G-Index

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G

g1(double, double). Method in class weka.classifiers.functions.pace.PaceMatrix: Constructs the Givens rotation
g2(double[], int, int, int). Method in class weka.classifiers.functions.pace.PaceMatrix
gainRatio(). Method in class weka.classifiers.trees.j48.C45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio(). Method in class weka.classifiers.trees.j48.BinC45Split: Returns (C4.5-type) gain ratio for the generated split.
gainRatio(double[][]). Static method in class weka.core.ContingencyTables: Computes gain ratio for contingency table (split on rows).
GainRatioAttributeEval class weka.attributeSelection.GainRatioAttributeEval.: Class for Evaluating attributes individually by measuring gain ratio with respect to the class.
GainRatioAttributeEval(). Constructor for class weka.attributeSelection.GainRatioAttributeEval: Constructor
GainRatioSplitCrit class weka.classifiers.trees.j48.GainRatioSplitCrit.: Class for computing the gain ratio for a given distribution.
GainRatioSplitCrit(). Constructor for class weka.classifiers.trees.j48.GainRatioSplitCrit
generateInstances(int[]). Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Generates a new instance using one kernel estimator.
generateInstances(int[]). Method in interface weka.gui.boundaryvisualizer.DataGenerator: Generate an instance.
generateRankingTipText(). Method in class weka.attributeSelection.RaceSearch: Returns the tip text for this property
generateRankingTipText(). Method in class weka.attributeSelection.ForwardSelection: Returns the tip text for this property
generateRankingTipText(). Method in class weka.attributeSelection.Ranker: Returns the tip text for this property
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.
GeneratorPropertyIteratorPanel class weka.gui.experiment.GeneratorPropertyIteratorPanel.: This panel controls setting a list of values for an arbitrary resultgenerator property for an experiment to iterate over.
GeneratorPropertyIteratorPanel(). Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel: Creates the property iterator panel initially disabled.
GeneratorPropertyIteratorPanel(Experiment). Constructor for class weka.gui.experiment.GeneratorPropertyIteratorPanel: Creates the property iterator panel and sets the experiment.
GenericArrayEditor class weka.gui.GenericArrayEditor.: A PropertyEditor for arrays of objects that themselves have property editors.
GenericArrayEditor(). Constructor for class weka.gui.GenericArrayEditor: Sets up the array editor.
GenericObjectEditor class weka.gui.GenericObjectEditor.: A PropertyEditor for objects that themselves have been defined as editable in the GenericObjectEditor configuration file, which lists possible values that can be selected from, and themselves configured.
GenericObjectEditor.GOEPanel class weka.gui.GenericObjectEditor.GOEPanel.: Handles the GUI side of editing values.
GenericObjectEditor.GOEPanel(GenericObjectEditor). 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(GenericObjectEditor, JTree). Constructor for class weka.gui.GenericObjectEditor.JTreePopupMenu: Constructs a new popup menu.
GenericObjectEditor(). Constructor for class weka.gui.GenericObjectEditor: Default constructor.
GenericObjectEditor(boolean). Constructor for class weka.gui.GenericObjectEditor: Constructor that allows specifying whether it is possible to change the class within the editor dialog.
GeneticSearch class weka.attributeSelection.GeneticSearch.: Class for performing a genetic based search.
GeneticSearch(). Constructor for class weka.attributeSelection.GeneticSearch: Constructor.
get(int). Method in class weka.core.DynamicArrayOfPosInt
get(int). Method in class weka.classifiers.functions.pace.DoubleVector: Gets a single element.
get(int). Method in class weka.classifiers.functions.pace.IntVector: Gets the value of an element.
get(int, int). Method in class weka.classifiers.functions.pace.Matrix: Get a single element.
getAcuity(). Method in class weka.clusterers.Cobweb: get the acuity value
getAdjustWeights(). Method in class weka.filters.supervised.instance.SpreadSubsample: Returns true if instance weights will be adjusted to maintain total weight per class.
getAdvanceDataSetFirst(). Method in class weka.experiment.Experiment: Get the value of m_DataSetFirstFirst.
getAlpha(). Method in class weka.classifiers.bayes.BayesNet: Method declaration
getAlpha(). Method in class weka.classifiers.functions.Winnow: Get the value of Alpha.
getAnimatedIcon(). Method in class weka.gui.beans.BeanVisual: Returns the animated icon
getArffFile(). Method in class weka.gui.streams.InstanceSavePanel
getArffFile(). Method in class weka.gui.streams.InstanceLoader
getArray(). Method in class weka.classifiers.functions.pace.Matrix: Access the internal two-dimensional array.
getArrayCopy(). Method in class weka.classifiers.functions.pace.Matrix: Copy 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.
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.GenericArrayEditor: Returns null as we don't support getting/setting values as text.
getAsText(). Method in class weka.gui.SelectedTagEditor: Gets the current value as text.
getAsText(). Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting/setting values as text.
getAsText(). Method in class weka.gui.CostMatrixEditor: Some objects can be represented as text, but a cost matrix cannot.
getAttIndex(int). Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Attribute Indexes array
getAttIndexSet(). Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the Index of the Attribute that is to be changed.
getAttribute1(). Method in class weka.gui.visualize.VisualizePanelEvent
getAttribute2(). Method in class weka.gui.visualize.VisualizePanelEvent
getAttributeEvaluator(). Method in class weka.attributeSelection.RankSearch: Get the attribute evaluator used to generate the ranking.
getAttributeEvaluator(). Method in class weka.attributeSelection.RaceSearch: Get the attribute evaluator used to generate the ranking.
getAttributeIndex(). Method in class weka.filters.unsupervised.attribute.Add: Get the index where the attribute will be inserted
getAttributeIndex(). Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.unsupervised.attribute.StringToNominal: Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the index of the attribute used.
getAttributeIndex(). Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the 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.Remove: 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
getAttributeMax(int). Method in class weka.classifiers.lazy.IBk: Get an attributes maximum observed value
getAttributeMin(int). Method in class weka.classifiers.lazy.IBk: Get an attributes minimum observed value
getAttributeName(). Method in class weka.filters.unsupervised.attribute.Add: Get the name of the attribute to be created
getAttributeSelectionMethod(). Method in class weka.classifiers.functions.LinearRegression: Gets the method used to select attributes for use in the linear regression.
getAttributeType(). Method in class weka.filters.unsupervised.attribute.RemoveType: Gets the attribute type to be deleted by the filter.
getAttsToEliminatePerIteration(). Method in class weka.attributeSelection.SVMAttributeEval: Get the constant rate of attribute elimination per iteration
getAutoBuild(). Method in class weka.classifiers.functions.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.
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.
getBaseClassifier(int). Method in class weka.classifiers.meta.Grading: Gets the specific classifier from the set of base classifiers.
getBaseClassifier(int). Method in class weka.classifiers.meta.StackingC: Gets the specific classifier from the set of base classifiers.
getBaseClassifiers(). Method in class weka.classifiers.meta.Stacking: Gets the list of possible classifers to choose from.
getBaseClassifiers(). Method in class weka.classifiers.meta.Grading: Gets the list of possible classifers to choose from.
getBaseClassifiers(). Method in class weka.classifiers.meta.StackingC: 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
getBeanContext(). Method in class weka.gui.beans.DataVisualizer: Return the bean context (if any) that this bean is embedded in
getBeanDescriptor(). Method in class weka.gui.beans.CrossValidationFoldMakerBeanInfo: Return the bean descriptor for this bean
getBeanDescriptor(). Method in class weka.gui.beans.ClassAssignerBeanInfo
getBeanDescriptor(). Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo: 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.FilterBeanInfo: 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.ClassifierBeanInfo: 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.misc.VFI: Get the value of the bias parameter
getBiasToUniformClass(). Method in class weka.filters.supervised.instance.Resample: Gets the bias towards a uniform class.
getBinarizeNumericAttributes(). Method in class weka.attributeSelection.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
getBinValue(). Method in class weka.clusterers.XMeans: Gets value that represents true in a new numeric attribute.
getBuildLogisticModels(). Method in class weka.classifiers.functions.supportVector.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.supportVector.SMO: Get the value of C.
getC(). Method in class weka.classifiers.functions.suppogetCacheSize(). Method in class weka.classifiers.functions.supportVector.SMO: Get the size of the kernel cache
getCacheSize(). Method in class weka.classifiers.functions.supportVector.SMOreg: Get the size of the kernel cache
getCacheValues(double). Method in class weka.classifiers.lazy.kstar.KStarCache: Returns the values in the cache mapped by the specified key
getCalcOutOfBag(). Method in class weka.classifiers.meta.Bagging: Get whether the out of bag error is calculated.
getCalculatedNumToSelect(). Method in class weka.attributeSelection.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.ForwardSelection: Gets the calculated number of attributes to retain.
getCalculatedNumToSelect(). Method in class weka.attributeSelection.Ranker: Gets the calculated number to select.
getCalculateStdDevs(). Method in class weka.experiment.AveragingResultProducer: Get the value of CalculateStdDevs.
GetCardinalityOfParents(). Method in class weka.classifiers.bayes.ParentSet: returns cardinality of parents
getCenter(). Method in class weka.gui.treevisualizer.Node: Get the value of center.
getChangeInWeights(). Method in class weka.classifiers.functions.neural.NeuralNode: call this function to get the chnage in weights array.
getCheckErrorRate(). Method in class weka.classifiers.rules.JRip
getChild(int). Method in class weka.gui.treevisualizer.Node: Get the Edge for the child number 'i'.
getChildForBranch(int). Method in class weka.classifiers.trees.adtree.Splitter: Gets the child for a branch of the split.
getChildForBranch(int). Method in class weka.classifiers.trees.adtree.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.
getCindex(). Method in class weka.gui.visualize.PlotData2D: Get the currently set colouring index of the data
getCIndex(). Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute selected for coloring
getClassColumn(). Method in class weka.gui.beans.ClassAssigner
getClassCounts(). Method in class weka.filters.supervised.attribute.ClassOrder: Get the class distribution of the sorted class values.
getClassesToClusters(). Method in class weka.clusterers.ClusterEvaluation: Return the array (ordered by cluster number) of minimum error class to cluster mappings
getClassForIRStatistics(). Method in class weka.experiment.ClassifierSplitEvaluator: Get the value of ClassForIRStatistics.
getClassifier(). Method in class weka.gui.beans.IncrementalClassifierEvent: Get the classifier
getClassifier(). Method in class weka.gui.beans.BatchClassifierEvent: Get the classifier
getClassifier(). Method in class weka.gui.beans.Classifier: Get the classifier currently set for this wrapper
getClassifier(). Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the classifier used by the filter.
getClassifier(). Method in class weka.experiment.ClassifierSplitEvaluator: Get the value of Classifier.
getClassifier(). Method in class weka.experiment.RegressionSplitEvaluator: Get the value of Classifier.
getClassifier(). Method in class weka.attributeSelection.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.lazy.LWL: 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.ClassificationViaRegression: Get the base classifier (regression scheme) used as the classifier
getClassifier(). Method in class weka.classifiers.meta.DistributionMetaClassifier: Gets the classifier being wrapped.
getClassifier(). Method in class weka.classifiers.meta.MultiBoostAB: Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.meta.Bagging: 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.AdaBoostM1: Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the classifier used.
getClassifier(). Method in class weka.classifiers.meta.CVParameterSelection: Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.meta.LogitBoost: Get the classifier used as the classifier
getClassifier(). Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the classifier used.
getClassifier(). Method in class weka.classifiers.meta.RegressionByDiscretization: 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.FilteredClassifier: Gets the classifier used.
getClassifier(int). Method in class weka.classifiers.meta.Vote: Gets a single classifier from the set of available classifiers.
getClassifier(int). Method in class weka.classifiers.meta.MultiScheme: Gets a single classifier from the set of available classifiers.
getClassifiers(). Method in class weka.classifiers.meta.Vote: Gets the list of possible classifers to choose from.
getClassifiers(). Method in class weka.classifiers.meta.MultiScheme: Gets the list of possible classifers to choose from.
getClassIndex(). Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the attribute on which misclassifications are based.
getClassIndex(). Method in class weka.classifiers.BVDecompose: Get the index (starting from 1) of the attribute used as the class.
getClassName(). Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the class containing the transformation method.
getClassOrder(). Method in class weka.filters.supervised.attribute.ClassOrder: Get the wanted class order
getClearEachDataset(). Method in class weka.gui.streams.InstanceViewer
getClosestConnections(Point, int). Static method in class weka.gui.beans.BeanConnection: Return a list of connections within some delta of a point
getClosestConnectorPoint(Point). Method in class weka.gui.beans.BeanVisual: Returns the coordinates of the closest "connector" point to the supplied point.
getClusterAssignments(). Method in class weka.clusterers.ClusterEvaluation: Return an array of cluster assignments corresponding to the most recent set of instances clustered.
getClusterer(). Method in class weka.clusterers.DistributionMetaClusterer: Gets the clusterer being wrapped.
getClusterer(). Method in class weka.filters.unsupervised.attribute.AddCluster: Gets the clusterer used by the filter.
getClusterModelsNumericAtts(). Method in class weka.clusterers.EM: Return the normal distributions for the cluster models
getClusterPriors(). Method in class weka.clusterers.EM: Return the priors for the clusters
getColor(). Method in class weka.gui.treevisualizer.Node: Get the value of color.
getColorBox(). Method in class weka.gui.AttributeVisualizationPanel
getColoringIndex(). Method in class weka.gui.AttributeVisualizationPanel: Get the coloring index for the plot
getColoringIndex(). Method in class weka.gui.beans.AttributeSummarizer: Return the coloring index for the attribute summary plots
getColors(). Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the current vector of Color objects used for the classes
getColumn(int). Method in class weka.core.Matrix: Gets a column of the matrix and returns it as a double array.
getColumn(int). Method in class weka.classifiers.functions.pace.PaceMatrix: Return a DoubleVector that stores a column of the matrix
getColumn(int, int, int). Method in class weka.classifiers.functions.pace.PaceMatrix: Return a DoubleVector that stores some elements of a column of the matrix
getColumnDimension(). Method in class weka.classifiers.functions.pace.Matrix: Get column dimension.
getColumnPackedCopy(). Method in class weka.classifiers.functions.pace.Matrix: Make a one-dimensional column packed copy of the internal array.
getCommand(). Method in class weka.gui.treevisualizer.TreeDisplayEvent
getCompatibilityState(). Method in interface weka.experiment.ResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.LearningRateResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.CrossValidationResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.RandomSplitResultProducer: Gets a description of the internal settings of the result producer, sufficient for distinguishing a ResultProducer instance from another with different settings (ignoring those settings set through this interface).
getCompatibilityState(). Method in class weka.experiment.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.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).
getComplexityParameter(). Method in class weka.attributeSelection.SVMAttributeEval: Get the value of C used with SMO
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.Rule: Get the consequent of this rule, i.e.
getCostMatrix(). Method in class weka.classifiers.meta.MetaCost: Gets the misclassification cost matrix.
getCostMatrix(). Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the misclassification cost matrix.
getCostMatrixSource(). Method in class weka.classifiers.meta.MetaCost: Gets the source location method of the cost matrix.
getCostMatrixSource(). Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the source location method of the cost matrix.
getCount(double). Method in class weka.classifiers.bayes.DiscreteEstimatorBayes: Get a counts for a value
getCount(Node, int). Static method in class weka.gui.treevisualizer.Node: Recursively finds the number of visible nodes there are (this may accidentally count some of the invis nodes).
getCounts(int[], int[], int[], int, int, ADNode, boolean). Method in class weka.classifiers.bayes.VaryNode: get counts for specific instantiation of a set of nodes
getCounts(int[], int[], int[], int, int, boolean). Method in class weka.classifiers.bayes.ADNode: get counts for specific instantiation of a set of nodes
getCrossoverProb(). Method in class weka.attributeSelection.GeneticSearch: get the probability of crossover
getCrossVal(). Method in class weka.classifiers.rules.DecisionTable: Gets the number of folds for cross validation
getCrossValidate(). Method in class weka.classifiers.lazy.IBk: Gets whether hold-one-out cross-validation will be used to select the best k value
getCurrentDatasetNumber(). Method in class weka.experiment.Experiment: When an experiment is running, this returns the current dataset number.
getCurrentInstance(). Method in class weka.gui.beans.IncrementalClassifierEvent: Get the current instance
getCurrentPropertyNumber(). Method in class weka.experiment.Experiment: When an experiment is running, this returns the index of the current custom property value.
getCurrentRunNumber(). Method in class weka.experiment.Experiment: When an experiment is running, this returns the current run number.
getCurve(FastVector). Method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector). Method in class weka.classifiers.evaluation.CostCurve: Calculates the performance stats for the default class and return results as a set of Instances.
getCurve(FastVector). Method in class weka.classifiers.evaluation.MarginCurve: Calculates the cumulative margin distribution for the set of predictions, returning the result as a set of Instances.
getCurve(FastVector, int). Method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the performance stats for the desired class and return results as a set of Instances.
getCurve(FastVector, int). Method in class weka.classifiers.evaluation.CostCurve: Calculates the performance stats for the desired class and return results as a set of Instances.
getCustomEditor(). Method in class weka.gui.GenericArrayEditor: Returns the array editing component.
getCustomEditor(). Method in class weka.gui.GenericObjectEditor: Returns the array editing component.
getCustomEditor(). Method in class weka.gui.FileEditor: Gets the custom editor component.
getCustomEditor(). Method in class weka.gui.CostMatrixEditor: Gets a GUI component with which the user can edit the cost matrix.
getCustomPanel(). Method in class weka.gui.GenericObjectEditor: Gets the custom panel used for editing the object.
getCustomPanel(). Method in interface weka.gui.CustomPanelSupplier: Gets the custom panel for the object.
getCutoff(). Method in class weka.clusterers.Cobweb: get the cutoff
getCutOffFactor(). Method in class weka.clusterers.XMeans: Gets the cutoff factor.
getCutPoints(int). Method in class weka.filters.supervised.attribute.Discretize: Gets the cut points for an attribute
getCutPoints(int). Method in class weka.filters.unsupervised.attribute.Discretize: Gets the cut points for an attribute
getCVisible(). Method in class weka.gui.treevisualizer.Node: Get If this node's childs are visible.
getCVParameter(int). Method in class weka.classifiers.meta.CVParameterSelection: Gets the scheme paramter with the given index.
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.
getData(). Method in class weka.classifiers.rules.RuleStats: Get the data of the stats
getDatabaseURL(). Method in class weka.experiment.DatabaseUtils: Get the value of DatabaseURL.
getDataFileName(). Method in class weka.classifiers.BVDecompose: Get the name of the data file used for the decomposition
getDataPoint(). Method in class weka.gui.beans.ChartEvent: Get the data point
getDataSet(). Method in class weka.gui.beans.DataSetEvent: Return the instances of the data set
getDataSet(). Method in class weka.core.converters.AbstractLoader
getDataSet(). Method in class weka.core.converters.CSVLoader: Return the full data set.
getDataSet(). Method in class weka.core.converters.C45Loader: 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.ArffLoader: Return the full data set.
getDataSet(). Method in class weka.core.converters.SerializedInstancesLoader: Return the full data set.
getDatasetKeyColumns(). Method in class weka.experiment.PairedTTester: Get the value of DatasetKeyColumns.
getDatasets(). Method in class weka.experiment.Experiment: Gets the datasets in the experiment.
getDebug(). Method in class weka.gui.streams.InstanceSavePanel
getDebug(). Method in class weka.gui.streams.InstanceLoader
getDebug(). Method in class weka.gui.streams.InstanceJoiner
getDebug(). Method in class weka.gui.streams.InstanceViewer
getDebug(). Method in class weka.gui.streams.InstanceCounter
getDebug(). Method in class weka.gui.streams.InstanceTable
getDebug(). Method in class weka.clusterers.EM: Get debug mode
getDebug(). Method in class weka.filters.unsupervised.attribute.AddExpression: Gets whether debug is set
getDebug(). Method in class weka.attributeSelection.RaceSearch: Get whether output is to be verbose
getDebug(). Method in class weka.classifiers.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.lazy.IBk: Get the value of Debug.
getDebug(). Method in class weka.classifiers.lazy.LWL: SGts whether debugging output should be produced
getDebug(). Method in class weka.classifiers.meta.AdditiveRegression: Gets whether debugging has been turned on
getDebug(). Method in class weka.classifiers.meta.MultiBoostAB: Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.meta.Vote: 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.AdaBoostM1: Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.meta.CVParameterSelection: Gets 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.RegressionByDiscretization: Gets whether debugging output will be printed
getDebug(). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get whether debugging is turned on
getDebug(). Method in class weka.classifiers.rules.JRip
getDebug(). Method in class weka.classifiers.trees.RandomTree: Get the value of Debug.
getDebug(). Method in class weka.classifiers.functions.LeastMedSq: Returns whether or not debugging output shouild be printed
getDebug(). Method in class weka.classifiers.functions.LinearRegression: Controls whether debugging output will be printed
getDebug(). Method in class weka.classifiers.functions.Logistic: Gets whether debugging output will be printed.
getDebug(). Method in class weka.classifiers.functions.pace.PaceRegression: Controls whether debugging output will be printed
getDebugLevel(). Method in class weka.core.KDTree: Gets the debug level.
getDebugLevel(). Method in class weka.clusterers.XMeans: Gets the debug level.
getDecay(). Method in class weka.classifiers.functions.neural.NeuralNetwork
getDefaultWeight(). Method in class weka.classifiers.functions.Winnow: Get the value of defaultWeight.
getDelimiters(). Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the value of delimiters.
getDelta(). Method in class weka.associations.Apriori: Get the value of delta.
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.
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
getDistanceF(). Method in class weka.clusterers.XMeans: Gets the distance function.
getDistanceFunction(). Method in class weka.core.KDTree: Gets the distance function.
getDistanceWeighting(). Method in class weka.classifiers.lazy.IBk: Gets the distance weighting method used.
getDistributionClassifier(). Method in class weka.classifiers.meta.ThresholdSelector: Get the DistributionClassifier used as the classifier.
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
getDistributions(int). Method in class weka.classifiers.rules.RuleStats: Get the class distribution predicted by the rule in given position
getDistributionSpread(). Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the value for the distribution spread
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.core.AlgVector: Returns the value of a cell in the matrix.
getElement(int). Method in class weka.clusterers.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.core.AlgVector: Gets the elements of the vector and returns them as double array.
getElements(int). Method in class weka.clusterers.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.
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.
getEps(). Method in class weka.classifiers.functions.supportVector.SMOreg: Get the value of eps.
getEpsilon(). Method in class weka.classifiers.functions.supportVector.SMO: Get the value of epsilon.
getEpsilon(). Method in class weka.classifiers.functions.supportVector.SMOreg: Get the value of epsilon.
getEpsilonParameter(). Method in class weka.attributeSelection.SVMAttributeEval: Get the value of P used with SMO
getError(). Method in class weka.classifiers.BVDecompose: Get the calculated error rate
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.KDConditionalEstimator: Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.DKConditionalEstimator: Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.NDConditionalEstimator: 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.DNConditionalEstimator: Get a probability estimator for a value
getEstimator(double). Method in class weka.estimators.NNConditionalEstimator: Get a probability estimator for a value
getEvaluationMode(). Method in class weka.classifiers.meta.ThresholdSelector: Gets the evaluation mode used.
getEvaluator(). Method in class weka.filters.supervised.attribute.AttributeSelection: Get the name of the attribute/subset evaluator
getEvaluator(). Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the attribute evaluator used
getEventSetDescriptors(). Method in class weka.gui.beans.AbstractTrainAndTestSetProducerBeanInfo
getEventSetDescriptors(). Method in class weka.gui.beans.AbstractTestSetProducerBeanInfo
getEventSetDescriptors(). Method in class weka.gui.beans.AbstractTrainingSetProducerBeanInfo: Returns event set descriptors for this type of bean
getEventSetDescriptors(). Method in class weka.gui.beans.TextViewerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors(). Method in class weka.gui.beans.DataVisualizerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors(). Method in class weka.gui.beans.GraphViewerBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors(). Method in class weka.gui.beans.ClassAssignerBeanInfo: Returns the event set descriptors
getEventSetDescriptors(). Method in class weka.gui.beans.AbstractDataSourceBeanInfo: Get the event set descriptors pertinent to data sources
getEventSetDescriptors(). Method in class weka.gui.beans.ClassifierPerformanceEvaluatorBeanInfo
getEventSetDescriptors(). Method in class weka.gui.beans.IncrementalClassifierEvaluatorBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors(). Method in class weka.gui.beans.ScatterPlotMatrixBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors(). Method in class weka.gui.beans.AttributeSummarizerBeanInfo: 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.StripChartBeanInfo: Get the event set descriptors for this bean
getEventSetDescriptors(). Method in class weka.gui.beans.ClassifierBeanInfo
getExclusive(). Method in class weka.classifiers.rules.ConjunctiveRule
getExecutionStatus(). Method in class weka.experiment.TaskStatusInfo: Get the execution status of this Task.
getExpectedResultsPerAverage(). Method in class weka.experiment.AveragingResultProducer: Get the value of ExpectedResultsPerAverage.
getExperiment(). Method in class weka.gui.experiment.SetupPanel: Gets the currently configured experiment.
getExperiment(). Method in class weka.gui.experiment.SetupModePanel: Gets the currently configured experiment.
getExperiment(). Method in class weka.gui.experiment.SimpleSetupPanel: Gets the currently configured experiment.
getExperiment(). Method in class weka.experiment.RemoteExperimentSubTask: Get the experiment for this sub task
getExponent(). Method in class weka.classifiers.functions.VotedPerceptron: Get the value of exponent.
getExponent(). Method in class weka.classifiers.functions.supportVector.SMO: Get the value of exponent.
getExponent(). Method in class weka.classifiers.functions.supportVector.SMOreg: Get the value of exponent.
getExpression(). Method in class weka.filters.unsupervised.attribute.AddExpression: Get the expression
getFallout(). Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the fallout.
getFalseNegative(). Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of positive instances predicted as negative
getFalsePositive(). Method in class weka.classifiers.evaluation.TwoClassStats: Gets the number of negative instances predicted as positive
getFalsePositiveRate(). Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the false positive rate.
getFeatureSpaceNormalization(). Method in class weka.classifiers.functions.supportVector.SMO: Check whether feature spaces is being normalized.
getFeatureSpaceNormalization(). Method in class weka.classifiers.functions.supportVector.SMOreg: Check whether feature spaces is being normalized.
getFile(). Method in class weka.core.converters.ArffLoader: get the File specified as the source
getFillWithMissing(). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Gets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
getFilter(). Method in class weka.gui.beans.Filter
getFilter(). Method in class weka.classifiers.meta.FilteredClassifier: Gets the filter used.
getFiltered(int). Method in class weka.classifiers.rules.RuleStats: Get the data after filtering the given rule
getFilterType(). Method in class weka.attributeSelection.SVMAttributeEval: Get the filtering mode passed to SMO
getFilterType(). Method in class weka.classifiers.functions.supportVector.SMO: Gets how the training data will be transformed.
getFilterType(). Method in class weka.classifiers.functions.supportVector.SMOreg: Gets how the training data will be transformed.
getFindNumBins(). Method in class weka.filters.unsupervised.attribute.Discretize: Get the value of FindNumBins.
getFindNumBins(). Method in class weka.filters.unsupervised.attributstValueIndex(). Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the first value used.
getFlag(char, String[]). Static method in class weka.core.Utils: Checks if the given array contains the flag "-Char".
getFMeasure(). Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the F-Measure.
getFold(). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the fold which is selected.
getFold(). Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the fold which is selected.
getFoldColumn(). Method in class weka.experiment.PairedTTester: Get the value of FoldColumn.
getFolds(). Method in class weka.gui.beans.CrossValidationFoldMaker: Get the currently set number of folds
getFolds(). Method in class weka.attributeSelection.WrapperSubsetEval: Get the number of folds used for accuracy estimation
getFolds(). Method in class weka.classifiers.rules.Ridor
getFolds(). Method in class weka.classifiers.rules.JRip
getFolds(). Method in class weka.classifiers.rules.ConjunctiveRule
getFoldsType(). Method in class weka.attributeSelection.RaceSearch: Get the xfold type
getFrequencyLimitForParentAttributes(). Method in class weka.classifiers.bayes.AODE: Return the frequency limit for parent attributes
getFunctionValue(int). Method in class weka.classifiers.functions.pace.DiscreteFunction: Gets a particular function value
getGamma(). Method in class weka.classifiers.functions.supportVector.SMO: Get the value of gamma.
getGamma(). Method in class weka.classifiers.functions.supportVector.SMOreg: Get the value of gamma.
getGCount(Node, int). Static method in class weka.gui.treevisualizer.Node: Recursively finds the number of visible groups of siblings there are.
getGenerateRanking(). Method in class weka.attributeSelection.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.ForwardSelection: Gets whether ranking has been requested.
getGenerateRanking(). Method in class weka.attributeSelection.Ranker: This is a dummy method.
getGeneratorSamplesBase(). Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the base used for computing the number of samples to obtain from each generator
getGlobalBlend(). Method in class weka.classifiers.lazy.kstar.KStar: Get the value of the global blend parameter
getGraphString(). Method in class weka.gui.beans.GraphEvent: Return the dot string for the graph
getGraphTitle(). Method in class weka.gui.beans.GraphEvent: Return the graph title
getGridWidth(). Method in class weka.gui.beans.AttributeSummarizer: Get the width of the grid of plots
getGroup(). Method in class weka.attributeSelection.BestFirst.Link2: Get a group
getGroup(). Method in class weka.classifiers.rules.DecisionTable.Link: Gets the group.
getGUI(). Method in class weka.classifiers.functions.neural.NeuralNetwork
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.
getId(). Method in class weka.classifiers.functions.neural.NeuralConnection
getID(). Method in class weka.gui.treevisualizer.TreeDisplayEvent
getID(). Method in class weka.gui.streams.InstanceEvent: Get the event type
getID(). Method in class weka.core.Tag: Gets the numeric ID of the Tag.
getIgnoredAttributeIndices(). Method in class weka.filters.unsupervised.attribute.AddCluster: Gets ranges of attributes to be ignored.
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.
getInputNums(). Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the input numbers.
getInputs(). Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the inputs.
getInstance(). Method in class weka.gui.beans.InstanceEvent: Get the instance
getInstanceIndex(int). Method in class weka.classifiers.lazy.LBR.Indexes: Returns the boolean value at the specified index in the Instance Index array
getInstanceRange(). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries: Gets the number of instances forward to translate values between.
getInstances(). Method in class weka.gui.SetInstancesPanel: Gets the set of instances currently held by the panel
getInstances(). Method in class weka.gui.explorer.PreprocessPanel: Gets the working set of instances.
getInstances(). Method in class weka.gui.treevisualizer.Node: This will return the Instances object related to this node.
getInstances(). Method in class weka.gui.visualize.VisualizePanel: Get the master plot's instances
getInstances(). Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer: Get the training instances
getInstances(). Method in class weka.core.KDTree: Gets instances used in the tree.
getInstances(). Method in class weka.experiment.PairedTTester: Get the value of Instances.
getInstances1(). Method in class weka.gui.visualize.VisualizePanelEvent
getInstances2(). Method in class weka.gui.visualize.VisualizePanelEvent
getInstancesIndices(). Method in class weka.filters.unsupervised.instance.RemoveRange: Gets ranges of instances selected.
getInstList(). Method in class weka.core.KDTree: Gets instance list used in the tree.
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.Remove: 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.RemoveType: Get whether the supplied columns are to be removed or kept
getInvertSelection(). Method in class weka.filters.unsupervised.attribute.NumericTransform: Get whether the supplied columns are to be transformed or not
getInvertSelection(). Method in class weka.filters.unsupervised.instance.RemoveFolds: 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.RemovePercentage: 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
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.SelectedTagEditor: Returns a description of the property value as java source.
getJavaInitializationString(). Method in class weka.gui.GenericObjectEditor: Supposedly returns an initialization string to create a Object identical to the current one, including it's state, but this doesn't appear possible given that the initialization string isn't supposed to contain multiple statements.
getJavaInitializationString(). Method in class weka.gui.FileEditor: Returns a representation of the current property value as java source.
getJavaInitializationString(). Method in class weka.gui.CostMatrixEditor: Returns the Java code that generates an object the same as the one being edited.
getKDTree(). Method in class weka.clusterers.XMeans: Gets the KDTree class.
getKDTree(). Method in class weka.classifiers.lazy.IBk: Gets the KDTree class.
getKernelBandwidth(). Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Get the kernel bandwidth
getKey(). Method in class weka.experiment.ClassifierSplitEvaluator: Gets the key describing the current SplitEvaluator.
getKey(). Method in class weka.experiment.RegressionSplitEvaluator: Gets the key describing the current SplitEvaluator.
getKey(). Method in interface weka.experiment.SplitEvaluator: Gets the key describing the current SplitEvaluator.
getKeyFieldName(). Method in class weka.experiment.AveragingResultProducer: Get the value of KeyFieldName.
getKeyNames(). Method in interface weka.experiment.ResultProducer: Gets the names of each of the key columns produced for a single run.
getKeyNames(). Method in class weka.experiment.ClassifierSplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeyNames(). Method in class weka.experiment.LearningRateResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames(). Method in class weka.experiment.CrossValidationResultProducer: Gets the names of each of the columns produced for a single run.
getKeyNames(). Method in class weka.experiment.RegressionSplitEvaluator: Gets the names of each of the key columns produced for a single run.
getKeyNames(). Method in class weka.experiment.RandomSplitResultProducer: 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.DatabaseResultProducer: 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 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.ClassifierSplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.LearningRateResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.CrossValidationResultProducer: Gets the data types of each of the columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.RegressionSplitEvaluator: Gets the data types of each of the key columns produced for a single run.
getKeyTypes(). Method in class weka.experiment.RandomSplitResultProducer: 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.DatabaseResultProducer: 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.
getKNN(). Method in class weka.classifiers.lazy.IBk: Gets the number of neighbours the learner will use.
getKNN(). Method in class weka.classifiers.lazy.LWL: Gets the number of neighbours used for kernel bandwidth setting.
getKValue(). Method in class weka.classifiers.trees.RandomTree: Get the value of K.
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.
getLearningRate(). Method in class weka.classifiers.functions.neural.NeuralNetwork
getLegendText(). Method in class weka.gui.beans.ChartEvent: Get the legend text vector
getLevel(). Method in class weka.gui.HierarchyPropertyParser: Get the level of current node.
getLikelihoodThreshold(). Method in class weka.classifiers.meta.LogitBoost: Get the value of Precision.
getLine(int). Method in class weka.gui.treevisualizer.Edge: Returns line number n
getLine(int). Method in class weka.gui.treevisualizer.Node: Returns the text String for the specfied line.
getLinkAt(int). Method in class weka.attributeSelection.BestFirst.LinkedList2: returns the element (Link) at a specific index from the list.
getLinkAt(int). Method in class weka.classifiers.rules.DecisionTable.LinkedList: Returns the element (Link) at a specific index from the list.
getList(). Method in class weka.gui.ResultHistoryPanel: Gets the JList used by the results list
getListCellRendererComponent(JList, Object, int, boolean, boolean). Method in class weka.gui.experiment.AlgorithmListPanel.ObjectCellRenderer
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
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.supportVector.SMO: Check whether lower-order terms are being used.
getLowerOrderTerms(). Method in class weka.classifiers.functions.supportVector.SMOreg: Check whether lower-order terms are being used.
getLowerSize(). Method in class weka.experiment.LearningRateResultProducer: Get the value of LowerSize.
getMajorityClass(). Method in class weka.classifiers.rules.Ridor
getMakeBinary(). Method in class weka.filters.superviA>: Gets whether binary attributes should be made for discretized ones.
getMasterPlot(). Method in class weka.gui.visualize.Plot2D: Get the master plot
getMatchMissingValues(). Method in class weka.filters.unsupervised.instance.RemoveWithValues: Gets whether missing values are counted as a match.
getMatrix(int[], int[]). Method in class weka.classifiers.functions.pace.Matrix: Get a submatrix.
getMatrix(int[], int, int). Method in class weka.classifiers.functions.pace.Matrix: Get a submatrix.
getMatrix(int, int, int[]). Method in class weka.classifiers.functions.pace.Matrix: Get a submatrix.
getMatrix(int, int, int, int). Method in class weka.classifiers.functions.pace.Matrix: Get a submatrix.
getMax(). Method in class weka.gui.beans.ChartEvent: Get the max y value
getMaxC(). Method in class weka.gui.visualize.Plot2D: Return the current max value of the colouring attribute
getMaxChunkSize(). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the maximum chunk size
getMaxCost(int). Method in class weka.classifiers.CostMatrix: Gets the maximum cost for a particular class value.
getMaxCount(). Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the value for the max count
getMaxDepth(). Method in class weka.classifiers.trees.REPTree: Get the value of MaxDepth.
getMaxGenerations(). Method in class weka.attributeSelection.GeneticSearch: get the number of generations
getMaximumVariancePercentageAllowed(). Method in class weka.filters.unsupervised.attribute.RemoveUseless: Gets the maximum variance attributes are allowed to have before they are deleted by the filter.
getMaxInstInLeaf(). Method in class weka.core.KDTree: Get the maximum number of instances in a leaf.
getMaxIterations(). Method in class weka.clusterers.EM: Get the maximum number of iterations
getMaxIterations(). Method in class weka.clusterers.XMeans: Gets the maximum number of iterations.
getMaxIterations(). Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the maximum number of cleansing iterations performed
getMaxIterations(). Method in class weka.classifiers.meta.MultiBoostAB: Get the maximum number of boost 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
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.
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.
getMaxPlots(). Method in class weka.gui.beans.AttributeSummarizer: Get the number of plots to display
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
getMaxX(). Method in class weka.gui.visualize.Plot2D: Return the current max value of the attribute plotted on the x axis
getMaxY(). Method in class weka.gui.visualize.Plot2D: Return the current max value of the attribute plotted on the y axis
getMeanSquared(). Method in class weka.classifiers.lazy.IBk: Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation.
getMeasure(String). Method in interface weka.core.AdditionalMeasureProducer: Returns the value of the named measure
getMeasure(String). Method in class weka.experiment.ClassifierSplitEvaluator: Returns the value of the named measure
getMeasure(String). Method in class weka.experiment.LearningRateResultProducer: Returns the value of the named measure
getMeasure(String). Method in class weka.experiment.CrossValidationResultProducer: 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.RandomSplitResultProducer: 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.DatabaseResultProducer: 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.Bagging: 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.Ridor: Returns the value of the named measure
getMeasure(String). Method in class weka.classifiers.rules.JRip: Returns the value of the named measure
getMeasure(String). Method in class weka.classifiers.rules.part.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.RandomForest: Returns the value of the named measure.
getMeasure(String). Method in class weka.classifiers.trees.m5.M5Base: Returns the value of the named measure
getMeasure(String). Method in class weka.classifiers.trees.j48.J48: Returns the value of the named measure
getMeasure(String). Method in class weka.classifiers.trees.adtree.ADTree: Returns the value of the named measure.
getMerit(). Method in class weka.classifiers.rules.DecisionTable.Link: Gets the merit.
getMetaClassifier(). Method in class weka.classifiers.meta.Stacking: Gets the meta classifier.
getMetaClassifier(). Method in class weka.classifiers.meta.Grading: Gets the meta classifiers.
getMetaClassifier(). Method in class weka.classifiers.meta.StackingC: Gets the meta classifier.
getMetaClassifiersX(). Method in class weka.classifiers.meta.Grading: Gets the meta classifiers.
getMetadata(). Method in class weka.core.Attribute: Returns the properties supplied for this attribute.
getMethod(). Method in class weka.classifiers.meta.MultiClassClassifier: Gets the method used.
getMethod(). Method in class weka.classifiers.functions.neural.NeuralNode
getMethodName(). Method in class weka.filters.unsupervised.attribute.NumericTransform: Get the transformation method.
getMetricType(). Method in class weka.associations.Apriori: Get the metric type
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
getMinFunction(). Method in class weka.core.Optimization: Get the minimal function value
getMinimizeExpectedCost(). Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the value of MinimizeExpectedCost.
getMinMetric(). Method in class weka.associations.Apriori: Get the value of minConfidence.
getMinNo(). Method in class weka.classifiers.rules.Ridor
getMinNo(). Method in class weka.classifiers.rules.JRip
getMinNo(). Method in class weka.classifiers.rules.ConjunctiveRule
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.RuleNode: 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
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.
getMinStdDev(). Method in class weka.clusterers.EM: Get the minimum allowable standard deviation.
getMinVarianceProp(). Method in class weka.classifiers.trees.REPTree: Get the value of MinVarianceProp.
getMinX(). Method in class weka.gui.visualize.Plot2D: Return the current min value of the attribute plotted on the x axis
getMinY(). Method in class weka.gui.visualize.Plot2D: Return the current min value of the attribute plotted on the y axis
getMissingMerge(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: 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.ChiSquaredAttributeEval: 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
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
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
getMutationProb(). Method in class weka.attributeSelection.GeneticSearch: get the probability of mutation
getName(). Method in class weka.gui.visualize.VisualizePanel: Returns the name associated with this plot.
getName(). Method in class weka.filters.unsupervised.attribute.AddExpression: Returns the name of the new attribute
getNameAtIndex(int). Method in class weka.gui.ResultHistoryPanel: Gets the name of theitem in the list at the specified index
getNamedBuffer(String). Method in class weka.gui.ResultHistoryPanel: Gets the named buffer
getNamedObject(String). Method in class weka.gui.ResultHistoryPanel: Get the named object from the list
getNext(int). Method in class weka.classifiers.functions.supportVector.SMOset: Gets the next element in the set.
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.CSVLoader: CSVLoader is unable to process a data set incrementally.
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 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.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.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.
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
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.
getNormalize(). Method in class weka.core.KDTree: Gets the normalize flag.
getNormalize(). Method in class weka.attributeSelection.PrincipalComponents: Gets whether or not input data is to be normalized
getNormalizeAttributes(). Method in class weka.classifiers.functions.neural.NeuralNetwork
getNormalizeNumericClass(). Method in class weka.classifiers.functions.neural.NeuralNetwork
getNotes(). Method in class weka.experiment.Experiment: Get the user notes.
getNPointPrecision(Instances, int). Static method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the n point precision result, which is the precision averaged over n evenly spaced (w.r.t recall) samples of the curve.
GetNrOfParents(). Method in class weka.classifiers.bayes.ParentSet: returns number of parents
getNumAntds(). Method in class weka.classifiers.rules.ConjunctiveRule
getNumAttemptsOfGeneOption(). Method in class weka.classifiers.rules.Nnge: Gets the number of attempts for generalisation.
getNumAttributes(). Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes in the dataset
getNumAttributesSet(). Method in class weka.classifiers.lazy.LBR.Indexes: Returns the number of attributes "in use"
getNumClusters(). Method in class weka.clusterers.ClusterEvaluation: Return the number of clusters found for the most recent call to evaluateClusterer
getNumClusters(). Method in class weka.clusterers.FarthestFirst: gets the number of clusters to generate
getNumClusters(). Method in class weka.clusterers.SimpleKMeans: gets the number of clusters to generate
getNumDatasets(). Method in class weka.experiment.PairedTTester: Gets the number of datasets in the resultsets
getNumeric(). Method in class weka.filters.unsupervised.attribute.MakeIndicator: Check if new attribute is to be numeric.
getNumFeatures(). Method in class weka.classifiers.trees.RandomForest: Get the number of features used in random selection.
getNumFoldersMIOption(). Method in class weka.classifiers.rules.Nnge: Gets the number of folder for mutual information.
getNumFolds(). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the number of folds in which dataset is to be split into.
getNumFolds(). Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the number of folds in which dataset is to be split into.
getNumFolds(). Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the number of cross-validation folds used by the filter.
getNumFolds(). Method in class weka.experiment.CrossValidationResultProducer: Get the value of NumFolds.
getNumFolds(). Method in class weka.classifiers.meta.Stacking: Gets the number of folds for the cross-validation.
getNumFolds(). Method in class weka.classifiers.meta.MultiScheme: Gets the number of folds for cross-validation.
getNumFolds(). Method in class weka.classifiers.meta.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.Grading: Gets the number of folds for the cross-validation.
getNumFolds(). Method in class weka.classifiers.meta.StackingC: 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.classifiers.functions.supportVector.SMO: Get the value of numFolds.
getNumGeneratingModels(). Method in class weka.gui.boundaryvisualizer.KDDataGenerator: Return the number of kernels (there is one per training instance)
getNumGeneratingModels(). Method in interface weka.gui.boundaryvisualizer.DataGenerator: Returns the number of generating models used by this DataGenerator
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"
getNumIterations(). Method in class weka.classifiers.meta.Bagging: Gets the number of bagging iterations
getNumIterations(). Method in class weka.classifiers.meta.MetaCost: Gets the number of bagging iterations
getNumIterations(). Method in class weka.classifiers.functions.VotedPerceptron: Get the value of NumIterations.
getNumIterations(). Method in class weka.classifiers.functions.Winnow: Get the value of numIterations.
getNumNeighbours(). Method in class weka.attributeSelection.ReliefFAttributeEval: Get the number of nearest neighbours
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
getNumResultsets(). Method in class weka.experiment.PairedTTester: Gets the number of resultsets in the data.
getNumRules(). Method in class weka.associations.Apriori: Get the value of numRules.
getNumRuns(). Method in class weka.classifiers.meta.LogitBoost: Get the value of NumRuns.
getNumSamplesPerRegion(). Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Get the number of points to sample from a region (fixed dimensions).
getNumSubCmtys(). Method in class weka.classifiers.meta.MultiBoostAB: Get the number of sub committees to use
getNumSymbols(). Method in class weka.estimators.DiscreteEstimator: Gets the number of symbols this estimator operates with
getNumSymbols(). Method in class weka.classifiers.bayes.DiscreteEstimatorBayes: Gets the number of symbols this estimator operates with
getNumToSelect(). Method in 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.ForwardSelection: Gets the number of attributes to be retained.
getNumToSelect(). Method in class weka.attributeSelection.Ranker: Gets the number of attributes to be retained.
getNumTraining(). Method in class weka.classifiers.lazy.IBk: Get the number of training instances the classifier is currently using
getNumTrees(). Method in class weka.classifiers.trees.RandomForest: Get the value of numTrees.
getNumXValFolds(). Method in class weka.classifiers.meta.ThresholdSelector: Get the number of folds used for cross-validation.
getObject(). Method in class weka.core.SerializedObject: Returns a serialized object.
getOnDemandDirectory(). Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator: Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory(). Method in class weka.classifiers.meta.MetaCost: Returns the directory that will be searched for cost files when loading on demand.
getOnDemandDirectory(). Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns the directory that will be searched for cost files when loading on demand.
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 interface weka.core.OptionHandler: Gets the current option settings for the OptionHandler.
getOptions(). Method in class weka.core.KDTree: Gets the current settings of KDtree.
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.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.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.filters.supervised.attribute.ClassOrder: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.supervised.attribute.NominalToBinary: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.supervised.attribute.Discretize: Gets the current settings of the filter.
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.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.Remove: 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.Add: 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.Copy: 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.SwapValues: 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.RemoveUseless: 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.AddCluster: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.unsupervised.attribute.NominalToBinary: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.unsupervised.attribute.StringToNominal: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.unsupervised.attribute.PKIDiscretize: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.unsupervised.attribute.MakeIndicator: 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.instance.RemoveFolds: 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.RemoveMisclassified: 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.RemovePercentage: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.unsupervised.instance.Resample: Gets the current settings of the filter.
getOptions(). Method in class weka.filters.unsupervised.instance.RemoveWithValues: Gets the current settings of the filter.
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.LearningRateResultProducer: Gets the current settings of the result producer.
getOptions(). Method in class weka.experiment.PairedTTester: Gets current settings of the PairedTTester.
getOptions(). Method in class weka.experiment.CSVResultListener: 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.RegressionSplitEvaluator: Gets the current settings of the Classifier.
getOptions(). Method in class weka.experiment.RandomSplitResultProducer: 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.AveragingResultProducer: 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.InstanceQuery: Gets the current settings of InstanceQuery
getOptions(). Method in class weka.attributeSelection.ExhaustiveSearch: Gets the current settings of RandomSearch.
getOptions(). Method in class weka.attributeSelection.SVMAttributeEval: Gets the current settings of SVMAttributeEval
getOptions(). Method in class weka.attributeSelection.RankSearch: Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.attributeSelection.RaceSearch: Gets the current settings of BestFirst.
getOptions(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.attributeSelection.GainRatioAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.attributeSelection.RandomSearch: 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.GeneticSearch: Gets the current settings of ReliefFAttributeEval.
getOptions(). Method in class weka.attributeSelection.CfsSubsetEval: Gets the current settings of CfsSubsetEval
getOptions(). Method in class weka.attributeSelection.BestFirst: Gets the current settings of BestFirst.
getOptions(). Method in class weka.attributeSelection.ReliefFAttributeEval: Gets the current settings of ReliefFAttributeEval.
getOptions(). Method in class weka.attributeSelection.Ranker: Gets the current settings of ReliefFAttributeEval.
getOptions(). Method in class weka.attributeSelection.ChiSquaredAttributeEval: Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.attributeSelection.InfoGainAttributeEval: 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.PrincipalComponents: Gets the current settings of PrincipalComponents
getOptions(). Method in class weka.attributeSelection.WrapperSubsetEval: Gets the current settings of WrapperSubsetEval.
getOptions(). Method in class weka.associations.Apriori: Gets the current settings of the Apriori object.
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.lazy.IBk: Gets the current settings of IBk.
getOptions(). Method in class weka.classifiers.lazy.LWL: 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.AdditiveRegression: 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.DistributionMetaClassifier: Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.meta.Stacking: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.MultiBoostAB: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.Vote: 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.ThresholdSelector: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.MetaCost: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.MultiScheme: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.MultiClassClassifier: 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.OrdinalClassClassifier: 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.CVParameterSelection: 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.Grading: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.StackingC: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.CostSensitiveClassifier: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.RegressionByDiscretization: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: 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.misc.VFI: Gets the current settings of VFI
getOptions(). Method in class weka.classifiers.bayes.BayesNet: Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.bayes.AODE: Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.bayes.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.rules.DecisionTable: Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.rules.Ridor: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.rules.JRip: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.rules.ConjunctiveRule: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.rules.Nnge: Gets the current option settings for the OptionHandler.
getOptions(). Method in class weka.classifiers.rules.OneR: Gets the current settings of the OneR classifier.
getOptions(). Method in class weka.classifiers.rules.part.PART: Gets the current settings of the Classifier.
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.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.classifiers.trees.j48.J48: Gets the current settings of the Classifier.
getOptions(). Method in class weka.classifiers.trees.adtree.ADTree: Gets the current settings of ADTree.
getOptions(). Method in class weka.classifiers.functions.LeastMedSq: Gets the current option settings for the OptionHandler.
getOptions(). Method in class weka.classifiers.functions.VotedPerceptron: Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.functions.LinearRegression: Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.functions.Winnow: 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.pace.PaceRegression: 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.supportVector.SMO: Gets the current settings of the classifier.
getOptions(). Method in class weka.classifiers.functions.supportVector.SMOreg: Gets the current settings of the classifier.
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.CSVResultListener: Get the value of OutputFile.
getOutputFile(). Method in class weka.experiment.CrossValidationResultProducer: Get the value of OutputFile.
getOutputFile(). Method in class weka.experiment.RandomSplitResultProducer: Get the value of OutputFile.
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.
getOutputs(). Method in class weka.classifiers.functions.neural.NeuralConnection: Use this to get easy access to the outputs.
getOutputWordCounts(). Method in class weka.filters.unsupervised.attribute.StringToWordVector: Gets whether output instances contain 0 or 1 indicating word presence, or word counts.
getParent(int). Method in class weka.gui.treevisualizer.Node: Get the parent edge.
GetParent(int). Method in class weka.classifiers.bayes.ParentSet: returns index parent of parent specified by index
getPath(). Method in class weka.gui.PropertySelectorDialog: Gets the path of property nodes to the selected property.
getPercent(). Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the size of noise data as a percentage of the original set.
getPercentage(). Method in class weka.filters.unsupervised.instance.RemovePercentage: Gets the percentage of instances to select.
getPercentCompleted(). Method in class weka.gui.boundaryvisualizer.RemoteResult: Return the progress for this row
getPercentThreshold(). Method in class weka.attributeSelection.SVMAttributeEval: Get the threshold below which percentage elimination reverts to constant elimination.
getPercentToEliminatePerIteration(). Method in class weka.attributeSelection.SVMAttributeEval: Get the percentage rate of attribute elimination per iteration
getPlotInstances(). Method in class weka.gui.visualize.PlotData2D: Returns the instances for this plot
getPlotName(). Method in class weka.gui.visualize.PlotData2D: Get the name of this plot
getPlots(). Method in class weka.gui.visualize.Plot2D: Return the list of plots
getPlotTrainingData(). Method in class weka.gui.boundaryvisualizer.BoundaryPanel: Returns true if training data is to be superimposed
getPointValue(int). Method in class weka.classifiers.functions.pace.DiscreteFunction: Gets a particular point value
getPopulationSize(). Method in class weka.attributeSelection.GeneticSearch: get the size of the population
getPrecision(). Method in class weka.classifiers.evaluation.TwoClassStats: Calculate the precision.
getPrediction(DistributionClassifier, Instance). Method in class weka.classifiers.evaluation.EvaluationUtils: Generate a single prediction for a test instance given the pre-trained classifier.
getProbabilities(). Method in class weka.gui.boundaryvisualizer.RemoteResult: Return the probability distributions for this row in the visualization
getProbability(double). Method in class weka.estimators.NormalEstimator: 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 class weka.estimators.MahalanobisEstimator: Get a probability estimate for a value
getProbability(double). Method in class weka.estimators.KernelEstimator: Get a probability estimate for a value.
getProbability(double). Method in interface weka.estimators.Estimator: Get a probability estimate for a value.
getProbability(double). Method in class weka.estimators.PoissonEstimator: Get a probability estimate for a value
getProbability(double). Method in class weka.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.KDConditionalEstimator: Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.DKConditionalEstimator: Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.NDConditionalEstimator: Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.KKConditionalEstimator: Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.DNConditionalEstimator: Get a probability estimate for a value
getProbability(double, double). Method in class weka.estimators.NNConditionalEstimator: 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.CrossValidationFoldMakerBeanInfo: Return the property descriptors for this bean
getPropertyDescriptors(). Method in class weka.gui.beans.ClassAssignerBeanInfo: Returns the property descriptors
getPropertyDescriptors(). Method in class weka.gui.beans.TrainTestSplitMakerBeanInfo: Get the property descriptors for this bean
getPropertyDescriptors(). Method in class weka.gui.beans.StripChartBeanInfo: Get the property descriptors for this bean
getPropertyPath(). Method in class weka.experiment.Experiment: Gets the path of properties taken to get to the custom property to iterate over.
getPrune(). Method in class weka.core.KDTree: Gets the pruning flag.
getPruningType(). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the pruning type
getQuery(). Method in class weka.experiment.InstanceQuery: Get the query to execute against the database
getRaceType(). Method in class weka.attributeSelection.RaceSearch: Get the race type
getRandomized(). Method in class weka.classifiers.rules.ConjunctiveRule
getRandomizeData(). Method in class weka.experiment.RandomSplitResultProducer: Get if dataset is to be randomized
getRandomOrder(). Method in class weka.classifiers.bayes.BayesNetK2: Get random order flag
getRandomSeed(). Method in class weka.filters.supervised.instance.Resample: Gets the random number seed.
getRandomSeed(). Method in class weka.filters.supervised.instance.SpreadSubsample: Gets the random number seed.
getRandomSeed(). Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the random number seed.
getRandomSeed(). Method in class weka.filters.unsupervised.instance.Randomize: Get the random number generator seed value.
getRandomSeed(). Method in class weka.filters.unsupervised.instance.Resample: Gets the random number seed.
getRandomSeed(). Method in class weka.classifiers.trees.adtree.ADTree: Gets random seed for a random walk.
getRandomSeed(). Method in class weka.classifiers.functions.LeastMedSq: get the seed for the random number generator
getRandomSeed(). Method in class weka.classifiers.functions.neural.NeuralNetwork
getRandomSeed(). Method in class weka.classifiers.functions.supportVector.SMO: Get the value of randomSeed.
getRandomWidthFactor(). Method in class weka.classifiers.meta.MultiClassClassifier: Gets the multiplier when generating random codes.
getRangeCorrection(). Method in class weka.classifiers.meta.ThresholdSelector: Gets the confidence range correction mode used.
getRanges(). Method in class weka.core.Range: Gets the string representing the selected range of values
getRanges(). Method in class weka.core.Instances: Method to get the ranges.
getRawOutput(). Method in class weka.experiment.RegressionSplitEvaluator: Gets the raw output from the classifier
getRawResultOutput(). Method in interface weka.experiment.SplitEvaluator: Returns the raw output for the most recent call to getResult.
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.RuleNode: Get the value of regressionTree.
getRegressionTree(). Method in class weka.classifiers.trees.m5.Rule: Get the value of regressionTree.
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
getReportFrequency(). Method in class weka.attributeSelection.GeneticSearch: get how often repports are generated
getReset(). Method in class weka.gui.beans.ChartEvent: get the value of the reset flag
getReset(). Method in class weka.classifiers.functions.neural.NeuralNetwork
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.RegressionSplitEvaluator: Gets the results for the supplied train and test datasets.
getResult(Instances, Instances). Method in interface weka.experiment.SplitEvaluator: Gets the results for the supplied train and test datasets.
getResultFromTable(String, ResultProducer, Object[]). Method in class weka.experiment.DatabaseUtils: Executes a database query to extract a result for the supplied key from the database.
getResultListener(). Method in class weka.experiment.Experiment: Gets the result listener where results will be sent.
getResultNames(). Method in interface weka.experiment.ResultProducer: Gets the names of each of the result columns produced for a single run.
getResultNames(). Method in class weka.experiment.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.LearningRateResultProducer: Gets the names of each of the 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.RegressionSplitEvaluator: Gets the names of each of the result 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.AveragingResultProducer: 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 interface weka.experiment.SplitEvaluator: Gets the names of each of the result columns produced for a single run.
getResultProducer(). Method in class weka.experiment.LearningRateResultProducer: 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.AveragingResultProducer: Get the ResultProducer.
getResultProducer(). Method in class weka.experiment.DatabaseResultProducer: Get the ResultProducer.
getResultSet(). Method in class weka.experiment.DatabaseUtils: Gets the results generated by a previous query.
getResultsetKeyColumns(). Method in class weka.experiment.PairedTTester: Get the value of ResultsetKeyColumns.
getResultsetName(int). Method in class weka.experiment.PairedTTester: Gets a string descriptive of the specified resultset.
getResultsTableName(ResultProducer). Method in class weka.experiment.DatabaseUtils: Gets the name of the experiment table that stores results from a particular ResultProducer.
getResultTypes(). Method in interface weka.experiment.ResultProducer: Gets the data types of each of the result columns produced for a single run.
getResultTypes(). Method in class weka.experiment.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.LearningRateResultProducer: Gets the data types of each of the 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.RegressionSplitEvaluator: Gets the data types of each of the result 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.AveragingResultProducer: 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 interface weka.experiment.SplitEvaluator: Gets the data types of each of the result columns produced for a single run.
getRidge(). Method in class weka.classifiers.functions.LinearRegression: Get the value of Ridge.
getRidge(). Method in class weka.classifiers.functions.Logistic: Gets the ridge in the log-likelihood.
getROCArea(Instances). Static method in class weka.classifiers.evaluation.ThresholdCurve: Calculates the area under the ROC curve.
getRoot(). Method in class weka.gui.treevisualizer.Node: Get the value of root.
getRow(int). Method in class weka.core.Matrix: Gets a row of the matrix and returns it as double array.
getRowDimension(). Method in class weka.classifiers.functions.pace.Matrix: Get row dimension.
getRowPackedCopy(). Method in class weka.classifiers.functions.pace.Matrix: Make a one-dimensional row packed copy of the internal array.
getRsource(). Method in class weka.gui.treevisualizer.Edge: Get the value of rsource.
getRtarget(). Method in class weka.gui.treevisualizer.Edge: Get the value of rtarget.
getRuleset(). Method in class weka.classifiers.rules.JRip: Get the ruleset generated by Ripper
getRuleset(). Method in class weka.classifiers.rules.RuleStats: Get the ruleset of the stats
getRulesetSize(). Method in class weka.classifiers.rules.RuleStats: Get the size of the ruleset in the stats
getRuleStats(int). Method in class weka.classifiers.rules.JRip: Get the statistics of the ruleset in the given position
getRunColumn(). Method in class weka.experiment.PairedTTester: Get the value of RunColumn.
getRunLower(). Method in class weka.experiment.Experiment: Get the lower run number for the experiment.
getRunUpper(). Method in class weka.experiment.Experiment: Get the upper run number for the experiment.
getSampleSize(). Method in class weka.attributeSelection.ReliefFAttributeEval: Get the number of instances used for estimating attributes
getSampleSize(). Method in class weka.classifiers.functions.LeastMedSq: gets number of samples
getSampleSizePercent(). Method in class weka.filters.supervised.instance.Resample: Gets the subsample size as a percentage of the original set.
getSampleSizePercent(). Method in class weka.filters.unsupervised.instance.Resample: Gets the subsample size as a percentage of the original set.
getSaveInstanceData(). Method in class weka.clusterers.Cobweb: Get the value of saveInstances.
getSaveInstanceData(). Method in class weka.classifiers.trees.j48.J48: Check whether instance data is to be saved.
getSaveInstanceData(). Method in class weka.classifiers.trees.adtree.ADTree: Gets whether the tree is to save instance data.
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.filters.supervised.attribute.AttributeSelection: Get the name of the search method
getSearch(). Method in class weka.classifiers.meta.AttributeSelectedClassifier: Gets the search method used
getSearchPath(). Method in class weka.classifiers.trees.adtree.ADTree: Gets the method of searching the tree for a new insertion.
getSearchPercent(). Method in class weka.attributeSelection.RandomSearch: get the percentage of the search space to consider
getSearchTermination(). Method in class weka.attributeSelection.BestFirst: Get the termination criterion (number of non-improving nodes).
getSecondValueIndex(). Method in class weka.filters.unsupervised.attribute.MergeTwoValues: Get the index of the second value used.
getSecondValueIndex(). Method in class weka.filters.unsupervised.attribute.SwapValues: Get the index of the second value used.
getSeed(). Method in class weka.gui.beans.TrainTestSplitMaker: Get the value of the random seed
getSeed(). Method in class weka.gui.beans.CrossValidationFoldMaker: Get the currently set seed
getSeed(). Method in interface weka.core.Randomizable: Gets the seed for the random number generations
getSeed(). Method in class weka.clusterers.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 class weka.filters.supervised.attribute.ClassOrder: Get the current randomization seed
getSeed(). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds: Gets the random number seed used for shuffling the dataset.
getSeed(). Method in class weka.filters.unsupervised.instance.RemoveFolds: Gets the random number seed used for shuffling the dataset.
getSeed(). Method in class weka.attributeSelection.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.meta.Stacking: Gets the random number seed.
getSeed(). Method in class weka.classifiers.meta.MultiBoostAB: Get seed for resampling.
getSeed(). Method in class weka.classifiers.meta.Vote: Gets the random number seed.
getSeed(). Method in class weka.classifiers.meta.Bagging: Gets the seed for the random number generations
getSeed(). Method in class weka.classifiers.meta.ThresholdSelector: Gets the random number seed.
getSeed(). Method in class weka.classifiers.meta.MetaCost: Get seed for resampling.
getSeed(). Method in class weka.classifiers.meta.MultiScheme: Gets the random number seed.
getSeed(). Method in class weka.classifiers.meta.MultiClassClassifier: Gets the random number seed.
getSeed(). Method in class weka.classifiers.meta.AdaBoostM1: Get seed for resampling.
getSeed(). Method in class weka.classifiers.meta.CVParameterSelection: Gets the random number seed.
getSeed(). Method in class weka.classifiers.meta.LogitBoost: Get seed for resampling.
getSeed(). Method in class weka.classifiers.meta.Grading: Gets the random number seed.
getSeed(). Method in class weka.classifiers.meta.StackingC: Gets the random number seed.
getSeed(). Method in class weka.classifiers.meta.CostSensitiveClassifier: Get seed for resampling.
getSeed(). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get seed for resampling.
getSeed(). Method in class weka.classifiers.rules.Ridor
getSeed(). Method in class weka.classifiers.rules.JRip
getSeed(). Method in class weka.classifiers.rules.ConjunctiveRule
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.classifiers.evaluation.EvaluationUtils: Gets the seed for randomization during cross-validation
getSeed(). Method in class weka.classifiers.functions.VotedPerceptron: Get the value of Seed.
getSeed(). Method in class weka.classifiers.functions.Winnow: Get the value of Seed.
getSelectedAttributes(). Method in class weka.gui.AttributeSelectionPanel: Gets an array containing the indices of all selected attributes.
getSelectedBuffer(). Method in class weka.gui.ResultHistoryPanel: Gets the buffer associated with the currently selected item in the list.
getSelectedName(). Method in class weka.gui.ResultHistoryPanel: Get the name of the currently selected item in the list
getSelectedObject(). Method in class weka.gui.ResultHistoryPanel: Gets the object associated with the currently selected item in the list.
getSelectedRange(). Method in class weka.filters.unsupervised.attribute.StringToWordVector: Get the value of m_SelectedRange.
getSelectedTag(). Method in class weka.core.SelectedTag: Gets the selected Tag.
getSelection(). Method in class weka.core.Range: Gets an array containing all the selected values, in the order that they were selected (or ascending order if range inversion is on)
getSelectionModel(). Method in class weka.gui.ResultHistoryPanel: Gets the selection model used by the results list.
getSelectionModel(). Method in class weka.gui.AttributeSelectionPanel: Gets the selection model used by the table.
getSelectionModel(). Method in class weka.gui.AttributeListPanel: Gets the selection model used by the table.
getSelectionThreshold(). Method in class weka.attributeSelection.RaceSearch: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
getSeparatingTture.html">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.
getShowStdDevs(). Method in class weka.experiment.PairedTTester: Returns true if standard deviations have been requested.
getShrinkage(). Method in class weka.classifiers.meta.AdditiveRegression: Get the shrinkage rate.
getShrinkage(). Method in class weka.classifiers.meta.LogitBoost: Get the value of Shrinkage.
getShuffle(). Method in class weka.classifiers.rules.Ridor
getSigma(). Method in class weka.attributeSelection.ReliefFAttributeEval: Get the value of sigma.
getSigma(). Method in class weka.classifiers.BVDecompose: Get the calculated sigma squared
getSignificanceLevel(). Method in class weka.experiment.PairedTTester: Get the value of SignificanceLevel.
getSignificanceLevel(). Method in class weka.attributeSelection.RaceSearch: Get the significance level
getSignificanceLevel(). Method in class weka.associations.Apriori: Get the value of significanceLevel.
getSimpleStats(int). Method in class weka.classifiers.rules.RuleStats: Get the simple stats of one rule, including 6 parameters: 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives; 4: false positives; 5: false negatives
getSIndex(). Method in class weka.gui.visualize.VisualizePanel: Get the index of the shape selected for creating splits.
getSmoothing(). Method in class weka.classifiers.trees.m5.RuleNode: Method declaration
getSmoothing(). Method in class weka.classifiers.trees.m5.Rule: Get whether or not smoothing has been turned on
getSource(). Method in class weka.gui.treevisualizer.Edge: Get the value of source.
getSparseData(). Method in class weka.experiment.InstanceQuery: Gets whether data is to be returned as a set of sparse instances
getSplitByDataSet(). Method in class weka.experiment.RemoteExperiment: Returns true if sub experiments are to be created on the basis of data set..
getSplitEvaluator(). Method in class weka.experiment.CrossValidationResultProducer: Get the SplitEvaluator.
getSplitEvaluator(). Method in class weka.experiment.RandomSplitResultProducer: Get the SplitEvaluator.
getSplitPoint(). Method in class weka.filters.unsupervised.instance.RemoveWithValues: Get the split point used for numeric selection
getStartSet(). Method in class weka.attributeSelection.ExhaustiveSearch: Returns a list of attributes (and or attribute ranges) as a String
getStartSet(). Method in class weka.attributeSelection.RandomSearch: 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.BestFirst: 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
getStatus(). Method in class weka.gui.beans.IncrementalClassifierEvent: Get the status
getStatus(). Method in class weka.gui.beans.InstanceEvent: Get the status
getStatusMessage(). Method in class weka.experiment.TaskStatusInfo: Get the status message.
getStepSize(). Method in class weka.experiment.LearningRateResultProducer: Get the value of StepSize.
getStructure(). Method in class weka.core.converters.AbstractLoader
getStructure(). Method in class weka.core.converters.CSVLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
getStructure(). Method in class weka.core.converters.C45Loader: 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.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.SerializedInstancesLoader: Determines and returns (if possible) the structure (internally the header) of the data set as an empty set of instances.
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.gui.GenericArrayEditor: Returns null as we don't support getting values as tags.
getTags(). Method in class weka.gui.SelectedTagEditor: Gets the list of tags that can be selected from.
getTags(). Method in class weka.gui.GenericObjectEditor: Returns null as we don't support getting values as tags.
getTags(). Method in class weka.gui.CostMatrixEditor: Some objects can return tags, but a cost matrix cannot.
getTags(). Method in class weka.core.SelectedTag: Gets the set of all valid Tags.
getTarget(). Method in class weka.gui.treevisualizer.Edge: Get the value of target.
getTaskResult(). Method in class weka.experiment.TaskStatusInfo: Get the returnable result of this task.
getTaskStatus(). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask: Return status information for this sub task
getTaskStatus(). Method in interface weka.experiment.Task: Clients should be able to call this method at any time to obtain information on a current task.
getTaskStatus(). Method in class weka.experiment.RemoteExperimentSubTask
getTestPredictions(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.TextEvent: Describe getText method here.
getText(). Method in class weka.gui.beans.BeanVisual: Get the visual's label
getTextTitle(). Method in class weka.gui.beans.TextEvent: Describe getTextTitle method here.
getThreshold(). Method in class weka.filters.unsupervised.instance.RemoveMisclassified: Gets the threshold for the max error when predicting a numeric class.
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.ForwardSelection: Returns the threshold so that the AttributeSelection module can discard attributes from the ranking.
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
getThresholdInstance(Instances, double). Static method in class weka.classifiers.evaluation.ThresholdCurve: Gets the index of the instance with the closest threshold value to the desired target
getTimestamp(). Static method in class weka.experiment.CrossValidationResultProducer: Gets a Double representing the current date and time.
getTimestamp(). Static method in class weka.experiment.RandomSplitResultProducer: Gets a Double representing the current date and time.
getToken(StreamTokenizer). Static method in class weka.core.converters.ConverterUtils: Gets token.
getToleranceParameter(). Method in class weka.attributeSelection.SVMAttributeEval: Get the value of T used with SMO
getToleranceParameter(). Method in class weka.classifiers.functions.supportVector.SMO: Get the value of tolerance parameter.
getToleranceParameter(). Method in class weka.classifiers.functions.supportVector.SMOreg: Get the value of tolerance parameter.
getToolTipText(MouseEvent). Method in class weka.gui.AttributeVisualizationPanel: Returns "<nominal value> [<nominal value count>]" if displaying a bar plot and mouse is on some bar.
getTop(). Method in class weka.gui.treevisualizer.Node: Get the value of top.
getTotalCount(Node, int). Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of nodes there are.
getTotalGCount(Node, int). Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of groups of siblings there are.
getTotalHeight(Node, int). Static method in class weka.gui.treevisualizer.Node: Recursively finds the total number of levels there are.
getTrainingSet(). Method in class weka.gui.beans.TrainingSetEvent: Get the training instances
getTrainingTime(). Method in class weka.classifiers.functions.neural.NeuralNetwork
getTrainIterations(). Method in class weka.classifiers.BVDecompose: Gets the maximum number of boost iterations
getTrainPercent(). Method in class weka.gui.beans.TrainTestSplitMaker: Get the percentage of the data that will be in the training portion of the split
getTrainPercent(). Method in class weka.experiment.RandomSplitResultProducer: Get the value of TrainPercent.
getTrainPoolSize(). Method in class weka.classifiers.BVDecompose: Get the number of instances in the training pool.
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.
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.
getUnpruned(). Method in class weka.classifiers.rules.part.PART: 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
getUnpruned(). Method in class weka.classifiers.trees.j48.J48: Get the value of unpruned.
getUpdateIncrementalClassifier(). Method in class weka.gui.beans.Classifier
getUpper(). Method in class weka.gui.experiment.RunNumberPanel: Gets the current upper run number.
getUpperBoundMinSupport(). Method in class weka.associations.Apriori: Get the value of upperBoundMinSupport.
getUpperNumericBound(). Method in class weka.core.Attribute: Returns the upper bound of a numeric attribute.
getUpperSize(). Method in class weka.experiment.LearningRateResultProducer: Get the value of UpperSize.
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.
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.
getUseIBk(). Method in class weka.classifiers.rules.DecisionTable: Gets whether IBk is being used instead of the majority class
getUseKernelEstimator(). Method in class weka.classifiers.bayes.NaiveBayes: Gets if kernel estimator is being used.
getUseKononenko(). Method in class weka.filters.supervised.attribute.Discretize: Gets whether Kononenko's MDL criterion is to be used.
getUseLaplace(). Method in class weka.classifiers.trees.j48.J48: Get the value of useLaplace.
getUseMissing(). Method in class weka.filters.unsupervised.attribute.AddNoise: Gets the flag if missing values are treated as extra values.
getUsePropertyIterator(). Method in class weka.experiment.Experiment: Gets whether the custom property iterator should be used.
getUsePruning(). Method in class weka.classifiers.rules.JRip
getUseRBF(). Method in class weka.classifiers.functions.supportVector.SMO: Check if the RBF kernel is to be used.
getUseRBF(). Method in class weka.classifiers.functions.supportVector.SMOreg: Check if the RBF kernel is to be used.
getUseResampling(). Method in class weka.classifiers.meta.MultiBoostAB: Get whether resampling is turned on
getUseResampling(). Method in class weka.classifiers.meta.AdaBoostM1: Get whether resampling is turned on
getUseResampling(). Method in class weka.classifiers.meta.LogitBoost: Get whether resampling is turned on
getUseResampling(). Method in class weka.classifiers.meta.getUseTraining(). Method in class weka.attributeSelection.ClassifierSubsetEval: Get if training data is to be used instead of hold out/test data
getUseTree(). Method in class weka.classifiers.trees.m5.Rule: get whether an m5 tree is being used rather than rules
getUseUnsmoothed(). Method in class weka.classifiers.trees.m5.M5Base: Get whether or not smoothing is being used
getValidationChunkSize(). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost: Get the validation chunk size
getValidationSetSize(). Method in class weka.classifiers.functions.neural.NeuralNetwork
getValidationThreshold(). Method in class weka.classifiers.functions.neural.NeuralNetwork
getValue(). Method in class weka.gui.GenericArrayEditor: Gets the current object array.
getValue(). Method in class weka.gui.HierarchyPropertyParser: Get the value of current node
getValue(). Method in class weka.gui.GenericObjectEditor: Gets the current Object.
getValue(). Method in class weka.gui.CostMatrixEditor: Gets the cost matrix that is being edited.
getValue(). Method in class weka.classifiers.trees.adtree.PredictionNode: Gets the prediction value of the node.
getValueIndices(). Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the indices of the indicator values.
getValueRange(). Method in class weka.filters.unsupervised.attribute.MakeIndicator: Get the range containing the indicator values.
getValues(). Method in class weka.gui.visualize.VisualizePanelEvent
getVarbValues(). Method in class weka.core.Optimization: Get the variable values.
getVariance(). Method in class weka.classifiers.BVDecompose: Get the calculated variance
getVarianceCovered(). Method in class weka.attributeSelection.PrincipalComponents: Gets the proportion of total variance to account for when retaining principal components
getVerbose(). Method in class weka.attributeSelection.ExhaustiveSearch: get whether or not output is verbose
getVerbose(). Method in class weka.attributeSelection.RandomSearch: get whether or not output is verbose
getVisible(). Method in class weka.gui.treevisualizer.Node: Get the value of visible.
getVisual(). Method in class weka.gui.beans.AbstractDataSource: Get the visual being used by this data source.
getVisual(). Method in interface weka.gui.beans.Visible: Get the visual representation
getVisual(). Method in class weka.gui.beans.DataVisualizer: Return the visual appearance of this bean
getVisual(). Method in class weka.gui.beans.AbstractTestSetProducer: Get the visual for this bean
getVisual(). Method in class weka.gui.beans.AbstractEvaluator: Get the visual
getVisual(). Method in class weka.gui.beans.Filter: Get the visual appearance of this bean
getVisual(). Method in class weka.gui.beans.AbstractTrainingSetProducer: Get the visual for this bean
getVisual(). Method in class weka.gui.beans.Classifier: Gets the visual appearance of this wrapper bean
getVisual(). Method in class weka.gui.beans.AbstractTrainAndTestSetProducer: Get the visual for this bean
getVisual(). Method in class weka.gui.beans.TextViewer: 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.GraphViewer: Get the visual appearance of this bean
getVisual(). Method in class weka.gui.beans.ClassAssigner
getWeightByConfidence(). Method in class weka.classifiers.misc.VFI: Get whether feature intervals are being weighted by confidence
getWeightByDistance(). Method in class weka.attributeSelection.ReliefFAttributeEval: Get whether nearest neighbours are being weighted by distance
getWeightingKernel(). Method in class weka.classifiers.lazy.LWL: Gets the kernel weighting method to use.
getWeights(). Method in class weka.gui.boundaryvisualizer.KDDataGenerator
getWeights(). Method in interface weka.gui.boundaryvisualizer.DataGenerator: Get weights
getWeights(). Method in class weka.classifiers.functions.neural.NeuralNode: call this function to get the weights array.
getWeightThreshold(). Method in class weka.classifiers.meta.MultiBoostAB: Get the degree of weight thresholding
getWeightThreshold(). Method in class weka.classifiers.meta.AdaBoostM1: Get the degree of weight thresholding
getWeightThreshold(). Method in class weka.classifiers.meta.LogitBoost: Get the degree of weight thresholding
getWholeDataErr(). Method in class weka.classifiers.rules.Ridor
getWidth(). Method in class weka.gui.beans.BeanInstance: Gets the width of this bean
getWindowSize(). Method in class weka.classifiers.lazy.IBk: Gets the maximum number of instances allowed in the training pool.
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 interface weka.gui.beans.WekaWrapper: Get the algorithm
getWrappedAlgorithm(). Method in class weka.gui.beans.Loader: Get the loader
getWrappedAlgorithm(). Method in class weka.gui.beans.Filter: Get the filter wrapped by this bean
getWrappedAlgorithm(). Method in class weka.gui.beans.Classifier: Returns the wrapped classifier
getX(). Method in class weka.gui.beans.BeanInstance: Gets the x coordinate of this bean
getX(). Method in class weka.classifiers.functions.neural.NeuralConnection
getXindex(). Method in class weka.gui.visualize.PlotData2D: Get the currently set x index of the data
getXIndex(). Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute on the x axis
getXLabelFreq(). Method in class weka.gui.beans.StripChart: Get the frequency by which x axis values are printed
getY(). Method in class weka.gui.beans.BeanInstance: Gets the y coordinate of this bean
getY(). Method in class weka.classifiers.functions.neural.NeuralConnection
getYindex(). Method in class weka.gui.visualize.PlotData2D: Get the currently set y index of the data
getYIndex(). Method in class weka.gui.visualize.VisualizePanel: Get the index of the attribute on the y axis
globalInfo(). Method in class weka.gui.beans.TrainTestSplitMaker: Global info for this bean
globalInfo(). Method in class weka.gui.beans.CrossValidationFoldMaker: Global info for this bean
globalInfo(). Method in class weka.gui.beans.ClassAssigner: Global info for this bean
globalInfo(). Method in class weka.core.converters.CSVLoader: Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.core.converters.C45Loader: Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.clusterers.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.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.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.Remove: 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.Add: 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.AddNoise: 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.RemoveUseless: 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.Obfuscate: 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.PKIDiscretize: Returns a string describing this filter
globalInfo(). Method in class weka.filters.unsupervised.attribute.MakeIndicator
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.RemoveRange: 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.SparseToNonSparse: 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.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.LearningRateResultProducer: 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.CSVResultListener: Returns a string describing this result listener
globalInfo(). Method in class weka.experiment.InstancesResultListener: Returns a string describing this result listener
globalInfo(). Method in class weka.experiment.CrossValidationResultProducer: 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.RandomSplitResultProducer: 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.DatabaseResultProducer: Returns a string describing this result producer
globalInfo(). Method in class weka.attributeSelection.ExhaustiveSearch: Returns a string describing this search method
globalInfo(). Method in class weka.attributeSelection.SVMAttributeEval: Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.attributeSelection.RankSearch: Returns a string describing this search method
globalInfo(). Method in class weka.attributeSelection.RaceSearch: Returns a string describing this search method
globalInfo(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval: Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.attributeSelection.GainRatioAttributeEval: Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.attributeSelection.RandomSearch: 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.GeneticSearch: 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.BestFirst: 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.Ranker: Returns a string describing this search method
globalInfo(). Method in class weka.attributeSelection.ChiSquaredAttributeEval: Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.attributeSelection.OneRAttributeEval: Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.attributeSelection.InfoGainAttributeEval: 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.ClassifierSubsetEval: 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.WrapperSubsetEval: Returns a string describing this attribute evaluator
globalInfo(). Method in class weka.associations.Apriori: Returns a string describing this associator
globalInfo(). Method in class weka.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.ThresholdSelector
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.AttributeSelectedClassifier: Returns a string describing this search method
globalInfo(). Method in class weka.classifiers.meta.CostSensitiveClassifier
globalInfo(). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost
globalInfo(). Method in class weka.classifiers.misc.VFI: Returns a string describing this search method
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.trees.UserClassifier: This will return a string describing the classifier.
globalInfo(). Method in class weka.classifiers.trees.adtree.ADTree
globalInfo(). Method in class weka.classifiers.functions.neural.NeuralNetwork: This will return a string describing the classifier.
goDown(String). Method in class weka.gui.HierarchyPropertyParser: Go to a certain node of the tree down from the current node according to the specified relative path.
goTo(String). Method in class weka.gui.HierarchyPropertyParser: Go to a certain node of the tree according to the specified path Note that the path must be absolute path from the root.
goToChild(int). Method in class weka.gui.HierarchyPropertyParser: Go to one child node from the current position in the tree according to the given position
goToChild(String). Method in class weka.gui.HierarchyPropertyParser: Go to one child node from the current position in the tree according to the given value
If the child node with the given value cannot be found it returns false, true otherwise.
goToParent(). Method in class weka.gui.HierarchyPropertyParser: Go to the parent from the current position in the tree If the current position is the root, it stays there and does not move
goToRoot(). Method in class weka.gui.HierarchyPropertyParser: Go to the root of the tree
gr(double, double). Static method in class weka.core.Utils: Tests if a is smaller than b.
Grading class weka.classifiers.meta.Grading.: Implements Grading.
Grading(). Constructor for class weka.classifiers.meta.Grading
graph(). Method in interface weka.core.Drawable: Returns a string that describes a graph representing the object.
graph(). Method in class weka.clusterers.Cobweb: Generates the graph string of the Cobweb tree
graph(). Method in class weka.classifiers.meta.CostSensitiveClassifier: Returns graph describing the classifier (if possible).
graph(). Method in class weka.classifiers.trees.UserClassifier
graph(). Method in class weka.classifiers.trees.REPTree: Outputs the decision tree as a graph
graph(). Method in class weka.classifiers.trees.m5.M5P: Return a dot style String 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.adtree.ADTree: Returns graph describing the tree.
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
grOrEq(double, double). Static method in class weka.core.Utils: Tests if a is greater or equal to b.
grouping(boolean). Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat
grow(Instances). Method in class weka.classifiers.rules.Rule: Build this rule
GUIChooser class weka.gui.GUIChooser.: The main class for the Weka GUIChooser.
GUIChooser(). Constructor for class weka.gui.GUIChooser: Creates the experiment environment gui with no initial experiment
GUITipText(). Method in class weka.classifiers.functions.neural.NeuralNetwork

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