector
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.KL
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.RandomPairwiseSelector
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.WeightedDotP
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.WeightedEuclidean
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.WeightedMahalanobis
Parses a given list of options.
setOptions(String[]) - Method in class weka.core.metrics.XingMetric
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.datagenerators.TextSource
 
setOptions(String[]) - Method in class weka.deduping.BasicDeduper
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.PairwiseSelector
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.blocking.Blocking
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.AffineMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.AffineProbMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.JaccardMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.KernelVSMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.NGramTokenizer
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.SumInstanceMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.VectorSpaceMetric
Parses a given list of options.
setOptions(String[]) - Method in class weka.deduping.metrics.WordTokenizer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
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.CSVResultListener
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.ClassifierSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.CrossValidationResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DatabaseResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DeduperSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
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.ExtractionResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.ExtractionSplitEvaluator
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.LearningCurveCrossValidationResultProducer
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.NoiseCurveCrossValidationResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.PairedTTester
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RandomSplitResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.RegressionSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Parses a given list of options.
setOptions(String[]) - Method in class weka.extraction.ClusteringExtractor
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.AttributeSelection
Parses a given list of options.
setOptions(String[]) - Method in class weka.filters.supervised.attribute.ClassOrder
Parses a given list of options 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.attribute.NominalToBinary
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.AbstractTimeSeries
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.Add
Parses a list of 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.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.Copy
Parses a given list of options controlling the behaviour of 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.FirstOrder
Parses a given list of options controlling the behaviour of 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.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.NumericTransform
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.Remove
Parses a given list of options controlling the behaviour of 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.RemoveUseless
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.StringToWordVector
Parses a given list of options controlling the behaviour of 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.instance.Randomize
Parses a list of 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.RemoveMisclassified
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.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.filters.unsupervised.instance.Resample
Parses a list of options for this object.
setOutput(PrintWriter) - Method in class weka.datagenerators.ClusterGenerator
Sets the print writer.
setOutput(PrintWriter) - Method in class weka.datagenerators.Generator
Sets the print writer.
setOutputCenterFile(String) - Method in class weka.clusterers.XMeans
Sets the name of the file to write the list of centers to.
setOutputFile(File) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.CSVResultListener
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.CrossValidationResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.ExtractionResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.RandomSplitResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of OutputFile.
setOutputFile(File) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the value of OutputFile.
setOutputFormat(Instances) - Method in class weka.filters.Filter
Sets the format of output instances.
setOutputFormat() - Method in class weka.filters.supervised.attribute.AttributeSelection
Set the output format.
setOutputFormat() - Method in class weka.filters.supervised.attribute.Discretize
Set the output format.
setOutputFormat() - Method in class weka.filters.unsupervised.attribute.Discretize
Set the output format.
setOutputWordCounts(boolean) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Sets whether output instances contain 0 or 1 indicating word presence, or word counts.
setParent(Edge) - Method in class weka.gui.treevisualizer.Node
Set the value of parent.
setPattern(int) - Method in class weka.datagenerators.BIRCHCluster
Sets the pattern type.
setPercent(int) - Method in class weka.filters.unsupervised.attribute.AddNoise
Sets the size of noise data, as a percentage of the original set.
setPercent() - Method in class weka.gui.visualize.MatrixPanel
Calculates the percentage to resample
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
setPercentage(int) - Method in class weka.filters.unsupervised.instance.RemovePercentage
Sets the percentage of intances to select.
setPhaseTwoRandom(boolean) - Method in class weka.clusterers.MPCKMeans
Set m_PhaseTwoRandom
setPhaseTwoRandom(boolean) - Method in class weka.clusterers.PCKMeans
Set m_PhaseTwoRandom
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
setPlotPoints(String) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.ExtractionResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of PlotPoints.
setPlotPoints(String) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the value of PlotPoints.
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
setPoints() - Method in class weka.experiment.Grapher
Set array of points on learning curve from Key_Total_instances values in data
setPoints() - Method in class weka.experiment.NoiseGrapher
Set array of points on learning curve from Key_Noise_levels values in data
setPoints(double[]) - Method in class weka.experiment.PairedTTester
Set the points on the learning curve
setPopulationSize(int) - Method in class weka.attributeSelection.GeneticSearch
set the population size
setPosNegDiffInstanceRatio(double) - Method in class weka.core.metrics.LearnableMetric
Set the ratio of positive and negative instances to be used for training
setPosStringMode(SelectedTag) - Method in class weka.deduping.PairwiseSelector
Set the selection mode for positive string examples
setPositivesMode(SelectedTag) - Method in class weka.core.metrics.HardPairwiseSelector
Set the selection mode for positives
setPositivesMode(SelectedTag) - Method in class weka.deduping.PairwiseSelector
Set the selection mode for positives
setPrecision(double) - Method in class weka.classifiers.functions.Logistic
Sets the precision of stopping criterion in Newton method.
setPrecision(int) - Method in class weka.experiment.PairedTTester
Set the value of m_Precision.
setPriors(Instances) - Method in class weka.classifiers.EnsembleEvaluation
Sets the class prior probabilities
setPriors(Instances) - Method in class weka.classifiers.Evaluation
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.
setProperty(int, Object) - Method in class weka.experiment.Experiment
Recursively sets the custom property value, by setting all values along the property path.
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.
setPrune(boolean) - Method in class weka.core.KDTree
Sets the flag for pruning of the blacklisting algorithm.
setPruningType(SelectedTag) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set the pruning type
setPseudoCountDirichlet(double) - Method in class weka.core.metrics.KL
Set the pseudo-count value for Dirichlet smoothing
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.
setRandomOrder(boolean) - Method in class weka.classifiers.bayes.BayesNetK2
Set random order flag
setRandomSeed(long) - Method in class weka.classifiers.functions.LeastMedSq
Set the seed for the random number generator
setRandomSeed(int) - Method in class weka.classifiers.functions.SMO
Set the value of randomSeed.
setRandomSeed(long) - Method in class weka.classifiers.functions.neural.NeuralNetwork
This seeds the random number generator, that is used when a random number is needed for the network.
setRandomSeed(int) - Method in class weka.classifiers.trees.adtree.ADTree
Sets random seed for a random walk.
setRandomSeed(int) - Method in class weka.clusterers.HAC
Set the random number seed
setRandomSeed(int) - Method in class weka.clusterers.MPCKMeans
Set the random number seed
setRandomSeed(int) - Method in class weka.clusterers.PCKMeans
Set the random number seed
setRandomSeed(int) - Method in class weka.clusterers.PCSoftKMeans
Set the random number seed
setRandomSeed(int) - Method in class weka.clusterers.SeededKMeans
Set the random number seed
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.Randomize
Set the random number generator seed value.
setRandomSeed(int) - Method in class weka.filters.unsupervised.instance.Resample
Sets the random number seed.
setRandomSize(double) - Method in class weka.classifiers.meta.DEC
Sets number of random instances to add at each iteration.
setRandomSize(double) - Method in class weka.classifiers.meta.SemiSupDecorate
Sets number of random instances to add at each iteration.
setRandomWidthFactor(double) - Method in class weka.classifiers.meta.MultiClassClassifier
Sets the multiplier when generating random codes.
setRandomizeData(boolean) - Method in class weka.experiment.RandomSplitResultProducer
Set to true if dataset is to be randomized
setRandomized(boolean) - Method in class weka.classifiers.rules.ConjunctiveRule
 
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.
setRank(int) - Method in class weka.attributeSelection.MatlabNMF
Sets the rank of the basis matrix W.
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.ActiveFeatureAcquisitionCVResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set to true if raw split evaluator output is to be saved
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.DedupingPRCurveCVResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.ExtractionResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
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
setRawOutput(boolean) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set to true if raw split evaluator output is to be saved
setRawOutput(boolean) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set to true if raw split evaluator output is to be saved
setRedClassValue(int) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the class value index for the red colour
setReducedErrorPruning(boolean) - Method in class weka.classifiers.rules.part.PART
Set the value of reducedErrorPruning.
setReducedErrorPruning(boolean) - Method in class weka.classifiers.trees.j48.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.ClusterGenerator
Sets the relation name the dataset should have.
setRelationName(String) - Method in class weka.datagenerators.Generator
Sets the relation name the dataset should have.
setRemoveAllMissingCols(boolean) - Method in class weka.associations.Apriori
Remove columns containing all missing values.
setRemoveInconsistentExamples(boolean) - Method in class weka.classifiers.sparse.SVMlight
Set whether the inconsistent examples are removed and retraining follows
setReportFrequency(int) - Method in class weka.attributeSelection.GeneticSearch
set how often reports are generated
setReset(boolean) - Method in class weka.classifiers.functions.neural.NeuralNetwork
This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.
setReset(boolean) - Method in class weka.gui.beans.ChartEvent
Set the reset flag
setResultKeyFromDialog() - Method in class weka.gui.experiment.ResultsPanel
 
setResultListener(ResultListener) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
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.CrossValidationResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.DatabaseResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
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.ExtractionResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Sets the object to send results of each run to.
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.NoiseCurveCrossValidationResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.RandomSplitResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.RemoteExperiment
Sets the result listener where results will be sent.
setResultListener(ResultListener) - Method in interface weka.experiment.ResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Sets the object to send results of each run to.
setResultListener(ResultListener) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Sets the object to send results of each run to.
setResultProducer(ResultProducer) - Method in class weka.experiment.AveragingResultProducer
Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.DatabaseResultProducer
Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.Experiment
Set the result producer used for the current experiment.
setResultProducer(ResultProducer) - Method in class weka.experiment.LearningRateResultProducer
Set the ResultProducer.
setResultProducer(ResultProducer) - Method in class weka.experiment.RemoteExperiment
Set the result producer used for the current experiment.
setResultsetKeyColumns(Range) - Method in class weka.experiment.PairedTTester
Set the value of ResultsetKeyColumns.
setRetrieval(int) - Method in class weka.core.converters.AbstractLoader
Sets the retrieval mode.
setRidge(double) - Method in class weka.classifiers.functions.LinearRegression
Set the value of Ridge.
setRidge(double) - Method in class weka.classifiers.functions.Logistic
Sets the ridge parameter.
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
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.
setS(double) - Method in class weka.classifiers.sparse.SVMlight
Set parameter s in sigmoid/polynomial kernel
setSIndex(int) - Method in class weka.gui.visualize.VisualizePanel
Set the shape for creating splits.
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.classifiers.trees.adtree.ADTree
Sets whether the tree is to save instance data.
setSaveInstanceData(boolean) - Method in class weka.classifiers.trees.j48.J48
Set whether instance data is to be saved.
setSaveInstanceData(boolean) - Method in class weka.clusterers.Cobweb
Set the value of saveInstances.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.M5P
Set whether to save instance data at each node in the tree for visualization purposes
setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.Rule
Sets whether instances at each node in an M5 tree should be saved for visualization purposes.
setSaveInstances(boolean) - Method in class weka.classifiers.trees.m5.RuleNode
Set whether to save instances for visualization purposes.
setScoreType(SelectedTag) - Method in class weka.classifiers.bayes.BayesNet
Method declaration
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
setSearch(ASSearch) - Method in class weka.filters.supervised.attribute.AttributeSelection
Set as string holding the name of a search class
setSearchPath(SelectedTag) - Method in class weka.classifiers.trees.adtree.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(int) - Method in class weka.filters.unsupervised.attribute.MergeTwoValues
Sets index of the second value used.
setSecondValueIndex(int) - Method in class weka.filters.unsupervised.attribute.SwapValues
Sets index of the second value used.
setSeed(int) - Method in class weka.attributeSelection.AttributeSelection
set the seed for use in cross validation
setSeed(int) - Method in class weka.attributeSelection.GeneticSearch
set the seed for random number generation
setSeed(int) - Method in class weka.attributeSelection.ReliefFAttributeEval
Set the random number seed for randomly sampling instances.
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.RegressionBVDecompose
Sets the random number seed
setSeed(int) - Method in class weka.classifiers.bayes.SemiSupEM
Set the random number seed
setSeed(int) - Method in class weka.classifiers.evaluation.EvaluationUtils
Sets the seed for randomization during cross-validation
setSeed(int) - Method in class weka.classifiers.functions.VotedPerceptron
Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.functions.Winnow
Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.meta.ActiveDecorate
Set the seed for random number generator.
setSeed(int) - Method in class weka.classifiers.meta.AdaBoostM1
Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.meta.Bagging
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.CVParameterSelection
Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.CostSensitiveClassifier
Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.meta.Crate
Set the seed for random number generator.
setSeed(int) - Method in class weka.classifiers.meta.DEC
Set 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.Fable
Set the seed for random number generator.
setSeed(int) - Method in class weka.classifiers.meta.LogitBoost
Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.meta.MetaCost
Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.meta.MultiBoostAB
Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.meta.MultiClassClassifier
Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.MultiScheme
Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.QBag
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.QBoost
Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set seed for resampling.
setSeed(int) - Method in class weka.classifiers.meta.SemiSupDecorate
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.Stacking
Sets the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.meta.ThresholdSelector
Sets the seed for random number generation.
setSeed(long) - Method in class weka.classifiers.rules.ConjunctiveRule
 
setSeed(long) - Method in class weka.classifiers.rules.JRip
 
setSeed(int) - Method in class weka.classifiers.rules.Ridor
 
setSeed(int) - Method in class weka.classifiers.trees.REPTree
Set the value of Seed.
setSeed(int) - Method in class weka.classifiers.trees.RandomForest
Set the seed for random number generation.
setSeed(int) - Method in class weka.classifiers.trees.RandomTree
Set the seed for random number generation.
setSeed(int) - Method in class weka.clusterers.ClusterEvaluation
set the seed to use for cross validation
setSeed(int) - Method in class weka.clusterers.EM
Set the random number seed
setSeed(int) - Method in class weka.clusterers.FarthestFirst
Set the random number seed
setSeed(int) - Method in class weka.clusterers.SimpleKMeans
Set the random number seed
setSeed(int) - Method in class weka.clusterers.XMeans
Sets the random number seed.
setSeed(int) - Method in interface weka.core.Randomizable
Set the seed for random number generation.
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(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(int) - Method in class weka.gui.beans.CrossValidationFoldMaker
Set the seed
setSeed(int) - Method in class weka.gui.beans.TrainTestSplitMaker
Set the random seed
setSeedHash(HashMap) - Method in class weka.clusterers.HAC
Set the m_SeedHash
setSeedHash(HashMap) - Method in class weka.clusterers.MPCKMeans
Set the m_SeedHash
setSeedHash(HashMap) - Method in class weka.clusterers.PCKMeans
Set the m_SeedHash
setSeedHash(HashMap) - Method in class weka.clusterers.PCSoftKMeans
Set the m_SeedHash
setSeedHash(HashMap) - Method in class weka.clusterers.SeededKMeans
Set the m_SeedHash
setSeedUnseenClasses(boolean) - Method in class weka.classifiers.bayes.SemiSupEM
 
setSeedable(boolean) - Method in class weka.clusterers.HAC
Turn seeding on and off
setSeedable(boolean) - Method in class weka.clusterers.MPCKMeans
Turn seeding on and off
setSeedable(boolean) - Method in class weka.clusterers.PCKMeans
Turn seeding on and off
setSeedable(boolean) - Method in class weka.clusterers.PCSoftKMeans
Turn seeding on and off
setSeedable(boolean) - Method in class weka.clusterers.SeededKMeans
Turn seeding on and off
setSeedingMethod(SelectedTag) - Method in class weka.clusterers.SeededKMeans
Set the seeding method.
setSelectedRange(String) - Method in class weka.filters.unsupervised.attribute.StringToWordVector
Set the value of m_SelectedRange.
setSelectionScheme(int) - Method in class weka.classifiers.meta.ActiveDecorate
Set the value of m_SelectionScheme.
setSelectionScheme(int) - Method in class weka.classifiers.meta.Fable
Set the value of m_SelectionScheme.
setSelectionThreshold(double) - Method in class weka.attributeSelection.RaceSearch
Set the threshold by which the AttributeSelection module can discard attributes.
setSelector(PairwiseSelector) - Method in class weka.core.metrics.ClassifierMetricLearner
Set the pairwise selector
setSelector(PairwiseSelector) - Method in class weka.core.metrics.GDMetricLearner
Set the pairwise selector
setSelector(PairwiseSelector) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Set the pairwise selector for this metric
setSelector(PairwiseSelector) - Method in class weka.deduping.metrics.SumInstanceMetric
Set the pairwise selector for this metric
setSeparateTrainingFile(String) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Set the value of separate training file
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.
setShapeSize(int[]) - Method in class weka.gui.visualize.PlotData2D
Set the shape sizes for the plot data
setShapeSize(FastVector) - Method in class weka.gui.visualize.PlotData2D
Set the shape sizes for the plot data
setShapeType(int[]) - Method in class weka.gui.visualize.PlotData2D
Set the shape type for the plot data
setShapeType(FastVector) - Method in class weka.gui.visualize.PlotData2D
Set the shape type for the plot data
setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
This can be used to set the shapes that should appear.
setShapes(FastVector) - Method in class weka.gui.visualize.VisualizePanel
This will set the shapes for the instances.
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.associations.Apriori
Set the value of significanceLevel.
setSignificanceLevel(double) - Method in class weka.attributeSelection.RaceSearch
Sets the significance level to use
setSignificanceLevel(double) - Method in class weka.experiment.PairedTTester
Set the value of SignificanceLevel.
setSindex(int) - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Set the index of the attribute to use for the shape.
setSingle(String) - Method in class weka.gui.ResultHistoryPanel
Sets the single-click display to view the named result.
setSinglePass(boolean) - Method in class weka.clusterers.assigners.RMNAssigner
Get/Set m_singlePass
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
setSmoothing(boolean) - Method in class weka.classifiers.trees.m5.RuleNode
Get if smoothing is being used
setSmoothingType(SelectedTag) - Method in class weka.core.metrics.KL
Set the type of smoothing
setSource(File) - Method in class weka.core.converters.AbstractLoader
Default implementation throws an IOException.
setSource(InputStream) - Method in class weka.core.converters.AbstractLoader
Default implementation throws an IOException.
setSource(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(InputStream) - Method in class weka.core.converters.ArffLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(File) - Method in class weka.core.converters.C45Loader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(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(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(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(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(Node) - Method in class weka.gui.treevisualizer.Edge
Set the value of source.
setSpaceEquivalents(String) - Method in class weka.deduping.metrics.NGramTokenizer
Specify which characters should be treated as spaces
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.ActiveFeatureAcquisitionCVResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.CrossValidationResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.ExtractionResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.RandomSplitResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the SplitEvaluator.
setSplitEvaluator(SplitEvaluator) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the SplitEvaluator.
setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(Instances) - Method in class weka.classifiers.trees.j48.C45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(double) - Method in class weka.filters.unsupervised.instance.RemoveWithValues
Split point to be used for selection on numeric attribute.
setStartSet(String) - Method in class weka.attributeSelection.BestFirst
Sets a starting set of attributes for the search.
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 class weka.attributeSelection.GeneticSearch
Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.RandomSearch
Sets a starting set of attributes for the search.
setStartSet(String) - Method in class weka.attributeSelection.Ranker
Sets a starting set of attributes for the search.
setStartSet(String) - Method in interface weka.attributeSelection.StartSetHandler
Sets a starting set of attributes for the search.
setStatic() - Method in class weka.gui.beans.BeanVisual
Set the static version of the icon
setStatus(int) - Method in class weka.gui.beans.IncrementalClassifierEvent
Set the status
setStatus(int) - Method in class weka.gui.beans.InstanceEvent
Set the status
setStatusMessage(String) - Method in class weka.experiment.TaskStatusInfo
Set the status message.
setStemming(boolean) - Method in class weka.deduping.metrics.Tokenizer
Turn stemming on/off
setStepSize(int) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Set the value of StepSize.
setStepSize(int) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set the value of StepSize.
setStepSize(int) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set the value of StepSize.
setStepSize(int) - Method in class weka.experiment.LearningRateResultProducer
Set the value of StepSize.
setStepSize(int) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set the value of StepSize.
setStepSize(int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set the value of StepSize.
setStepSize(int) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set the value of StepSize.
setStepSize(int) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of StepSize.
setStepSize(int) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the value of StepSize.
setStopwordRemoval(boolean) - Method in class weka.deduping.metrics.Tokenizer
Turn stopword removal on/off and load the stopwords
setStringMetrics(StringMetric[]) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Set the baseline metric
setSubCost(double) - Method in class weka.deduping.metrics.AffineMetric
Set the substitution cost
setSubtreeRaising(boolean) - Method in class weka.classifiers.trees.j48.J48
Set the value of subtreeRaising.
setTTester() - Method in class weka.gui.experiment.ResultsPanel
Updates the test chooser with possible tests
setTable(AttributeStats, int) - Method in class weka.gui.AttributeSummaryPanel
Creates a tablemodel for the attribute being displayed
setTarget(Object) - Method in class weka.gui.PropertySheetPanel
Sets a new target object for customisation.
setTarget(Node) - Method in class weka.gui.treevisualizer.Edge
Set the value of target.
setTaskResult(Object) - Method in class weka.experiment.TaskStatusInfo
Set the returnable result for this task..
setTempDirPath(String) - Method in class weka.classifiers.sparse.SVMlight
Set the path for the temporary files
setTestBaseFromDialog() - Method in class weka.gui.experiment.ResultsPanel
 
setTestSet() - Method in class weka.gui.explorer.ClassifierPanel
Sets the user test set.
setTestSet() - Method in class weka.gui.explorer.ClustererPanel
Sets the user test set.
setText(String) - Method in class weka.gui.beans.BeanVisual
Set the label for the visual.
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.ForwardSelection
Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double) - Method in class weka.attributeSelection.RaceSearch
Sets the threshold for comparisons
setThreshold(double) - Method in interface weka.attributeSelection.RankedOutputSearch
Sets a threshold by which attributes can be discarded from the ranking.
setThreshold(double) - Method in class weka.attributeSelection.Ranker
Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double) - Method in class weka.attributeSelection.WrapperSubsetEval
Set the value of the threshold for repeating cross validation
setThreshold(double) - Method in class weka.classifiers.functions.Winnow
Set the value of Threshold.
setThreshold(double) - Method in class weka.classifiers.functions.pace.PaceRegression
Set threshold for the olsc estimator
setThreshold(double) - Method in class weka.classifiers.meta.DEC
Set the value of threshold.
setThreshold(double) - Method in class weka.classifiers.meta.SemiSupDecorate
Set the value of threshold.
setThreshold(double) - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Sets the threshold for the max error when predicting a numeric class.
setTimes(int, 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
setTokenizer(Tokenizer) - Method in class weka.deduping.blocking.Blocking
Set the tokenizer to use
setTokenizer(Tokenizer) - Method in class weka.deduping.metrics.JaccardMetric
Set the tokenizer to use
setTokenizer(Tokenizer) - Method in class weka.deduping.metrics.KernelVSMetric
Set the tokenizer to use
setTokenizer(Tokenizer) - Method in class weka.deduping.metrics.VectorSpaceMetric
Set the tokenizer to use
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.
setTop(double) - Method in class weka.gui.treevisualizer.Node
Set the value of top.
setTrainIterations(int) - Method in class weka.classifiers.BVDecompose
Sets the maximum number of boost iterations
setTrainIterations(int) - Method in class weka.classifiers.RegressionBVDecompose
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.
setTrainPoolSize(int) - Method in class weka.classifiers.RegressionBVDecompose
Set the number of instances in the training pool.
setTrainProportion(double) - Method in class weka.deduping.BasicDeduper
Set the amount of training
setTrainable(SelectedTag) - Method in class weka.clusterers.MPCKMeans
Turn metric learning on and off
setTrainable(boolean) - Method in class weka.core.metrics.LearnableMetric
Set the value of metricTraining
setTrainingData(Instances) - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Set the training data to use
setTrainingTime(int) - Method in class weka.classifiers.functions.neural.NeuralNetwork
Set the number of training epochs to perform.
setTransformBackToOriginal(boolean) - Method in class weka.attributeSelection.MatlabICA
Sets whether the data should be transformed back to the original space
setTransformBackToOriginal(boolean) - Method in class weka.attributeSelection.MatlabPCA
Sets whether the data should be transformed back to the original space
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
 
setUnlabeled(Instances) - Method in interface weka.classifiers.SemiSupClassifier
Provide unlabeled data to the classifier.
setUnlabeled(Instances) - Method in class weka.classifiers.bayes.SemiSupEM
Provide unlabeled data to the classifier.
setUnlabeled(Instances) - Method in class weka.classifiers.meta.SemiSupDecorate
Provide unlabeled data to the classifier.
setUnpruned(boolean) - Method in class weka.classifiers.rules.part.PART
Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.j48.J48
Set the value of unpruned.
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.M5Base
Use unpruned tree/rules
setUnpruned(boolean) - Method in class weka.classifiers.trees.m5.Rule
Use unpruned tree/rules
setUpComboBoxes(Instances) - Method in class weka.gui.visualize.VisualizePanel
 
setUpdateIncrementalClassifier(boolean) - Method in class weka.gui.beans.Classifier
 
setUpper(int) - Method in class weka.core.Range
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.ActiveFeatureAcquisitionCVResultProducer
Set the value of UpperSize.
setUpperSize(int) - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Set the value of UpperSize.
setUpperSize(int) - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Set the value of UpperSize.
setUpperSize(int) - Method in class weka.experiment.LearningRateResultProducer
Set the value of UpperSize.
setUpperSize(int) - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Set the value of UpperSize.
setUpperSize(int) - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Set the value of UpperSize.
setUpperSize(int) - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Set the value of UpperSize.
setUpperSize(int) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of UpperSize.
setUpperSize(int) - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Set the value of UpperSize.
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.
setUseBlocking(boolean) - Method in class weka.deduping.BasicDeduper
Turn debugging output on/off
setUseCombinedObjectiveFunction(boolean) - Method in class weka.clusterers.MPCKMeans
Use combined objective function (if true) or Potts Model (if false)
setUseDITCSmoothing(boolean) - Method in class weka.core.metrics.KL
Switch between using and not using DITC smoothing
setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.Discretize
Set the value of UseEqualFrequency.
setUseEqualFrequency(boolean) - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
Set the value of UseEqualFrequency.
setUseFalseImplicitNegatives(boolean) - Method in class weka.deduping.PairwiseSelector
Turn using false implicit negatives on/off
setUseGenerativeModel(boolean) - Method in class weka.deduping.metrics.AffineProbMetric
Set the distance to use the generative model or convert back to the additive model
setUseIBk(boolean) - Method in class weka.classifiers.rules.DecisionTable
Sets whether IBk should be used instead of the majority class
setUseIDF(boolean) - Method in class weka.deduping.blocking.Blocking
Turn IDF weighting on/off
setUseIDF(boolean) - Method in class weka.deduping.metrics.KernelVSMetric
Turn IDF weighting on/off
setUseIDF(boolean) - Method in class weka.deduping.metrics.VectorSpaceMetric
Turn IDF weighting on/off
setUseIDivergence(boolean) - Method in class weka.core.metrics.KL
Switch between regular KL divergence and I-divergence
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.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.
setUseMultipleMetrics(boolean) - Method in class weka.clusterers.MPCKMeans
Turn on/off the use of per-cluster metrics
setUseMustLinkPairsOnly(boolean) - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Set the value of useMustLinkPairsOnly.
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.
setUseRejectedPositives(boolean) - Method in class weka.deduping.PairwiseSelector
Turn using rejected positives as negatives on/off
setUseResampling(boolean) - Method in class weka.classifiers.meta.AdaBoostM1
Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.LogitBoost
Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.MultiBoostAB
Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.QBoost
Set resampling mode
setUseResampling(boolean) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set resampling mode
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
setUseWeights(int) - Method in class weka.classifiers.meta.DEC
Set flag for using weights for committee votes.
setUseWeights(int) - Method in class weka.classifiers.meta.SemiSupDecorate
Set flag for using weights for committee votes.
setValid(boolean) - Method in class weka.core.KDTree
Sets KDTree to be valid for dataset in m_Instances.
setValidationChunkSize(int) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Set the validation chunk size
setValidationSetSize(int) - Method in class weka.classifiers.functions.neural.NeuralNetwork
This will set the size of the validation set.
setValidationThreshold(int) - Method in class weka.classifiers.functions.neural.NeuralNetwork
This sets the threshold to use for when validation testing is being done.
setValue(double) - Method in class weka.classifiers.trees.adtree.PredictionNode
Sets the prediction value of the node.
setValue(int, double) - Method in class weka.core.BinarySparseInstance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, double) - Method in class weka.core.Instance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(int, String) - Method in class weka.core.Instance
Sets a value of a nominal or string attribute to the given value.
setValue(Attribute, double) - Method in class weka.core.Instance
Sets a specific value in the instance to the given value (internal floating-point format).
setValue(Attribute, String) - Method in class weka.core.Instance
Sets a value of an nominal or string attribute to the given value.
setValue(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) - Method in class weka.deduping.metrics.Weight
Set the current count
setValue(double) - Method in class weka.deduping.metrics.Weight
Set the current count
setValue(Object) - Method in class weka.gui.CostMatrixEditor
Sets the value of the CostMatrix to be edited.
setValue(Object) - Method in class weka.gui.GenericArrayEditor
Sets the current object array.
setValue(Object) - Method in class weka.gui.GenericObjectEditor
Sets the current Object.
setValueArray(double[]) - Method in class weka.core.Instance
Sets the whole attribute value array
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)
setValueSparse(int, double) - Method in class weka.core.BinarySparseInstance
Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.Instance
Sets a specific value in the instance to the given value (internal floating-point format).
setValueSparse(int, double) - Method in class weka.core.SparseInstance
Sets a specific value in the instance to the given value (internal floating-point format).
setVarianceCovered(double) - Method in class weka.attributeSelection.MatlabPCA
Sets the amount of variance to account for when retaining principal components
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
setVerbose(boolean) - Method in class weka.clusterers.HAC
set the verbosity level of the clusterer
setVerbose(boolean) - Method in class weka.clusterers.MPCKMeans
set the verbosity level of the clusterer
setVerbose(boolean) - Method in class weka.clusterers.PCKMeans
set the verbosity level of the clusterer
setVerbose(boolean) - Method in class weka.clusterers.PCSoftKMeans
set the verbosity level of the clusterer
setVerbose(boolean) - Method in class weka.clusterers.SeededKMeans
set the verbosity level of the clusterer
setVerbose(boolean) - Method in class weka.clusterers.Seeder
set the verbosity level of the clusterer
setVerbose(boolean) - Method in interface weka.clusterers.SemiSupClusterer
Sets verbose level
setVerbose(boolean) - Method in class weka.extraction.ClusteringExtractor
set the verbosity level of the clusterer
setVerbosityLevel(int) - Method in class weka.classifiers.sparse.SVMlight
Set verbosity level, can be anything between 0 and 3
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractDataSource
Set the visual for this data source
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractEvaluator
Set the visual
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTestSetProducer
Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.AbstractTrainingSetProducer
Set the visual for this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.ClassAssigner
 
setVisual(BeanVisual) - Method in class weka.gui.beans.Classifier
Sets the visual appearance of this wrapper bean
setVisual(BeanVisual) - Method in class weka.gui.beans.DataVisualizer
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.Filter
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.GraphViewer
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.StripChart
Set the visual appearance of this bean
setVisual(BeanVisual) - Method in class weka.gui.beans.TextViewer
Describe setVisual method here.
setVisual(BeanVisual) - Method in interface weka.gui.beans.Visible
Set a new visual representation
setVoteFlag(boolean) - Method in class weka.datagenerators.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
setWeightThreshold(int) - Method in class weka.classifiers.meta.AdaBoostM1
Set weight threshold
setWeightThreshold(int) - Method in class weka.classifiers.meta.LogitBoost
Set weight thresholding
setWeightThreshold(int) - Method in class weka.classifiers.meta.MultiBoostAB
Set weight threshold
setWeightThreshold(int) - Method in class weka.classifiers.meta.QBoost
Set weight threshold
setWeightingDimensions(boolean[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Set the dimensions to be used in computing a weight for each instance generated
setWeightingDimensions(boolean[]) - Method in class weka.gui.boundaryvisualizer.EMDataGenerator
Set which dimensions to use when computing a weight for the next instance to generate
setWeightingDimensions(boolean[]) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator
Set which dimensions to use when computing a weight for the next instance to generate
setWeightingKernel(int) - Method in class weka.classifiers.lazy.LWR
Sets the kernel weighting method to use.
setWeightingValues(double[]) - Method in interface weka.gui.boundaryvisualizer.DataGenerator
Set the values of the dimensions (chosen via setWeightingDimensions) to be used when computing instance weights
setWeightingValues(double[]) - Method in class weka.gui.boundaryvisualizer.EMDataGenerator
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 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
setWeights(double[]) - Method in class weka.core.metrics.LearnableMetric
Set the feature weights
setWeights(Matrix) - Method in class weka.core.metrics.WeightedMahalanobis
Set the weights
setWeights(double[]) - Method in class weka.core.metrics.WeightedMahalanobis
override the parent class methods
setWholeDataErr(boolean) - Method in class weka.classifiers.rules.Ridor
 
setWidth(double) - Method in class weka.classifiers.sparse.SVMlight
Set the epsilon width of tube for regression
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 weka.classifiers.sparse.IBkMetric
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.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 class weka.gui.beans.Loader
Set the loader
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
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
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.
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.PlotPanel
Set the index of the attribute to go on the x axis
setXval(boolean) - Method in class weka.attributeSelection.AttributeSelection
do a cross validation
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
setYIndex(int) - Method in class weka.gui.visualize.VisualizePanel
Set the index of the attribute for the y axis
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.PlotPanel
Set the index of the attribute to go on the y axis
setclassNoise(boolean) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set to true if Noise is to be added to Class
setclassNoiseTest(boolean) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set to true if Noise is to be added to Class in Testing
setfeatureMiss(boolean) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set to true if Features are to be set Missing
setfeatureMissTest(boolean) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set to true if Features are to be set Missing in Testing
setfeatureNoise(boolean) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set to true if Noise is to be added to Features
setfeatureNoiseTest(boolean) - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Set to true if Noise is to be added in Fetures in Testing
setupAttribLists() - Method in class weka.gui.visualize.MatrixPanel
Sets up the UI's attributes lists
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.
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.GraphViewer
Popup a result list from which the user can select a graph to view
showResults() - Method in class weka.gui.beans.TextViewer
Popup a component to display the selected text
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
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
sign() - Method in class weka.classifiers.functions.pace.DoubleVector
Returns the signs of all elements in terms of -1, 0 and +1.
sign - Variable in class weka.classifiers.functions.pace.ExponentialFormat
 
significanceLevelTipText() - Method in class weka.associations.Apriori
Returns the tip text for this property
significanceLevelTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
similarity(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetric
Returns a similarity estimate between two instances.
similarity(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetricMatlab
Returns a similarity estimate between two instances.
similarity(Instance, Instance) - Method in class weka.core.metrics.KL
Returns a similarity estimate between two instances.
similarity(Instance, Instance) - Method in class weka.core.metrics.Metric
Returns a similarity estimate between two instances.
similarity(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between two instances.
similarity(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a similarity estimate between two instances.
similarity(Instance, Instance) - Method in class weka.core.metrics.WeightedMahalanobis
Returns a similarity estimate between two instances.
similarity(Instance, Instance) - Method in class weka.core.metrics.XingMetric
Returns a similarity estimate between two instances.
similarity(InstanceReference, InstanceReference) - Method in class weka.deduping.blocking.Blocking
Compute similarity between two strings
similarity(String, String) - Method in class weka.deduping.metrics.AffineMetric
Returns a similarity estimate between two strings.
similarity(String, String) - Method in class weka.deduping.metrics.AffineProbMetric
Returns a similarity estimate between two strings.
similarity(Instance, Instance) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Returns similarity between two records
similarity(Instance, Instance) - Method in class weka.deduping.metrics.InstanceMetric
Returns a similarity estimate between two instances.
similarity(String, String) - Method in class weka.deduping.metrics.JaccardMetric
Compute similarity between two strings
similarity(String, String) - Method in class weka.deduping.metrics.KernelVSMetric
Compute similarity between two strings
similarity(String, String) - Method in class weka.deduping.metrics.StringMetric
Compute a measure of similarity between two strings
similarity(Instance, Instance) - Method in class weka.deduping.metrics.SumInstanceMetric
Returns similarity between two instances without using the weights.
similarity(String, String) - Method in class weka.deduping.metrics.VectorSpaceMetric
Compute similarity between two strings
similarityInCombinedModel(int, int) - Method in class weka.clusterers.MPCKMeans
finds similarity between instance and centroid in Combined Objective Model
similarityInPottsModel(int, int) - Method in class weka.clusterers.MPCKMeans
finds similarity between instance and centroid in Potts Model
similarityInternal(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between two instances.
similarityNonSparse(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between a two non-sparse instances
similarityNonSparseNonWeighted(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between a two non-sparse instances
similarityNonWeighted(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetric
Returns a similarity estimate between two instances without using the weights.
similarityNonWeighted(Instance, Instance) - Method in class weka.core.metrics.BarHillelMetricMatlab
Returns a similarity estimate between two instances without using the weights.
similarityNonWeighted(Instance, Instance) - Method in class weka.core.metrics.KL
Returns a similarity estimate between two instances without using the weights.
similarityNonWeighted(Instance, Instance) - Method in class weka.core.metrics.Metric
Returns similarity value between two instances without using the weights.
similarityNonWeighted(Instance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between two instances without using the weights.
similarityNonWeighted(Instance, Instance) - Method in class weka.core.metrics.WeightedEuclidean
Returns a similarity estimate between two instances without using the weights.
similarityNonWeighted(Instance, Instance) - Method in class weka.core.metrics.WeightedMahalanobis
Returns a similarity estimate between two instances without using the weights.
similarityNonWeighted(Instance, Instance) - Method in class weka.core.metrics.XingMetric
Returns a similarity estimate between two instances without using the weights.
similaritySparse(SparseInstance, SparseInstance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between two sparse instances.
similaritySparseNonSparse(SparseInstance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between a non-sparse instance and a sparse instance
similaritySparseNonSparseNonWeighted(SparseInstance, Instance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between a non-sparse instance and a sparse instance
similaritySparseNonWeighted(SparseInstance, SparseInstance) - Method in class weka.core.metrics.WeightedDotP
Returns a dot product similarity value between two sparse instances.
singleVariance(double, double, double) - Method in class weka.classifiers.trees.REPTree.Tree
Computes the variance for a single set
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).
size() - Method in class weka.classifiers.CostMatrix
Gets the size of the matrix.
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.
size() - Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable
Returns the number of keys in this hashtable.
size() - Method in class weka.classifiers.rules.JRip.RipperRule
the number of antecedents of the rule
size() - Method in class weka.classifiers.rules.Rule
The size of the rule.
size() - Method in 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.deduping.blocking.Blocking
Return the number of tokens indexed.
size() - Method in class weka.deduping.metrics.HashMapVector
Returns the number of tokens in the vector.
size() - Method in class weka.deduping.metrics.JaccardMetric
Return the number of tokens indexed.
size() - Method in class weka.deduping.metrics.KernelVSMetric
Return the number of tokens indexed.
size() - Method in class weka.deduping.metrics.VectorSpaceMetric
Return the number of tokens indexed.
sm(double, double) - Static method in class weka.core.Utils
Tests if a is smaller than b.
smOrEq(double, double) - Static method in class weka.core.Utils
Tests if a is smaller or equal to b.
smoothDistribution(double[]) - Method in class weka.classifiers.meta.ActiveDecorate
 
smoothDistribution(double[]) - Method in class weka.classifiers.meta.Fable
 
smoothingOriginal(double, double, double) - Static method in class weka.classifiers.trees.m5.RuleNode
Applies the m5 smoothing procedure to a prediction
softLabelClasses(SoftClassifiedInstance, List) - Method in class weka.classifiers.bayes.SemiSupEM
Soft label inst as being equally likely to be in an of the given classes
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
son(int) - Method in class weka.classifiers.rules.part.ClassifierDecList
Method just exists to make program easier to read.
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(int) - Method in class weka.core.Instances
Sorts the instances based on an attribute.
sort(Attribute) - Method in class weka.core.Instances
Sorts the instances based on an attribute.
sort(int[]) - Static method in class weka.core.Utils
Sorts a given array of integers in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
sort(double[]) - Static method in class weka.core.Utils
Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.
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
sourceClass(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
 
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.BinC45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.C45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
 
sourceExpression(int, Instances) - Method in class weka.classifiers.trees.j48.NoSplit
Returns a string containing java source code equivalent to the test made at this node.
sparseDataTipText() - Method in class weka.experiment.InstanceQuery
Returns the tip text for this property
sphere - Variable in class weka.classifiers.lazy.kstar.KStarWrapper
used/reused to hold the sphere size
split(Instances) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Splits the given set of instances into subsets.
split() - Method in class weka.classifiers.trees.m5.RuleNode
Finds an attribute and split point for this node
splitAtt() - Method in class weka.classifiers.trees.m5.RuleNode
Get the index of the splitting attribute for this node
splitAttr() - Method in class weka.classifiers.trees.m5.CorrelationSplitInfo
Returns the attribute used in this split
splitAttr() - Method in interface weka.classifiers.trees.m5.SplitEvaluate
Returns the attribute used in this split
splitAttr() - Method in class weka.classifiers.trees.m5.YongSplitInfo
Returns the attribute used in this split
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit
Computes entropy for given distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.EntropySplitCrit
Computes entropy of test distribution with respect to training distribution.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit
This method is a straightforward implementation of the gain ratio criterion for the given distribution.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.GainRatioSplitCrit
This method computes the gain ratio in the same way C4.5 does.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
This method is a straightforward implementation of the information gain criterion for the given distribution.
splitCritValue(Distribution, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double) - Method in class weka.classifiers.trees.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
Computes result of splitting criterion for given distribution.
splitCritValue(Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions.
splitCritValue(Distribution, Distribution, int) - Method in class weka.classifiers.trees.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions and given number of classes.
splitCritValue(Distribution, Distribution, Distribution) - Method in class weka.classifiers.trees.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions and given default distribution.
splitData(int[][][], double[][][], int, double, int[][], double[][], Instances) - Method in class weka.classifiers.trees.REPTree.Tree
Splits instances into subsets.
splitData(int[][][], double[][][], int, double, int[][], double[][], double[][], Instances) - Method in class weka.classifiers.trees.RandomTree
Splits instances into subsets.
splitEnt(Distribution) - Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit
Computes entropy after splitting without considering the class values.
splitEvaluatorTipText() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.CrossValidationResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.ExtractionResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
splitEvaluatorTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
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.
splitOptions(String) - Static method in class weka.gui.SimpleCLI
Split up a string containing options into an array of strings, one for each option.
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
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
squeezeIn(int, int) - Method in class weka.core.DynamicArrayOfPosInt
Sqeezes in the value in the specified position in the array.
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.
start() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Start the plotting thread
startAssociator() - Method in class weka.gui.explorer.AssociationsPanel
Starts running the currently configured associator with the current settings.
startAttributeSelection() - Method in class weka.gui.explorer.AttributeSelectionPanel
Starts running the currently configured attribute evaluator and search method.
startClassifier() - Method in class weka.gui.explorer.ClassifierPanel
Starts running the currently configured classifier with the current settings.
startClusterer() - Method in class weka.gui.explorer.ClustererPanel
Starts running the currently configured clusterer with the current settings.
startLoading() - Method in class weka.gui.beans.Loader
Start loading data
startSetTipText() - Method in class weka.attributeSelection.BestFirst
Returns the tip text for this property
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.GeneticSearch
Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.RandomSearch
Returns the tip text for this property
startSetTipText() - Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
statusMessage(String) - Method in class weka.gui.LogPanel
Sends the supplied message to the status line.
statusMessage(String) - Method in interface weka.gui.Logger
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 class weka.gui.experiment.RunPanel
Sends the supplied message to the log panel status line.
stdDev(int, Instances) - Static method in class weka.classifiers.trees.m5.Rule
Returns the standard deviation value of the supplied attribute index.
stdDev - Variable in class weka.experiment.Stats
The std deviation of values at the last calculateDerived() call
stem(String) - Method in class weka.deduping.metrics.Tokenizer
Stem a given token
stepSizeTipText() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
stepSizeTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Returns the tip text for this property
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
stop() - Method in class weka.gui.beans.AbstractEvaluator
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTestSetProducer
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.AbstractTrainingSetProducer
Stop any processing that the bean might be doing.
stop() - Method in interface weka.gui.beans.BeanCommon
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.ClassAssigner
 
stop() - Method in class weka.gui.beans.Classifier
Stop any classifier action
stop() - Method in class weka.gui.beans.ClassifierPerformanceEvaluator
Try and stop any action
stop() - Method in class weka.gui.beans.CrossValidationFoldMaker
Stop any action
stop() - Method in class weka.gui.beans.Filter
Stop all action if possible
stop() - Method in class weka.gui.beans.IncrementalClassifierEvaluator
Stop all action
stop() - Method in class weka.gui.beans.StripChart
Stop any processing that the bean might be doing.
stop() - Method in class weka.gui.beans.TestSetMaker
 
stop() - Method in class weka.gui.beans.TrainTestSplitMaker
Stop processing
stop() - Method in class weka.gui.beans.TrainingSetMaker
Stop any action
stopAssociator() - Method in class weka.gui.explorer.AssociationsPanel
Stops the currently running Associator (if any).
stopAttributeSelection() - Method in class weka.gui.explorer.AttributeSelectionPanel
Stops the currently running attribute selection (if any).
stopClassifier() - Method in class weka.gui.explorer.ClassifierPanel
Stops the currently running classifier (if any).
stopClusterer() - Method in class weka.gui.explorer.ClustererPanel
Stops the currently running clusterer (if any).
stopPlotting() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel
Stop the plotting thread
store(double, double, double) - Method in class weka.classifiers.lazy.kstar.KStarCache
Stores the specified values in the cahce table for easy retrieval.
str1 - Variable in class weka.deduping.StringPair
 
str2 - Variable in class weka.deduping.StringPair
 
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).
string - Variable in class weka.deduping.blocking.InstanceReference
The amalgamated instance string
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.Edge
This will calculate how large a rectangle using the FontMetrics passed that the lines of the label will take up
stringSize(FontMetrics) - Method in class weka.gui.treevisualizer.Node
This will return the width and height of the rectangle that the text will fit into.
stringValue(int) - Method in class weka.core.Instance
Returns the string value of a nominal, string, or date attribute for the instance.
stringValue(Attribute) - Method in class weka.core.Instance
Returns the string value of a nominal, string, or date attribute for the instance.
stripAffixes(String) - Method in class weka.deduping.metrics.Porter
Takes a String as input and returns its stem as a String.
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.
substract(AlgVector) - Method in class weka.clusterers.AlgVector
Returns the difference of this vector minus another.
substract(AlgVector) - Method in class weka.core.AlgVector
Returns the difference of this vector minus another.
subtract(ItemSet) - Method in class weka.associations.ItemSet
Subtracts an item set from another one.
subtract(Distribution) - Method in class weka.classifiers.trees.j48.Distribution
Subtracts the given distribution from this one.
subtract(HashMapVector) - Method in class weka.deduping.metrics.HashMapVector
Destructively subtract the given vector from the current vector
subtract(double, double) - Method in class weka.experiment.PairedStats
Removes an observed pair of values.
subtract(double) - Method in class weka.experiment.Stats
Removes a value to the observed values (no checking is done that the value being removed was actually added).
subtract(double, double) - Method in class weka.experiment.Stats
Subtracts a value that has been seen n times from the observed values
subvector(int, int) - Method in class weka.classifiers.functions.pace.DoubleVector
Returns a subvector.
subvector(IntVector) - 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.IntVector
Returns a subvector as indexed by an IntVector.
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.
sum - Variable in class weka.experiment.Stats
The sum of values seen
sum2() - Method in class weka.classifiers.functions.pace.DoubleVector
Returns the squared sum of all elements in the vector.
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
sum2(boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Squared sum of columns or rows of a matrix
sumInstances(Instance, Instance) - Method in class weka.clusterers.MPCKMeans
Finds sum of 2 instances (handles sparse and non-sparse)
sumInstances(Instance, Instance) - Method in class weka.clusterers.PCKMeans
Finds sum of 2 instances (handles sparse and non-sparse)
sumInstances(Instance, Instance) - Method in class weka.clusterers.PCSoftKMeans
Finds sum of 2 instances (handles sparse and non-sparse)
sumInstances(Instance, Instance) - Method in class weka.clusterers.SeededKMeans
Finds sum of instances (handles sparse and non-sparse)
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
support() - Method in class weka.associations.ItemSet
Outputs the support for an item set.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.ChisqMixture
Contructs the set of support points for mixture estimation.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.MixtureDistribution
Contructs the set of support points for mixture estimation.
supportPoints(DoubleVector, int) - Method in class weka.classifiers.functions.pace.NormalMixture
Contructs the set of support points for mixture estimation.
supportThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
supportsCustomEditor() - Method in class weka.gui.CostMatrixEditor
Indicates whether the cost matrix can be edited in a GUI, which it can.
supportsCustomEditor() - Method in class weka.gui.FileEditor
Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.GenericArrayEditor
Returns true because we do support a custom editor.
supportsCustomEditor() - Method in class weka.gui.GenericObjectEditor
Returns true because we do support a custom editor.
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
swap(int, int) - Method in class weka.core.FastVector
Swaps two elements in the vector.
switchToAdvanced(Experiment) - Method in class weka.gui.experiment.SetupModePanel
Switches to the advanced setup mode.
switchToLegend() - Method in class weka.gui.visualize.VisualizePanel.PlotPanel
Remove the attibute panel and replace it with the legend panel
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.
synopsis() - Method in class weka.core.Option
Returns the option's synopsis.

T

TAGS_ALGORITHM - Static variable in class weka.clusterers.MPCKMeans
 
TAGS_ALGORITHM - Static variable in class weka.clusterers.PCKMeans
 
TAGS_ALGORITHM - Static variable in class weka.clusterers.PCSoftKMeans
 
TAGS_ALGORITHM - Static variable in class weka.clusterers.SeededKMeans
 
TAGS_APPROACH - Static variable in class weka.attributeSelection.MatlabICA
 
TAGS_ATTRIBUTETYPE - Static variable in class weka.filters.unsupervised.attribute.RemoveType
Tag allowing selection of attribute type to delete
TAGS_CLUSTERING_MODE - Static variable in class weka.extraction.ClusteringExtractor
 
TAGS_CONVERSION - Static variable in class weka.core.metrics.BarHillelMetric
 
TAGS_CONVERSION - Static variable in class weka.core.metrics.BarHillelMetricMatlab
 
TAGS_CONVERSION - Static variable in class weka.core.metrics.KL
 
TAGS_CONVERSION - Static variable in class weka.core.metrics.WeightedDotP
 
TAGS_CONVERSION - Static variable in class weka.core.metrics.WeightedEuclidean
 
TAGS_CONVERSION - Static variable in class weka.core.metrics.WeightedMahalanobis
 
TAGS_CONVERSION - Static variable in class weka.core.metrics.XingMetric
 
TAGS_CONVERSION - Static variable in class weka.deduping.metrics.AffineMetric
 
TAGS_CONVERSION - Static variable in class weka.deduping.metrics.AffineProbMetric
 
TAGS_CONVERSION - Static variable in class weka.deduping.metrics.JaccardMetric
 
TAGS_CONVERSION - Static variable in class weka.deduping.metrics.KernelVSMetric
 
TAGS_CONVERSION - Static variable in class weka.deduping.metrics.VectorSpaceMetric
 
TAGS_ENGINE_TYPE - Static variable in class weka.clusterers.assigners.LPAssigner
 
TAGS_ESTIMATOR - Static variable in class weka.classifiers.functions.pace.PaceRegression
 
TAGS_EVAL - Static variable in class weka.classifiers.meta.ThresholdSelector
 
TAGS_FILTER - Static variable in class weka.classifiers.functions.SMO
 
TAGS_FOLD_CREATION_MODE - Static variable in class weka.experiment.DedupingPRCurveCVResultProducer
 
TAGS_FOLD_CREATION_MODE - Static variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
TAGS_FUNCTION - Static variable in class weka.attributeSelection.MatlabICA
 
TAGS_KERNEL_TYPE - Static variable in class weka.classifiers.sparse.SVMlight
 
TAGS_LINKING - Static variable in class weka.clusterers.HAC
 
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.meta.CostSensitiveClassifier
 
TAGS_MATRIX_SOURCE - Static variable in class weka.classifiers.meta.MetaCost
 
TAGS_METHOD - Static variable in class weka.classifiers.meta.MultiClassClassifier
 
TAGS_MISSING - Static variable in class weka.classifiers.lazy.kstar.KStar
Define possible missing value handling methods
TAGS_NEG_MODE - Static variable in class weka.deduping.PairwiseSelector
 
TAGS_OPTIMIZE - Static variable in class weka.classifiers.meta.ThresholdSelector
 
TAGS_ORDERING - Static variable in class weka.clusterers.PCKMeans
 
TAGS_PAIR_SELECTION_MODE - Static variable in class weka.core.metrics.HardPairwiseSelector
 
TAGS_POS_MODE - Static variable in class weka.deduping.PairwiseSelector
 
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.ADTree
 
TAGS_SEEDING - Static variable in class weka.clusterers.SeededKMeans
 
TAGS_SELECTION - Static variable in class weka.associations.Apriori
 
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.classifiers.functions.LinearRegression
 
TAGS_SMOOTHING - Static variable in class weka.core.metrics.KL
 
TAGS_STRING_PAIR_MODE - Static variable in class weka.deduping.PairwiseSelector
 
TAGS_SVM_MODE - Static variable in class weka.classifiers.sparse.SVMlight
 
TAGS_TRAINING - Static variable in class weka.clusterers.MPCKMeans
 
TAGS_WEIGHTING - Static variable in class weka.classifiers.lazy.IBk
 
TAGS_WEIGHTING - Static variable in class weka.classifiers.sparse.IBkMetric
 
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.CostCurve
 
THRESHOLD_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.ActiveLearningCurveCVResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.CrossValidationResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.DedupingPRCurveCVResultProducerSplit
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.ExtractionResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.LearningCurveCrossValidationResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.NoiseCurveCrossValidationResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.RandomSplitResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.SemiSupCrossValidationResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.SemiSupLearningCurveCVResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
 
TIMESTAMP_FIELD_NAME - Static variable in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
 
TO_BE_RUN - Static variable in class weka.experiment.TaskStatusInfo
 
TP_RATE_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
 
TRAINING_EXTERNAL - Static variable in class weka.clusterers.MPCKMeans
 
TRAINING_INTERNAL - Static variable in class weka.clusterers.MPCKMeans
 
TRAINING_NONE - Static variable in class weka.clusterers.MPCKMeans
Possible metric training
TRIANGLEDOWN_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
TRIANGLEUP_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
TRUE_NEG_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
 
TRUE_POS_NAME - Static variable in class weka.classifiers.evaluation.ThresholdCurve
 
TYPE_CROSSVALIDATION_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
TYPE_FIXEDSPLIT_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
TYPE_RANDOMSPLIT_TEXT - Static variable in class weka.gui.experiment.SimpleSetupPanel
 
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.
Task - interface weka.experiment.Task.
Interface to something that can be remotely executed as a task.
TaskLogger - interface weka.gui.TaskLogger.
Interface for objects that display log and display information on running tasks.
TaskStatusInfo - class weka.experiment.TaskStatusInfo.
A class holding information for tasks being executed on RemoteEngines.
TaskStatusInfo() - Constructor for class weka.experiment.TaskStatusInfo
 
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.)

TestEnsembleClassifier - class weka.classifiers.meta.TestEnsembleClassifier.
This class is for testing Ensemble evaluation
TestEnsembleClassifier() - Constructor for class weka.classifiers.meta.TestEnsembleClassifier
 
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
TextSource - class weka.datagenerators.TextSource.
Reads a collection of text documents and transforms them into sparse vectors.
TextSource() - Constructor for class weka.datagenerators.TextSource
 
TextSource.DataRow - class weka.datagenerators.TextSource.DataRow.
Sparse map data row structure with public hash map.
TextSource.DataRow() - Constructor for class weka.datagenerators.TextSource.DataRow
 
TextSource.Int - class weka.datagenerators.TextSource.Int.
A simpler wrapper for int than Integer.
TextSource.Int(int) - Constructor for class weka.datagenerators.TextSource.Int
 
TextSource.Real - class weka.datagenerators.TextSource.Real.
A simpler wrapper for double than Double.
TextSource.Real(double) - Constructor for class weka.datagenerators.TextSource.Real
 
TextSource.Table - class weka.datagenerators.TextSource.Table.
Table that allows incremental addition of attributes.
TextSource.Table(TextSource) - Constructor for class weka.datagenerators.TextSource.Table
 
TextSource.Token - class weka.datagenerators.TextSource.Token.
Information about a particular token.
TextSource.Token(String, TextSource.Int) - Constructor for class weka.datagenerators.TextSource.Token
 
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
 
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
 
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
 
TokenInfo - class weka.deduping.metrics.TokenInfo.
A lightweight object for storing information about a token (a.k.a word, term) in an inverted index.
TokenInfo() - Constructor for class weka.deduping.metrics.TokenInfo
Create an initially empty data structure
TokenInstanceOccurrence - class weka.deduping.blocking.TokenInstanceOccurrence.
A lightweight object for storing information about an occurrence of a token (a.k.a word, term) in a record.
TokenInstanceOccurrence(InstanceReference, int) - Constructor for class weka.deduping.blocking.TokenInstanceOccurrence
Create an occurrence with these values
TokenOccurrence - class weka.deduping.metrics.TokenOccurrence.
A lightweight object for storing information about an occurrence of a token (a.k.a word, term) in a Document.
TokenOccurrence(StringReference, int) - Constructor for class weka.deduping.metrics.TokenOccurrence
Create an occurrence with these values
TokenString - class weka.deduping.metrics.TokenString.
 
TokenString(String) - Constructor for class weka.deduping.metrics.TokenString
 
Tokenizer - class weka.deduping.metrics.Tokenizer.
This abstract class defines a tokenizer that turns strings into HashMapVectors
Tokenizer() - Constructor for class weka.deduping.metrics.Tokenizer
 
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
 
TrainingPair - class weka.core.metrics.TrainingPair.
This is a basic class for a distance training pair
TrainingPair(Instance, Instance, boolean, double) - Constructor for class weka.core.metrics.TrainingPair
 
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
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.
TreeVisualizer - class weka.gui.treevisualizer.TreeVisualizer.
Class for displaying a Node structure in Swing.
TreeVisualizer(TreeDisplayListener, String, NodePlace) - Constructor for class weka.gui.treevisualizer.TreeVisualizer
Constructs Displayer to display a tree provided in a dot format.
TreeVisualizer(TreeDisplayListener, Node, NodePlace) - Constructor for class weka.gui.treevisualizer.TreeVisualizer
Constructs Displayer with the specified Node as the top of the tree, and uses the NodePlacer to place the Nodes.
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.
tableExists(String) - Method in class weka.experiment.DatabaseUtils
Checks that a given table exists.
taskFinished() - Method in class weka.gui.LogPanel
Record a task ending
taskFinished() - Method in interface weka.gui.TaskLogger
Tells the task logger that a task has completed
taskFinished() - Method in class weka.gui.WekaTaskMonitor
Tells the panel that a task has completed
taskStarted() - Method in class weka.gui.LogPanel
Record the starting of a new task
taskStarted() - Method in interface weka.gui.TaskLogger
Tells the task logger that a new task has been started
taskStarted() - Method in class weka.gui.WekaTaskMonitor
Tells the panel that a new task has been started
tauVal(double[][]) - Static method in class weka.core.ContingencyTables
Computes Goodman and Kruskal's tau-value for a contingency table.
tempCnt - Variable in class weka.classifiers.lazy.LBR
 
tempSubInstances - Variable in class weka.classifiers.lazy.LBR
 
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.
testCase() - Static method in class weka.clusterers.PCKMeans
 
testCase() - Static method in class weka.clusterers.PCSoftKMeans
 
testEigen(Matrix, double[], boolean) - Method in class weka.core.Matrix
Test eigenvectors and eigenvalues.
testExtractor(Instances, HashMap) - Method in class weka.extraction.ClusteringExtractor
Perform extraction on a set of data.
testExtractor(Instances, HashMap) - Method in class weka.extraction.Extractor
Perform extraction on a set of data.
testWRTZeroR(Classifier, Evaluation, Instances, Instances) - Method in class weka.classifiers.CheckClassifier
Determine whether the scheme performs worse than ZeroR during testing
testsPerClassType(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Run a battery of tests for a given class attribute type
theoryDL(int) - Method in class weka.classifiers.rules.RuleStats
The description length of the theory for a given rule.
thresholdTipText() - Method in class weka.attributeSelection.ForwardSelection
Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.Ranker
Returns the tip text for this property
thresholdTipText() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns the tip text for this property
thresholdTipText() - Method in class weka.filters.unsupervised.instance.RemoveMisclassified
Returns the tip text for this property
times(double) - Method in class weka.classifiers.functions.pace.DoubleVector
Multiplies a scalar
times(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector
Multiplies another DoubleVector element by element
times(double) - Method in class weka.classifiers.functions.pace.Matrix
Multiply a matrix by a scalar, C = s*A
times(Matrix) - Method in class weka.classifiers.functions.pace.Matrix
Linear algebraic matrix multiplication, A * B
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.
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(DoubleVector) - Method in class weka.classifiers.functions.pace.DoubleVector
Multiplies another DoubleVector element by element in place
timesEquals(double) - Method in class weka.classifiers.functions.pace.Matrix
Multiply a matrix by a scalar in place, A = s*A
toArray() - Method in class weka.core.FastVector
Returns all the elements of this vector as an array
toClassDetailsString() - Method in class weka.classifiers.EnsembleEvaluation
 
toClassDetailsString(String) - Method in class weka.classifiers.EnsembleEvaluation
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() - Method in class weka.classifiers.Evaluation
 
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.
toCumulativeMarginDistributionString() - Method in class weka.classifiers.EnsembleEvaluation
Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
toCumulativeMarginDistributionString() - Method in class weka.classifiers.Evaluation
Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
toDoubleArray() - Method in class weka.core.BinarySparseInstance
Returns the values of each attribute as an array of doubles.
toDoubleArray() - Method in class weka.core.Instance
Returns the values of each attribute as an array of doubles.
toDoubleArray() - Method in class weka.core.SparseInstance
Returns the values of each attribute as an array of doubles.
toGraph(StringBuffer, int, REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Outputs one node for graph.
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.
toMatrixString() - Method in class weka.classifiers.EnsembleEvaluation
Calls toMatrixString() with a default title.
toMatrixString(String) - Method in class weka.classifiers.EnsembleEvaluation
Outputs the performance statistics as a classification confusion matrix.
toMatrixString() - Method in class weka.classifiers.Evaluation
Calls toMatrixString() with a default title.
toMatrixString(String) - Method in class weka.classifiers.Evaluation
Outputs the performance statistics as a classification confusion matrix.
toMatrixString(String) - Method in class weka.clusterers.SemiSupClustererEvaluation
Outputs the performance statistics as a classification confusion matrix.
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.meta.MultiBoostAB
Returns the boosted model as Java source code.
toSource(String) - Method in class weka.classifiers.meta.QBoost
Returns the boosted model as Java source code.
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.j48.ClassifierTree
Returns source code for the tree as an if-then statement.
toSource(String) - Method in class weka.classifiers.trees.j48.J48
Returns tree as an if-then statement.
toString() - Method in class weka.associations.Apriori
Outputs the size of all the generated sets of itemsets and the rules.
toString(Instances) - Method in class weka.associations.ItemSet
Returns the contents of an item set as a string.
toString() - Method in class weka.attributeSelection.BestFirst.Link2
 
toString() - Method in class weka.attributeSelection.BestFirst
returns a description of the search as a String
toString() - Method in class weka.attributeSelection.CfsSubsetEval
returns a string describing CFS
toString() - Method in class weka.attributeSelection.ChiSquaredAttributeEval
Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns a string describing classifierSubsetEval
toString(Instances, int) - Method in class weka.attributeSelection.ConsistencySubsetEval.hashKey
Convert a hash entry to a string
toString() - Method in class weka.attributeSelection.ConsistencySubsetEval
returns a description of the evaluator
toString() - Method in class weka.attributeSelection.ExhaustiveSearch
prints a description of the search
toString() - Method in class weka.attributeSelection.ForwardSelection
returns a description of the search.
toString() - Method in class weka.attributeSelection.GainRatioAttributeEval
Return a description of the evaluator
toString() - Method in class weka.attributeSelection.GeneticSearch
returns a description of the search
toString() - Method in class weka.attributeSelection.InfoGainAttributeEval
Describe the attribute evaluator
toString() - Method in class weka.attributeSelection.MatlabICA
Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.MatlabNMF
Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.MatlabPCA
Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.OneRAttributeEval
Return a description of the evaluator
toString() - Method in class weka.attributeSelection.PrincipalComponents
Returns a description of this attribute transformer
toString() - Method in class weka.attributeSelection.RaceSearch
 
toString() - Method in class weka.attributeSelection.RandomSearch
prints a description of the search
toString() - Method in class weka.attributeSelection.RankSearch
returns a description of the search as a String
toString() - Method in class weka.attributeSelection.Ranker
returns a description of the search as a String
toString() - Method in class weka.attributeSelection.ReliefFAttributeEval
Return a description of the ReliefF attribute evaluator.
toString() - Method in class weka.attributeSelection.SVMAttributeEval
Return a description of the evaluator
toString() - Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Return a description of the evaluator
toString() - Method in class weka.attributeSelection.WrapperSubsetEval
Returns a string describing the wrapper
toString() - Method in class weka.classifiers.BVDecompose
Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.RegressionBVDecompose
Returns description of the bias-variance decomposition results.
toString() - Method in class weka.classifiers.bayes.BayesNet
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.NaiveBayes
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.evaluation.ConfusionMatrix
Calls toString() with a default title.
toString(String) - Method in class weka.classifiers.evaluation.ConfusionMatrix
Outputs the performance statistics as a classification confusion matrix.
toString() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.NumericPrediction
Gets a human readable representation of this prediction.
toString() - Method in class weka.classifiers.evaluation.TwoClassStats
Returns a string containing the various performance measures for the current object
toString() - Method in class weka.classifiers.functions.LeastMedSq
Returns a string representing the best LinearRegression classifier found.
toString() - Method in class weka.classifiers.functions.LinearRegression
Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.functions.Logistic
Prints the model.
toString() - Method in class weka.classifiers.functions.SMO
Prints out the classifier.
toString() - Method in class weka.classifiers.functions.UnivariateLinearRegression
 
toString() - Method in class weka.classifiers.functions.VotedPerceptron
Returns textual description of classifier.
toString() - Method in class weka.classifiers.functions.Winnow
Returns textual description of the classifier.
toString() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
toString() - Method in class weka.classifiers.functions.pace.ChisqMixture
Converts to a string
toString() - Method in class weka.classifiers.functions.pace.DiscreteFunction
Converts the discrete function to string.
toString() - Method in class weka.classifiers.functions.pace.DoubleVector
Convert the DoubleVecor to a string
toString(int, boolean) - Method in class weka.classifiers.functions.pace.DoubleVector
Convert the DoubleVecor to a string
toString() - Method in class weka.classifiers.functions.pace.IntVector
Converts the IntVecor to a string
toString(int, boolean) - Method in class weka.classifiers.functions.pace.IntVector
Convert the IntVecor to a string
toString() - Method in class weka.classifiers.functions.pace.MixtureDistribution
Converts to a string
toString() - Method in class weka.classifiers.functions.pace.NormalMixture
Converts to a string
toString() - Method in class weka.classifiers.functions.pace.PaceMatrix
Converts matrix to string
toString(int, boolean) - Method in class weka.classifiers.functions.pace.PaceMatrix
Converts matrix to string
toString() - Method in class weka.classifiers.functions.pace.PaceRegression
Outputs the linear regression model as a string.
toString() - Method in class weka.classifiers.lazy.IB1
Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.IBk
Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.LBR
Returns a description of the classifier.
toString() - Method in class weka.classifiers.lazy.LWR
Returns a description of this classifier.
toString() - Method in class weka.classifiers.lazy.kstar.KStar
Returns a description of this classifier.
toString() - Method in class weka.classifiers.meta.ActiveDecorate
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.AdditiveRegression
Returns textual description of the classifier.
toString() - Method in class weka.classifiers.meta.AttributeSelectedClassifier
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Bagging
Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.CVParameterSelection.CVParameter
Returns a CVParameter as a string.
toString() - Method in class weka.classifiers.meta.CVParameterSelection
Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.meta.ClassificationViaRegression
Prints the classifiers.
toString() - Method in class weka.classifiers.meta.CostSensitiveClassifier
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.Crate
Returns description of the Crate classifier.
toString() - Method in class weka.classifiers.meta.DEC
Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.Decorate
Returns description of the Decorate classifier.
toString() - Method in class weka.classifiers.meta.DistributionMetaClassifier
Returns a description of the classifier.
toString() - Method in class weka.classifiers.meta.Fable
Returns description of the Decorate classifier.
toString() - Method in class weka.classifiers.meta.FilteredClassifier
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.MetaCost
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.MultiBoostAB
Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.MultiClassClassifier
Prints the classifiers.
toString() - Method in class weka.classifiers.meta.MultiScheme
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.OrdinalClassClassifier
Prints the classifiers.
toString() - Method in class weka.classifiers.meta.QBag
Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.QBoost
Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
toString() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
Returns description of the boosted classifier.
toString() - Method in class weka.classifiers.meta.RegressionByDiscretization
Returns a description of the classifier.
toString() - Method in class weka.classifiers.meta.SemiSupDecorate
Returns description of the bagged classifier.
toString() - Method in class weka.classifiers.meta.Stacking
Output a representation of this classifier
toString() - Method in class weka.classifiers.meta.ThresholdSelector
Returns description of the cross-validated classifier.
toString() - Method in class weka.classifiers.misc.HyperPipes
Returns a description of this classifier.
toString() - Method in class weka.classifiers.misc.Prototype
Returns a description of the classifier.
toString() - Method in class weka.classifiers.misc.PrototypeMetric
Returns a description of the classifier.
toString() - Method in class weka.classifiers.misc.VFI
Returns a description of this classifier.
toString(String, String) - Method in class weka.classifiers.rules.ConjunctiveRule
Prints this rule with the specified class label
toString() - Method in class weka.classifiers.rules.ConjunctiveRule
Prints this rule
toString() - Method in class weka.classifiers.rules.DecisionTable.Link
Returns string representation.
toString(Instances, int) - Method in class weka.classifiers.rules.DecisionTable.hashKey
Convert a hash entry to a string
toString() - Method in class weka.classifiers.rules.DecisionTable
Returns a description of the classifier.
toString(Attribute) - Method in class weka.classifiers.rules.JRip.RipperRule
Prints this rule
toString() - Method in class weka.classifiers.rules.JRip
Prints the all the rules of the rule learner.
toString() - Method in class weka.classifiers.rules.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.Ridor
Prints the all the rules of the rule learner.
toString() - Method in class weka.classifiers.rules.ZeroR
Returns a description of the classifier.
toString() - Method in class weka.classifiers.rules.part.ClassifierDecList
Prints rules.
toString() - Method in class weka.classifiers.rules.part.MakeDecList
Outputs the classifier into a string.
toString() - Method in class weka.classifiers.rules.part.PART
Returns a description of the classifier
toString() - Method in class weka.classifiers.sparse.IBkMetric
Returns a description of this classifier.
toString() - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Returns a description of the classifier.
toString() - Method in class weka.classifiers.sparse.SVMlight
Returns 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.Id3
Prints the decision tree using the private toString method from below.
toString(int, REPTree.Tree) - Method in class weka.classifiers.trees.REPTree.Tree
Recursively outputs the tree.
toString() - Method in class weka.classifiers.trees.REPTree
Outputs the decision tree.
toString() - Method in class weka.classifiers.trees.RandomForest
Outputs a description of this classifier.
toString() - Method in class weka.classifiers.trees.RandomTree
Outputs the decision tree.
toString(int) - Method in class weka.classifiers.trees.RandomTree
Recursively outputs the tree.
toString() - Method in class weka.classifiers.trees.UserClassifier
 
toString() - Method in class weka.classifiers.trees.adtree.ADTree
Returns a description of the classifier.
toString(PredictionNode, int) - Method in class weka.classifiers.trees.adtree.ADTree
Traverses the tree, forming a string that describes it.
toString() - Method in class weka.classifiers.trees.j48.ClassifierTree
Prints tree structure.
toString() - Method in class weka.classifiers.trees.j48.J48
Returns a description of the classifier.
toString() - Method in class weka.classifiers.trees.m5.Impurity
Converts an Impurity object to a string
toString() - Method in class weka.classifiers.trees.m5.M5Base
Returns a description of the classifier
toString() - Method in class weka.classifiers.trees.m5.Rule
Return a description of the m5 tree or rule
toString() - Method in class weka.classifiers.trees.m5.RuleNode
print the linear model at this node
toString() - Method in class weka.classifiers.trees.m5.Values
Converts the stats to a string
toString(Instances) - Method in class weka.classifiers.trees.m5.YongSplitInfo
Converts the spliting information to string
toString() - Method in class weka.clusterers.AlgVector
Converts a vector to a string
toString() - Method in class weka.clusterers.Cluster
 
toString() - Method in class weka.clusterers.Cobweb
Returns a description of the clusterer as a string.
toString() - Method in class weka.clusterers.DistributionMetaClusterer
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.InstancePair
returns string representation of InstancePair
toString() - Method in class weka.clusterers.MPCKMeans
return a string describing this clusterer
toString() - Method in class weka.clusterers.PCKMeans
return a string describing this clusterer
toString() - Method in class weka.clusterers.PCSoftKMeans
return a string describing this clusterer
toString() - Method in class weka.clusterers.SeededKMeans
return a string describing this clusterer
toString() - Method in class weka.clusterers.SimpleKMeans
return a string describing this clusterer
toString() - Method in class weka.clusterers.XMeans
Return a string describing this clusterer.
toString() - Method in class weka.core.AlgVector
Converts a vector to a string
toString() - Method in class weka.core.Attribute
Returns a description of this attribute in ARFF format.
toString() - Method in class weka.core.AttributeStats
Returns a human readable representation of this AttributeStats instance.
toString() - Method in class weka.core.BinarySparseInstance
Returns the description of one instance in sparse format.
toString() - Method in class weka.core.DistanceFunction
Converts a DistanceFunction object to a string
toString() - Method in class weka.core.DynamicArrayOfPosInt
Build a string representing this array.
toString() - Method in class weka.core.EuclideanDistance
Documents the content of an EuclideanDistance object in a string.
toString() - Method in class weka.core.Instance
Returns the description of one instance.
toString(int) - Method in class weka.core.Instance
Returns the description of one value of the instance as a string.
toString(Attribute) - Method in class weka.core.Instance
Returns the description of one value of the instance as a string.
toString() - Method in class weka.core.Instances
Returns the dataset as a string in ARFF format.
toString() - Method in class weka.core.KDTree
toString
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.Range
Constructs a representation of the current range.
toString() - Method in class weka.core.SparseInstance
Returns the description of one instance in sparse format.
toString() - Method in class weka.core.metrics.AttrEvalMetricLearner
Obtain a textual description of the metriclearner
toString() - Method in class weka.core.metrics.ClassifierMetricLearner
Obtain a textual description of the metriclearner
toString() - Method in class weka.core.metrics.GDMetricLearner
Obtain a textual description of the metriclearner
toString() - Method in class weka.datagenerators.Test
Returns the test represented by a string.
toString() - Method in class weka.datagenerators.TextSource.Int
 
toString() - Method in class weka.datagenerators.TextSource.Real
 
toString() - Method in class weka.deduping.blocking.InstanceReference
 
toString() - Method in class weka.deduping.metrics.HashMapVector
Return String of the vector showing the tokens and their weights
toString() - Method in class weka.deduping.metrics.StringReference
 
toString() - Method in class weka.deduping.metrics.TokenString
 
toString() - Method in class weka.estimators.DDConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.DKConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.DNConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.DiscreteEstimator
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.KKConditionalEstimator
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.MahalanobisEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.NDConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.NNConditionalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.NormalEstimator
Display a representation of this estimator
toString() - Method in class weka.estimators.PoissonEstimator
Display a representation of this estimator
toString() - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.AveragingResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.ClassifierSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.CrossValidationResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DatabaseResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DeduperSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.DedupingPRCurveCVResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.DedupingPRCurveCVResultProducerSplit
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.EnsembleClassifierSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.Experiment
Gets a string representation of the experiment configuration.
toString() - Method in class weka.experiment.ExtractionResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.ExtractionSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.LearningRateResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.NoiseCurveCrossValidationResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.PairedStats
Returns statistics on the paired comparison.
toString() - Method in class weka.experiment.PropertyNode
Returns a string description of this property.
toString() - Method in class weka.experiment.RandomSplitResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.RegressionSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.RemoteExperiment
Overides toString in Experiment
toString() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Returns a text description of the split evaluator.
toString() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
Gets a text descrption of the result producer.
toString() - Method in class weka.experiment.Stats
Returns a string summarising the stats so far.
toStringFormat() - Method in class weka.datagenerators.ClusterGenerator
Returns a string representing the dataset in the instance queue.
toStringFormat() - Method in class weka.datagenerators.Generator
Returns a string representing the dataset in the instance queue.
toSummaryString() - Method in class weka.classifiers.EnsembleEvaluation
Calls toSummaryString() with no title and no complexity stats
toSummaryString(boolean) - Method in class weka.classifiers.EnsembleEvaluation
Calls toSummaryString() with a default title.
toSummaryString(String, boolean) - Method in class weka.classifiers.EnsembleEvaluation
Outputs the performance statistics in summary form.
toSummaryString() - Method in class weka.classifiers.Evaluation
Calls toSummaryString() with no title and no complexity stats
toSummaryString(boolean) - Method in class weka.classifiers.Evaluation
Calls toSummaryString() with a default title.
toSummaryString(String, boolean) - Method in class weka.classifiers.Evaluation
Outputs the performance statistics in summary form.
toSummaryString() - Method in class weka.classifiers.meta.CVParameterSelection
 
toSummaryString() - Method in class weka.classifiers.rules.part.PART
Returns a superconcise version of the model
toSummaryString() - Method in class weka.classifiers.trees.j48.J48
Returns a superconcise version of the model
toSummaryString() - Method in class weka.clusterers.SemiSupClustererEvaluation
 
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.
tokenIDs - Variable in class weka.deduping.metrics.TokenString
 
tokenize(String) - Method in class weka.deduping.metrics.NGramTokenizer
Take a string and create a vector of n-gram tokens from it
tokenize(String) - Method in class weka.deduping.metrics.Tokenizer
Take a string and create a vector of tokens from it
tokenize(String) - Method in class weka.deduping.metrics.WordTokenizer
Take a string and create a vector of tokens from it
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
tokens - Variable in class weka.deduping.metrics.TokenString
 
toleranceParameterTipText() - Method in class weka.attributeSelection.SVMAttributeEval
Returns a tip text for this property suitable for display in the GUI
total() - Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of predictions that were made (actually the sum of the weights of predictions where the class value was known).
total() - Method in class weka.classifiers.trees.j48.Distribution
Returns total number of (possibly fractional) instances.
totalCost() - Method in class weka.classifiers.EnsembleEvaluation
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 weka.classifiers.Evaluation
Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
totalCount - Variable in class weka.core.AttributeStats
The total number of values (i.e.
trace() - Method in class weka.classifiers.functions.pace.Matrix
Matrix trace.
trailing - Variable in class weka.classifiers.functions.pace.ExponentialFormat
 
trailing - Variable in class weka.classifiers.functions.pace.FloatingPointFormat
 
trainCV(int, int) - Method in class weka.core.Instances
Creates the training set for one fold of a cross-validation on the dataset.
trainClusterer(Instances) - Method in class weka.clusterers.HAC
Train the clusterer using specified parameters
trainClusterer(Instances) - Method in class weka.clusterers.MPCKMeans
Train the clusterer using specified parameters
trainClusterer(Instances) - Method in class weka.clusterers.PCKMeans
Train the clusterer using specified parameters
trainClusterer(Instances) - Method in class weka.clusterers.PCSoftKMeans
Train the clusterer using specified parameters
trainClusterer(Instances) - Method in class weka.clusterers.SeededKMeans
Train the clusterer using specified parameters
trainClusterer(Instances) - Method in interface weka.clusterers.SemiSupClusterer
Train the clusterer using provided training data
trainDeduper(Deduper, Instances, Instances) - Method in class weka.deduping.DedupingEvaluation
Train a deduper on the supplied data
trainExtractor(Instances, Instances) - Method in class weka.extraction.ClusteringExtractor
Given training data, train the extractor
trainExtractor(Extractor, Instances, Instances) - Method in class weka.extraction.ExtractionEvaluation
Train an extractor on supplied data
trainExtractor(Instances, Instances) - Method in class weka.extraction.Extractor
Given training data, train the extractor
trainInstanceMetric(Instances, Instances) - Method in class weka.deduping.metrics.ClassifierInstanceMetric
Create a new metric for operating on specified instances
trainInstanceMetric(Instances, Instances) - Method in class weka.deduping.metrics.InstanceMetric
Create a new metric for operating on specified instances
trainInstanceMetric(Instances, Instances) - Method in class weka.deduping.metrics.SumInstanceMetric
Create a new metric for operating on specified instances
trainMetric(LearnableMetric, Instances) - Method in class weka.core.metrics.AttrEvalMetricLearner
Train a given met7ric using given training instances
trainMetric(LearnableMetric, Instances) - Method in class weka.core.metrics.ClassifierMetricLearner
Train a given metric using given training instances
trainMetric(LearnableMetric, Instances) - Method in class weka.core.metrics.GDMetricLearner
Train a given metric using given training instances
trainMetric(LearnableMetric, Instances) - Method in class weka.core.metrics.MatlabMetricLearner
Train a given metric using given training instances
trainMetric(LearnableMetric, Instances) - Method in class weka.core.metrics.MetricLearner
Train a given metric using given training instances
trainMetric(ArrayList) - Method in class weka.deduping.metrics.AffineProbMetric
Train the distance parameters using provided examples using EM
trainMetric(ArrayList) - Method in class weka.deduping.metrics.KernelVSMetric
Train the metric given a set of aligned strings
trainMetric(ArrayList) - Method in interface weka.deduping.metrics.LearnableStringMetric
Train a metric given a set of aligned strings
trainPercentTipText() - Method in class weka.experiment.RandomSplitResultProducer
Returns the tip text for this property
trainSVMlight() - Method in class weka.classifiers.sparse.SVMlight
Launch an SVM-light process assuming that the training data has been dumped
trainSelectionCommittee(Instances) - Method in class weka.classifiers.meta.ActiveDecorate
 
trainSelectionCommittee(Instances) - Method in class weka.classifiers.meta.Fable
 
trained() - Method in class weka.classifiers.sparse.SVMlight
Check whether the SVM has been trained
trainingTimeTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
transProb() - Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute
Calculates the probability of the indexed nominal attribute of the test instance transforming into the indexed nominal attribute of the training instance.
transProb() - Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute
Calculates the transformation probability of the attribute indexed "m_AttrIndex" in test instance "m_Test" to the same attribute in the train instance "m_Train".
transferInstances(Instances, Instances, HashMap, int) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Use current classifier to actively select specified number of instances to be transfered from the local to global pool
transferRandomInstances(Instances, Instances, HashMap, int) - Method in class weka.experiment.ActiveFeatureAcquisitionCVResultProducer
Randomly select specified number of instances to be transfered from the local to global pool
transformBackToOriginalTipText() - Method in class weka.attributeSelection.MatlabICA
Returns the tip text for this property
transformBackToOriginalTipText() - Method in class weka.attributeSelection.MatlabPCA
Returns the tip text for this property
transformBackToOriginalTipText() - Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
transformedData() - Method in interface weka.attributeSelection.AttributeTransformer
Returns the transformed data
transformedData() - Method in class weka.attributeSelection.MatlabICA
Gets the transformed training data.
transformedData() - Method in class weka.attributeSelection.MatlabNMF
Gets the transformed training data.
transformedData() - Method in class weka.attributeSelection.MatlabPCA
Gets the transformed training data.
transformedData() - Method in class weka.attributeSelection.PrincipalComponents
Gets the transformed training data.
transformedHeader() - Method in interface weka.attributeSelection.AttributeTransformer
Returns just the header for the transformed data (ie.
transformedHeader() - Method in class weka.attributeSelection.MatlabICA
Returns just the header for the transformed data (ie.
transformedHeader() - Method in class weka.attributeSelection.MatlabNMF
Returns just the header for the transformed data (ie.
transformedHeader() - Method in class weka.attributeSelection.MatlabPCA
Returns just the header for the transformed data (ie.
transformedHeader() - Method in class weka.attributeSelection.PrincipalComponents
Returns just the header for the transformed data (ie.
transpose() - Method in class weka.classifiers.functions.pace.Matrix
Matrix transpose.
transpose() - Method in class weka.core.Matrix
Returns the transpose of a matrix.
treeToString(int) - Method in class weka.classifiers.trees.m5.RuleNode
Recursively builds a textual description of the tree
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.
trimingThreshold - Variable in class weka.classifiers.functions.pace.ChisqMixture
 
trimingThreshold - Variable in class weka.classifiers.functions.pace.NormalMixture
 
trueNegativeRate(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the true negative rate with respect to a particular class.
trueNegativeRate(int) - Method in class weka.classifiers.Evaluation
Calculate the true negative rate with respect to a particular class.
truePositiveRate(int) - Method in class weka.classifiers.EnsembleEvaluation
Calculate the true positive 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.
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.
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.
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

UNCONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection
This unit is not connected to any others.
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.
UnitVector - class weka.filters.unsupervised.attribute.UnitVector.
Re-normalize the attributes for every instance so that it lies on a hypersphere of radius 1.
UnitVector() - Constructor for class weka.filters.unsupervised.attribute.UnitVector
 
UnivariateLinearRegression - class weka.classifiers.functions.UnivariateLinearRegression.
Class for learning a univariate linear regression model.
UnivariateLinearRegression() - Constructor for class weka.classifiers.functions.UnivariateLinearRegression
 
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.
UpdateableClassifier - interface weka.classifiers.UpdateableClassifier.
Interface to incremental classification models that can learn using one instance at a time.
UserClassifier - class weka.classifiers.trees.UserClassifier.
Class for generating an user defined decision tree.
UserClassifier() - Constructor for class weka.classifiers.trees.UserClassifier
Constructor
UserRequestAcceptor - interface weka.gui.beans.UserRequestAcceptor.
Interface to something that can accept requests from a user to perform some action
Utils - class weka.core.Utils.
Class implementing some simple utility methods.
Utils() - Constructor for class weka.core.Utils
 
uminus() - Method in class weka.classifiers.functions.pace.Matrix
Unary minus
unNormalizedDistributionForInstance(Instance) - Method in interface weka.classifiers.SoftClassifier
Predicts the class memberships for a given instance.
unNormalizedDistributionForInstance(Instance) - Method in class weka.classifiers.bayes.NaiveBayesSimple
Calculates the class membership probabilities for the given test instance.
unNormalizedDistributionForInstance(Instance) - Method in class weka.classifiers.sparse.NaiveBayesSimpleSparse
Calculates the class membership probabilities for the given test instance.
unclassified() - Method in class weka.classifiers.EnsembleEvaluation
Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
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).
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.
unhashClusters() - Method in class weka.clusterers.HAC
assuming m_clusters contains the clusters of indeces, convert it to clusters containing actual instances
unifDist - Variable in class weka.gui.visualize.MatrixPanel
For selecting uniform class distribution in the subsample
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.
unseenClasses(Instances) - Method in class weka.classifiers.bayes.SemiSupEM
Return a list of class values for which there are no instances in insts
unsorted() - Method in class weka.classifiers.functions.pace.DoubleVector
Returns true if vector not sorted
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.
update(double) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
update() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
updateAlpha() - Method in class weka.core.metrics.KL
Update the current value of alpha by the decay rate
updateChart(double[]) - Method in class weka.gui.beans.StripChart
Update the plot
updateChildPropertySheet() - Method in class weka.gui.GenericObjectEditor.GOEPanel
Updates the child property sheet, and creates if needed
updateCholeskyFactor(Matrix, Matrix, double[], double, boolean[]) - Method in class weka.core.Optimization
One rank update of the Cholesky factorization of B matrix in BFGS updates, i.e.
updateClassifier(Instance) - Method in interface weka.classifiers.UpdateableClassifier
Updates a classifier using the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.BayesNet
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.bayes.NaiveBayes
Updates the classifier with the given instance.
updateClassifier(Instance) - Method in class weka.classifiers.functions.Winnow
Updates the classifier with a new learning example
updateClassifier(Instance) - Method in class weka.classifiers.lazy.IB1
Updates the classifier.
updateClassifier(Instance) - Method in class weka.classifiers.lazy.IBk
Adds the supplied instance to the training set
updateClassifier(Instance) - Method in class weka.classifiers.lazy.LWR
Adds the supplied instance to the training set
updateClassifier(Instance) - Method in class weka.classifiers.lazy.kstar.KStar
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.sparse.IBkMetric
Adds the supplied instance to the training set
updateClusterAssignments() - Method in class weka.clusterers.MPCKMeans
Updates the clusterAssignments for all points after clustering.
updateClusterAssignments() - Method in class weka.clusterers.PCKMeans
Updates the clusterAssignments for all points after clustering.
updateClusterCentroids() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates cluster centroids
updateClusterCentroids() - Method in class weka.clusterers.PCKMeans
M-step of the KMeans clustering algorithm -- updates cluster centroids
updateClusterCentroids() - Method in class weka.clusterers.PCSoftKMeans
M-step of the KMeans clustering algorithm -- updates cluster centroids
updateClusterCentroids() - Method in class weka.clusterers.SeededKMeans
M-step of the KMeans clustering algorithm -- updates cluster centroids
updateEnsembleStats(double, Instance, double[]) - Method in class weka.classifiers.EnsembleClassifier
Update statistics for ensemble classifiers.
updateEnsembleStats(double, Instance, double[]) - Method in class weka.classifiers.EnsembleEvaluation
Update statistics for ensemble classifiers.
updateFS(Instance, Classifier[], double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
updateKDTree(Instance) - Method in class weka.core.KDTree
Adds one instance to the KDTree.
updateLogProbs() - Method in class weka.deduping.metrics.AffineProbMetric
store logs of all probabilities in m_editopLogProbs
updateMetricWeights() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights.
updateMetricWeightsDotPGD() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights.
updateMetricWeightsEuclidean() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights.
updateMetricWeightsKL() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights.
updateMetricWeightsKLGD() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights.
updateMetricWeightsMahalanobis() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights.
updateMinDistance(double[], boolean[], Instances, Instance) - Method in class weka.clusterers.FarthestFirst
 
updateMinMax(Instance) - Method in class weka.classifiers.bayes.SemiSupEM
Updates the minimum and maximum values for all the attributes based on a new instance.
updateMultipleMetricWeights() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights for the individual metrics.
updateMultipleMetricWeightsDotPGD() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights for the individual metrics.
updateMultipleMetricWeightsEuclidean() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights for the individual metrics.
updateMultipleMetricWeightsKLGD() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights for the individual metrics.
updateMultipleMetricWeightsMahalanobis() - Method in class weka.clusterers.MPCKMeans
M-step of the KMeans clustering algorithm -- updates metric weights.
updateObjectNames() - Method in class weka.gui.GenericObjectEditor
Updates the list of selectable object names, adding any new names to the list.
updateOptions() - Method in class weka.experiment.ClassifierSplitEvaluator
Updates the options that the current classifier is using.
updateOptions() - Method in class weka.experiment.DeduperSplitEvaluator
Updates the options that the current deduper is using.
updateOptions() - Method in class weka.experiment.ExtractionSplitEvaluator
Updates the options that the current extractor is using.
updateOptions() - Method in class weka.experiment.RegressionSplitEvaluator
Updates the options that the current classifier is using.
updateOptions() - Method in class weka.experiment.SemiSupClustererSplitEvaluator
Updates the options that the current clusterer is using.
updatePriors(Instance) - Method in class weka.classifiers.EnsembleEvaluation
Updates the class prior probabilities (when incrementally training)
updatePriors(Instance) - Method in class weka.classifiers.Evaluation
Updates the class prior probabilities (when incrementally training)
updateRadioLinks() - Method in class weka.gui.explorer.AttributeSelectionPanel
Updates the enabled status of the input fields and labels.
updateRadioLinks() - Method in class weka.gui.explorer.ClassifierPanel
Updates the enabled status of the input fields and labels.
updateRadioLinks() - Method in class weka.gui.explorer.ClustererPanel
Updates the enabled status of the input fields and labels.
updateRanges(Instance, int, double[][]) - Static method in class weka.clusterers.XMeans
Function should be in the Instances class!! Updates the minimum and maximum and width values for all the attributes based on a new instance.
updateRanges(Instance) - Method in class weka.core.DistanceFunction
Update the ranges if a new instance comes.
updateRanges(Instance, double[][]) - Static method in class weka.core.Instances
Updates the ranges given a new instance.
updateRangesFirst(Instance, int, double[][]) - Static method in class weka.clusterers.XMeans
Function should be in the Instances class!! Used to initialize the ranges.
updateRangesFirst(Instance, int, double[][]) - Method in class weka.core.Instances
Used to initialize the ranges.
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.
updateResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseResultListener
Determines the table name that results will be inserted into.
updateStatsForClusterer(double[], int) - Method in class weka.clusterers.SemiSupClustererEvaluation
Updates all the statistics about a clusterer performance for the current test instance.
updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.LinearUnit
This function will calculate what the change in weights should be and also update them.
updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralConnection
Call this function to update the weight values at this unit.
updateWeights(NeuralNode, double, double) - Method in interface weka.classifiers.functions.neural.NeuralMethod
This function will calculate what the change in weights should be and also update them.
updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralNode
Call this function to update the weight values at this unit.
updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.SigmoidUnit
This function will calculate what the change in weights should be and also update them.
updateWeightsUsingNewtonRaphson(double[], double[]) - Method in class weka.clusterers.MPCKMeans
calculates weights using Newton Raphson, to satisfy the positivity constraint of each attribute weight, returns learned attribute weights.
updateableClassifier() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme can build models incrementally.
updatingEquality(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier
Checks whether an updateable scheme produces the same model when trained incrementally as when batch trained.
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.ActiveFeatureAcquisitionCVResultProducer
Returns the tip text for this property
upperSizeTipText() - Method in class weka.experiment.ActiveLearningCurveCVResultProducer
Returns the tip text for this property
upperSizeTipText() - Method in class weka.experiment.LearningCurveCrossValidationResultProducer
Returns the tip text for this property
upperSizeTipText() - Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
upperSizeTipText() - Method in class weka.experiment.SemiSupCrossValidationResultProducer
Returns the tip text for this property
upperSizeTipText() - Method in class weka.experiment.SemiSupIncompleteLabelCurveCVResultProducer
Returns the tip text for this property
upperSizeTipText() - Method in class weka.experiment.SemiSupLearningCurveCVResultProducer
Returns the tip text for this property
upperSizeTipText() - Method in class weka.experiment.SemiSupPairActiveCurveCVResultProducer
Returns the tip text for this property
upperSizeTipText() - Method in class weka.experiment.SemiSupPointActiveCurveCVResultProducer
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
useClassifier(String, boolean) - Method in class weka.core.metrics.LearnableMetric
switch from calculating the metric to pair-space classification
useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSource
Use the default images for a data source
useDefaultVisual() - Method in class weka.gui.beans.AbstractEvaluator
Use the default images for an evaluator
useDefaultVisual() - Method in class weka.gui.beans.AbstractTestSetProducer
Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer
Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer
Use the default visual for this bean
useDefaultVisual() - Method in class weka.gui.beans.ClassAssigner
 
useDefaultVisual() - Method in class weka.gui.beans.Classifier
Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.DataVisualizer
Use the default appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.Filter
Use the default visual appearance
useDefaultVisual() - Method in class weka.gui.beans.GraphViewer
Use the default visual appearance
useDefaultVisual() - Method in class weka.gui.beans.StripChart
Use the default visual appearance for this bean
useDefaultVisual() - Method in class weka.gui.beans.TextViewer
Use the default visual appearance for this bean
useDefaultVisual() - Method in interface weka.gui.beans.Visible
Use the default visual representation
useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize
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.
useKononenkoTipText() - Method in class weka.filters.supervised.attribute.Discretize
Returns the tip text for this property
useMissingTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise
Returns the tip text for this property
useNoClassifier() - Method in class weka.core.metrics.LearnableMetric
switch from using a classifier in difference-space to vanilla L-1 norm distance
useResamplingTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
useTrainingTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval
Returns the tip text for this property
userCommand(TreeDisplayEvent) - Method in class weka.classifiers.trees.UserClassifier
Receives user choices from the tree view, and then deals with these choices.
userCommand(TreeDisplayEvent) - Method in interface weka.gui.treevisualizer.TreeDisplayListener
Gets called when the user selects something, in the tree display.
userDataEvent(VisualizePanelEvent) - Method in class weka.classifiers.trees.UserClassifier
This receives shapes from the data view.
userDataEvent(VisualizePanelEvent) - Method in interface weka.gui.visualize.VisualizePanelListener
This method receives an object containing the shapes, instances inside and outside these shapes and the attributes these shapes were created in.
usesClassifier() - Method in class weka.core.metrics.LearnableMetric
Is this metric defined in vanilla space, or difference space?

V

VFI - class weka.classifiers.misc.VFI.
Class implementing the voting feature interval classifier.
VFI() - Constructor for class weka.classifiers.misc.VFI
 
VISUALIZE_PROPERTIES - Static variable in class weka.gui.visualize.VisualizeUtils
Contains the visualization properties
VLINE - Static variable in class weka.gui.visualize.VisualizePanelEvent
 
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
VaryNode - class weka.classifiers.bayes.VaryNode.
Part of ADTree implementation.
VaryNode(int) - Constructor for class weka.classifiers.bayes.VaryNode
Creates new VaryNode
VectorSpaceMetric - class weka.deduping.metrics.VectorSpaceMetric.
This class uses a vector space to calculate similarity between two strings Some code borrowed from ir.vsr package by Raymond J.
VectorSpaceMetric() - Constructor for class weka.deduping.metrics.VectorSpaceMetric
Construct a vector space from a given set of examples
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(VisualizePanelListener) - Constructor for class weka.gui.visualize.VisualizePanel
This constructor allows a VisualizePanelListener to be set.
VisualizePanel() - Constructor for class weka.gui.visualize.VisualizePanel
Constructor
VisualizePanel.PlotPanel - class weka.gui.visualize.VisualizePanel.PlotPanel.
Inner class to handle plotting
VisualizePanel.PlotPanel() - Constructor for class weka.gui.visualize.VisualizePanel.PlotPanel
Constructor
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
 
VotedPerceptron - class weka.classifiers.functions.VotedPerceptron.
Implements the voted perceptron algorithm by Freund and Schapire.
VotedPerceptron() - Constructor for class weka.classifiers.functions.VotedPerceptron
 
validationChunkSizeTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
 
validationError() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee
 
validationSetSizeTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
validationThresholdTipText() - Method in class weka.classifiers.functions.neural.NeuralNetwork
 
valsToString(double[]) - Method in class weka.attributeSelection.MatlabPCA
 
value - Variable in class weka.classifiers.lazy.kstar.KStarCache.TableEntry
scale factor or stop parameter
value(int) - Method in class weka.core.Attribute
Returns a value of a nominal or string attribute.
value(int) - Method in class weka.core.BinarySparseInstance
Returns an instance's attribute value in internal format.
value(int) - Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
value(Attribute) - Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
value(int) - Method in class weka.core.SparseInstance
Returns an instance's attribute value in internal format.
value - Variable in class weka.core.metrics.TrainingPair
 
value - Variable in class weka.deduping.InstancePair
 
value - Variable in class weka.deduping.StringPair
 
value - Variable in class weka.deduping.metrics.Weight
A numerical weight value
value - Variable in class weka.experiment.PropertyNode
The current property value
valueIndicesTipText() - Method in class weka.filters.unsupervised.attribute.MakeIndicator
 
valueIsSmallerEqual(Instance, int, double) - Method in class weka.core.DistanceFunction
Returns true if the value of the given dimension is smaller or equal the value to be compared with.
valueIsSmallerEqual(Instance, int, double) - Method in class weka.core.EuclideanDistance
Returns true if the value of the given dimension is smaller or equal the value to be compared with.
valueSparse(int) - Method in class weka.core.BinarySparseInstance
Returns an instance's attribute value in internal format.
valueSparse(int) - Method in class weka.core.Instance
Returns an instance's attribute value in internal format.
values - Variable in class weka.classifiers.functions.pace.DiscreteFunction
 
variance(double[], double[], double[]) - Method in class weka.classifiers.trees.REPTree.Tree
Computes variance for subsets.
variance(int) - Method in class weka.core.Instances
Computes the variance for a numeric attribute.
variance(Attribute) - Method in class weka.core.Instances
Computes the variance for a numeric attribute.
variance(double[]) - Static method in class weka.core.Utils
Computes the variance for an array of doubles.
varianceCoveredTipText() - Method in class weka.attributeSelection.MatlabPCA
Returns the tip text for this property
varianceCoveredTipText() - Method in class weka.attributeSelection.PrincipalComponents
Returns the tip text for this property
vector - Variable in class weka.deduping.blocking.InstanceReference
The corresponding HashMapVector
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
visualize(String, int, int) - Method in class weka.gui.explorer.AttributeSelectionPanel
Handles constructing a popup menu with visualization options
visualize(String, int, int) - Method in class weka.gui.explorer.ClassifierPanel
Handles constructing a popup menu with visualization options.
visualizeClassifierErrors(VisualizePanel) - Method in class weka.gui.explorer.ClassifierPanel
Pops up a VisualizePanel for visualizing the data and errors for the classifier from the currently selected item in the results list
visualizeClusterAssignments(VisualizePanel) - Method in class weka.gui.explorer.ClustererPanel
Pops up a visualize panel to display cluster assignments
visualizeClusterer(String, int, int) - Method in class weka.gui.explorer.ClustererPanel
Handles constructing a popup menu with visualization options
visualizeTransformedData(VisualizePanel) - Method in class weka.gui.explorer.AttributeSelectionPanel
Popup a visualize panel for viewing transformed data
visualizeTree(String, String) - Method in class weka.gui.explorer.ClassifierPanel
Pops up a TreeVisualizer for the classifier from the currently selected item in the results list
visualizeTree(String, String) - Method in class weka.gui.explorer.ClustererPanel
Pops up a TreeVisualizer for the clusterer from the currently selected item in the results list

W

WEIGHT_INVERSE - Static variable in class weka.classifiers.lazy.IBk
 
WEIGHT_INVERSE - Static variable in class weka.classifiers.sparse.IBkMetric
 
WEIGHT_NONE - Static variable in class weka.classifiers.lazy.IBk
 
WEIGHT_NONE - Static variable in class weka.classifiers.sparse.IBkMetric
 
WEIGHT_SIMILARITY - Static variable in class weka.classifiers.lazy.IBk
 
WEIGHT_SIMILARITY - Static variable in class weka.classifiers.sparse.IBkMetric
 
WEST_CONNECTOR - Static variable in class weka.gui.beans.BeanVisual
 
WITHIN_BATCH - Static variable in class weka.gui.beans.IncrementalClassifierEvent
 
Weight - class weka.deduping.metrics.Weight.
A simple wrapper data structure for storing a double weight as an Object that can be put into lists, maps, etc.
Weight() - Constructor for class weka.deduping.metrics.Weight
 
WeightedDotP - class weka.core.metrics.WeightedDotP.
WeightedDotP class Implements the weighted dot product distance metric
WeightedDotP() - Constructor for class weka.core.metrics.WeightedDotP
Creates an empty metric class
WeightedDotP(int) - Constructor for class weka.core.metrics.WeightedDotP
Creates a new metric.
WeightedDotP(int[]) - Constructor for class weka.core.metrics.WeightedDotP
Creates a new metric which takes specified attributes.
WeightedEuclidean - class weka.core.metrics.WeightedEuclidean.
WeightedEuclidean class Implements weighted euclidean distance metric
WeightedEuclidean(int) - Constructor for class weka.core.metrics.WeightedEuclidean
Create a new metric.
WeightedEuclidean() - Constructor for class weka.core.metrics.WeightedEuclidean
Create a default new metric
WeightedEuclidean(int[]) - Constructor for class weka.core.metrics.WeightedEuclidean
Creates a new metric which takes specified attributes.
WeightedInstancesHandler - interface weka.core.WeightedInstancesHandler.
Interface to something that makes use of the information provided by instance weights.
WeightedMahalanobis - class weka.core.metrics.WeightedMahalanobis.
WeightedMahalanobis class Implements a weighted Mahalanobis distance metric weighted by a full matrix of weights.
WeightedMahalanobis(int) - Constructor for class weka.core.metrics.WeightedMahalanobis
Create a new metric.
WeightedMahalanobis() - Constructor for class weka.core.metrics.WeightedMahalanobis
Create a default new metric
WeightedMahalanobis(int[]) - Constructor for class weka.core.metrics.WeightedMahalanobis
Creates a new metric which takes specified attributes.
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).
Winnow - class weka.classifiers.functions.Winnow.
Implements Winnow and Balanced Winnow algorithms by N.
Winnow() - Constructor for class weka.classifiers.functions.Winnow
 
WordTokenizer - class weka.deduping.metrics.WordTokenizer.
This class defines a tokenizer that turns strings into HashMapVectors using the native Java StringTokenizer
WordTokenizer() - Constructor for class weka.deduping.metrics.WordTokenizer
A default constructor
WrapperSubsetEval - class weka.attributeSelection.WrapperSubsetEval.
Wrapper attribute subset evaluator.
WrapperSubsetEval() - Constructor for class weka.attributeSelection.WrapperSubsetEval
Constructor.
waitingExperiment(int) - Method in class weka.experiment.RemoteExperiment
Push an experiment back on the queue of waiting experiments
weight() - Method in class weka.classifiers.evaluation.NominalPrediction
Gets the weight assigned to this prediction.
weight() - Method in class weka.classifiers.evaluation.NumericPrediction
Gets the weight assigned to this prediction.
weight() - Method in interface weka.classifiers.evaluation.Prediction
Gets the weight assigned to this prediction.
weight(Instance) - Method in class weka.classifiers.rules.part.ClassifierDecList
Returns the weight a rule assigns to an instance.
weight() - Method in class weka.core.Attribute
Returns the attribute's weight.
weight() - Method in class weka.core.Instance
Returns the instance's weight.
weightAt(int) - Method in class weka.clusterers.Cluster
Returns the weight of the element at the given position.
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
weightInstances(Instances, double) - Method in class weka.classifiers.bayes.SemiSupEM
Weighted all given instances with given weight
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.
weightedInstancesHandler() - Method in class weka.classifiers.CheckClassifier
Checks whether the scheme says it can handle instance weights.
weights() - Method in class weka.classifiers.functions.SMO
Returns the coefficients in sparse format.
weights(Instance) - Method in class weka.classifiers.trees.j48.BinC45Split
Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.C45Split
Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns weights if instance is assigned to more than one subset.
weights(Instance) - Method in class weka.classifiers.trees.j48.NoSplit
Always returns null because there is only one subset.
weightsForInstance(Instance) - Method in class weka.clusterers.EM
Returns the weights (indicating cluster membership) for a given instance
weka.associations - package weka.associations
 
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.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.sparse - package weka.classifiers.sparse
 
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.m5 - package weka.classifiers.trees.m5
 
weka.clusterers - package weka.clusterers
 
weka.clusterers.assigners - package weka.clusterers.assigners
 
weka.core - package weka.core
 
weka.core.converters - package weka.core.converters
 
weka.core.metrics - package weka.core.metrics
 
weka.datagenerators - package weka.datagenerators
 
weka.deduping - package weka.deduping
 
weka.deduping.blocking - package weka.deduping.blocking
 
weka.deduping.metrics - package weka.deduping.metrics
 
weka.estimators - package weka.estimators
 
weka.experiment - package weka.experiment
 
weka.extraction - package weka.extraction
 
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.streams - package weka.gui.streams
 
weka.gui.treevisualizer - package weka.gui.treevisualizer
 
weka.gui.visualize - package weka.gui.visualize
 
wekaStaticWrapper(Sourcable, String) - Static method in class weka.classifiers.EnsembleEvaluation
Wraps a static classifier in enough source to test using the weka class libraries.
wekaStaticWrapper(Sourcable, String) - Static method in class weka.classifiers.Evaluation
Wraps a static classifier in enough source to test using the weka class libraries.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.BinC45Split
Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.C45Split
Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.ClassifierSplitModel
Returns index of subset instance is assigned to.
whichSubset(Instance) - Method in class weka.classifiers.trees.j48.NoSplit
Always returns 0 because only there is only one subset.
whileCnt - Variable in class weka.classifiers.lazy.LBR
 
width() - Method in class weka.classifiers.functions.pace.ExponentialFormat
 
width() - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
 
width - Variable in class weka.classifiers.functions.pace.FloatingPointFormat
 
width() - Method in class weka.classifiers.functions.pace.FloatingPointFormat
 
write(Writer) - Method in class weka.core.Matrix
Writes out a matrix.

X

XMeans - class weka.clusterers.XMeans.
XMeans clustering class.
XMeans() - Constructor for class weka.clusterers.XMeans
 
XVALTAGS_SELECTION - Static variable in class weka.attributeSelection.RaceSearch
 
X_SHAPE - Static variable in class weka.gui.visualize.Plot2D
 
XingMetric - class weka.core.metrics.XingMetric.
Class for performing RCA according to Bar-Hillel's algorithm.
XingMetric(int) - Constructor for class weka.core.metrics.XingMetric
Create a new metric.
XingMetric() - Constructor for class weka.core.metrics.XingMetric
Create a default new metric
XingMetric(int[]) - Constructor for class weka.core.metrics.XingMetric
Creates a new metric which takes specified attributes.
xLabelFreqTipText() - Method in class weka.gui.beans.StripChart
GUI Tip text
xStats - Variable in class weka.experiment.PairedStats
The stats associated with the data in column 1
xlogx(int) - Static method in class weka.core.Utils
Returns c*log2(c) for a given integer value c.
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

Z_MAX - Static variable in class weka.classifiers.meta.LogitBoost
A threshold for responses (Friedman suggests between 2 and 4)
Z_MAX - Static variable in class weka.classifiers.meta.RacedIncrementalLogitBoost
A threshold for responses (Friedman suggests between 2 and 4)
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.

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