All Packages  Class Hierarchy

S

sampleSizeTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval

Returns the tip text for this property

save(StringBuffer). Method in class weka.gui.SaveBuffer

Save a buffer

SaveBuffer class weka.gui.SaveBuffer.

This class handles the saving of StringBuffers to files.

SaveBuffer(Logger, Component). Constructor for class weka.gui.SaveBuffer

Constructor

saveInstanceDataTipText(). Method in class weka.clusterers.Cobweb

Returns the tip text for this property

saveInstanceDataTipText(). Method in class weka.classifiers.trees.adtree.ADTree

saveWorkingInstancesToFileQ(). Method in class weka.gui.explorer.PreprocessPanel

Queries the user for a file to save instances as, then saves the instances in a background process.

scalarMultiply(double). Method in class weka.core.AlgVector

Computes the scalar product of this vector with a scalar

scalarMultiply(double). Method in class weka.clusterers.AlgVector

Computes the scalar product of this vector with a scalar

ScatterPlotMatrix class weka.gui.beans.ScatterPlotMatrix.

Bean that encapsulates weka.gui.visualize.MatrixPanel for displaying a scatter plot matrix.

ScatterPlotMatrix(). Constructor for class weka.gui.beans.ScatterPlotMatrix

ScatterPlotMatrixBeanInfo class weka.gui.beans.ScatterPlotMatrixBeanInfo.

Bean info class for the scatter plot matrix bean

ScatterPlotMatrixBeanInfo(). Constructor for class weka.gui.beans.ScatterPlotMatrixBeanInfo

Scoreable interface weka.classifiers.bayes.Scoreable.

Interface for allowing to score a classifier

scoreTypeTipText(). Method in class weka.classifiers.bayes.BayesNet

search(ASEvaluation, Instances). Method in class weka.attributeSelection.ASSearch

Searches the attribute subset/ranking space.

search(ASEvaluation, Instances). Method in class weka.attributeSelection.ExhaustiveSearch

Searches the attribute subset space using an exhaustive search.

search(ASEvaluation, Instances). Method in class weka.attributeSelection.RankSearch

Ranks attributes using the specified attribute evaluator and then searches the ranking using the supplied subset evaluator.

search(ASEvaluation, Instances). Method in class weka.attributeSelection.RaceSearch

Searches the attribute subset space by racing cross validation errors of competing subsets

search(ASEvaluation, Instances). Method in class weka.attributeSelection.RandomSearch

Searches the attribute subset space using a genetic algorithm.

search(ASEvaluation, Instances). Method in class weka.attributeSelection.ForwardSelection

Searches the attribute subset space by forward selection.

search(ASEvaluation, Instances). Method in class weka.attributeSelection.GeneticSearch

Searches the attribute subset space using a genetic algorithm.

search(ASEvaluation, Instances). Method in class weka.attributeSelection.BestFirst

Searches the attribute subset space by best first search

search(ASEvaluation, Instances). Method in class weka.attributeSelection.Ranker

Kind of a dummy search algorithm.

search(Vector, String). Method in class weka.gui.HierarchyPropertyParser

Helper function to search for the given target string in a given vector in which the elements' value may hopefully is equal to the target.

SEARCHPATH_ALL. Static variable in class weka.classifiers.trees.adtree.ADTree

The search modes

SEARCHPATH_HEAVIEST. Static variable in class weka.classifiers.trees.adtree.ADTree

SEARCHPATH_RANDOM. Static variable in class weka.classifiers.trees.adtree.ADTree

SEARCHPATH_ZPURE. Static variable in class weka.classifiers.trees.adtree.ADTree

searchPathTipText(). Method in class weka.classifiers.trees.adtree.ADTree

searchPercentTipText(). Method in class weka.attributeSelection.RandomSearch

Returns the tip text for this property

searchPoints(int, int, boolean). Method in class weka.gui.visualize.Plot2D

Pops up a window displaying attribute information on any instances at a point+-plotting_point_size (in panel coordinates)

searchTerminationTipText(). Method in class weka.attributeSelection.BestFirst

Returns the tip text for this property

searchTipText(). Method in class weka.classifiers.meta.AttributeSelectedClassifier

Returns the tip text for this property

secondInstanceProduced(InstanceEvent). Method in class weka.gui.streams.InstanceJoiner

secondInstanceProduced(InstanceEvent). Method in interface weka.gui.streams.SerialInstanceListener

seedTipText(). Method in class weka.gui.beans.TrainTestSplitMaker

Tip text for this property

seedTipText(). Method in class weka.gui.beans.CrossValidationFoldMaker

Tip text for this property

seedTipText(). Method in class weka.clusterers.EM

Returns the tip text for this property

seedTipText(). Method in class weka.clusterers.FarthestFirst

Returns the tip text for this property

seedTipText(). Method in class weka.clusterers.SimpleKMeans

Returns the tip text for this property

seedTipText(). Method in class weka.clusterers.XMeans

Returns the tip text for this property.

seedTipText(). Method in class weka.filters.supervised.attribute.ClassOrder

Returns the tip text for this property

seedTipText(). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds

Returns the tip text for this property

seedTipText(). Method in class weka.filters.unsupervised.instance.RemoveFolds

Returns the tip text for this property

seedTipText(). Method in class weka.attributeSelection.GeneticSearch

Returns the tip text for this property

seedTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval

Returns the tip text for this property

seedTipText(). Method in class weka.attributeSelection.WrapperSubsetEval

Returns the tip text for this property

seedTipText(). Method in class weka.classifiers.meta.ThresholdSelector

seedTipText(). Method in class weka.classifiers.meta.CostSensitiveClassifier

seedTipText(). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

SelectAttributes(ASEvaluation, String[]). Static method in class weka.attributeSelection.AttributeSelection

Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.

SelectAttributes(ASEvaluation, String[], Instances). Static method in class weka.attributeSelection.AttributeSelection

Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.

SelectAttributes(Instances). Method in class weka.attributeSelection.AttributeSelection

Perform attribute selection on the supplied training instances.

selectAttributesCVSplit(Instances). Method in class weka.attributeSelection.AttributeSelection

Select attributes for a split of the data.

selectedAttributes(). Method in class weka.attributeSelection.AttributeSelection

get the final selected set of attributes.

SelectedTag class weka.core.SelectedTag.

Represents a selected value from a finite set of values, where each value is a Tag (i.e.

SelectedTag(int, Tag[]). Constructor for class weka.core.SelectedTag

Creates a new SelectedTag instance.

SelectedTagEditor class weka.gui.SelectedTagEditor.

A PropertyEditor that uses tags, where the tags are obtained from a weka.core.SelectedTag object.

SelectedTagEditor(). Constructor for class weka.gui.SelectedTagEditor

SELECTION_GREEDY. Static variable in class weka.classifiers.functions.LinearRegression

SELECTION_M5. Static variable in class weka.classifiers.functions.LinearRegression

SELECTION_NONE. Static variable in class weka.classifiers.functions.LinearRegression

selectionThresholdTipText(). Method in class weka.attributeSelection.RaceSearch

Returns the tip text for this property

selectModel(Instances). Method in class weka.classifiers.trees.j48.ModelSelection

Selects a model for the given dataset.

selectModel(Instances). Method in class weka.classifiers.trees.j48.C45ModelSelection

Selects C4.5-type split for the given dataset.

selectModel(Instances). Method in class weka.classifiers.trees.j48.BinC45ModelSelection

Selects C4.5-type split for the given dataset.

selectModel(Instances, Instances). Method in class weka.classifiers.trees.j48.ModelSelection

Selects a model for the given train data using the given test data

selectModel(Instances, Instances). Method in class weka.classifiers.trees.j48.C45ModelSelection

Selects C4.5-type split for the given dataset.

selectModel(Instances, Instances). Method in class weka.classifiers.trees.j48.BinC45ModelSelection

Selects C4.5-type split for the given dataset.

SEND_INSTANCES. Static variable in class weka.gui.treevisualizer.TreeDisplayEvent

Command to remove instances from this node and send them to the VisualizePanel.

separable(DoubleVector, int, int, double). Method in class weka.classifiers.functions.pace.MixtureDistribution

Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1

separable(DoubleVector, int, int, double). Method in class weka.classifiers.functions.pace.ChisqMixture

Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1

separable(DoubleVector, int, int, double). Method in class weka.classifiers.functions.pace.NormalMixture

Return true if a value can be considered for mixture estimatino separately from the data indexed between i0 and i1

seq(int, int). Static method in class weka.classifiers.functions.pace.IntVector

Generates an IntVector that stores all integers inclusively between two integers.

SerialInstanceListener interface weka.gui.streams.SerialInstanceListener.

Defines an interface for objects able to produce two output streams of instances.

SerializedInstancesLoader class weka.core.converters.SerializedInstancesLoader.

Reads a source that contains serialized Instances.

SerializedInstancesLoader(). Constructor for class weka.core.converters.SerializedInstancesLoader

SerializedObject class weka.core.SerializedObject.

Class for storing an object in serialized form in memory.

SerializedObject(Object). Constructor for class weka.core.SerializedObject

Creates a new serialized object (without compression).

SerializedObject(Object, boolean). Constructor for class weka.core.SerializedObject

Creates a new serialized object.

set(double). Method in class weka.classifiers.functions.pace.DoubleVector

Set all elements to a value

set(DoubleVector). Method in class weka.classifiers.functions.pace.DoubleVector

Set the elements using a DoubleVector

set(int). Method in class weka.classifiers.functions.pace.IntVector

Sets the value of an element.

set(int, double). Method in class weka.classifiers.functions.pace.DoubleVector

Set a single element.

set(int, int). Method in class weka.core.DynamicArrayOfPosInt

Stores the value in the specified position in the array.

set(int, int). Method in class weka.classifiers.functions.pace.IntVector

Sets a single element.

set(int, int, double). Method in class weka.classifiers.functions.pace.Matrix

Set a single element.

set(int, int, double). Method in class weka.classifiers.functions.pace.DoubleVector

Set some elements to a value

set(int, int, double[], int). Method in class weka.classifiers.functions.pace.DoubleVector

Set some elements using a 2-D array

set(int, int, DoubleVector, int). Method in class weka.classifiers.functions.pace.DoubleVector

Set some elements using a DoubleVector.

set(int, int, int[], int). Method in class weka.classifiers.functions.pace.IntVector

Sets the values of elements from an int array.

set(int, int, IntVector, int). Method in class weka.classifiers.functions.pace.IntVector

Sets the values of elements from another IntVector.

set(IntVector). Method in class weka.classifiers.functions.pace.IntVector

Sets the values of elements from another IntVector.

setAcuity(double). Method in class weka.clusterers.Cobweb

set the acuity.

setAdditionalMeasures(String[]). Method in interface weka.experiment.ResultProducer

Sets a list of method names for additional measures to look for in SplitEvaluators.

setAdditionalMeasures(String[]). Method in class weka.experiment.ClassifierSplitEvaluator

Set a list of method names for additional measures to look for in Classifiers.

setAdditionalMeasures(String[]). Method in class weka.experiment.LearningRateResultProducer

Set a list of method names for additional measures to look for in SplitEvaluators.

setAdditionalMeasures(String[]). Method in class weka.experiment.CrossValidationResultProducer

Set a list of method names for additional measures to look for in SplitEvaluators.

setAdditionalMeasures(String[]). Method in class weka.experiment.RegressionSplitEvaluator

Set a list of method names for additional measures to look for in Classifiers.

setAdditionalMeasures(String[]). Method in class weka.experiment.RandomSplitResultProducer

Set a list of method names for additional measures to look for in SplitEvaluators.

setAdditionalMeasures(String[]). Method in class weka.experiment.AveragingResultProducer

Set a list of method names for additional measures to look for in SplitEvaluators.

setAdditionalMeasures(String[]). Method in class weka.experiment.DatabaseResultProducer

Set a list of method names for additional measures to look for in SplitEvaluators.

setAdditionalMeasures(String[]). Method in interface weka.experiment.SplitEvaluator

Sets a list of method names for additional measures to look for in SplitEvaluators.

setAdjustWeights(boolean). Method in class weka.filters.supervised.instance.SpreadSubsample

Sets whether the instance weights will be adjusted to maintain total weight per class.

setAdvanceDataSetFirst(boolean). Method in class weka.experiment.Experiment

Set the value of m_AdvanceDataSetFirst.

setAlpha(double). Method in class weka.classifiers.bayes.BayesNet

Method declaration

setAlpha(double). Method in class weka.classifiers.functions.Winnow

Set the value of Alpha.

setAnimated(). Method in class weka.gui.beans.BeanVisual

Set the animated version of the icon

setArffFile(String). Method in class weka.gui.streams.InstanceSavePanel

setArffFile(String). Method in class weka.gui.streams.InstanceLoader

setAsText(String). Method in class weka.gui.GenericArrayEditor

Returns null as we don't support getting/setting values as text.

setAsText(String). Method in class weka.gui.setAsText(String). Method in class weka.gui.CostMatrixEditor

Some objects can be represented as text, but a cost matrix cannot.

setAttIndex(int, boolean). Method in class weka.classifiers.lazy.LBR.Indexes

Changes the boolean value at the specified index in the AttIndexes array

setAttIndexSet(int). Method in class weka.filters.unsupervised.attribute.AddNoise

Sets the Index of the Attribute to be changed.

setAttribute(int). Method in class weka.gui.AttributeVisualizationPanel

Tells the panel which attribute to visualize.

setAttribute(int). Method in class weka.gui.AttributeSummaryPanel

Sets the attribute that statistics will be displayed for.

setAttributeEvaluator(ASEvaluation). Method in class weka.attributeSelection.RankSearch

Set the attribute evaluator to use for generating the ranking.

setAttributeEvaluator(ASEvaluation). Method in class weka.attributeSelection.RaceSearch

Set the attribute evaluator to use for generating the ranking.

setAttributeIndex(int). Method in class weka.filters.unsupervised.attribute.Add

Set the index where the attribute will be inserted

setAttributeIndex(int). Method in class weka.filters.unsupervised.attribute.MergeTwoValues

Sets index of the attribute used.

setAttributeIndex(int). Method in class weka.filters.unsupervised.attribute.SwapValues

Sets index of the attribute used.

setAttributeIndex(int). Method in class weka.filters.unsupervised.attribute.StringToNominal

Sets index of the attribute used.

setAttributeIndex(int). Method in class weka.filters.unsupervised.attribute.MakeIndicator

Sets index of of the attribute used.

setAttributeIndex(int). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Sets attribute to be used for selection

setAttributeIndices(String). Method in class weka.filters.supervised.attribute.Discretize

Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).

setAttributeIndices(String). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries

Set which attributes are to be copied (or kept if invert is true)

setAttributeIndices(String). Method in class weka.filters.unsupervised.attribute.Remove

Set which attributes are to be deleted (or kept if invert is true)

setAttributeIndices(String). Method in class weka.filters.unsupervised.attribute.Copy

Set which attributes are to be copied (or kept if invert is true)

setAttributeIndices(String). Method in class weka.filters.unsupervised.attribute.Discretize

Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).

setAttributeIndices(String). Method in class weka.filters.unsupervised.attribute.FirstOrder

Set which attributes are to be deleted (or kept if invert is true)

setAttributeIndices(String). Method in class weka.filters.unsupervised.attribute.NumericTransform

Set which attributes are to be transformed (or kept if invert is true).

setAttributeIndicesArray(int[]). Method in class weka.filters.supervised.attribute.Discretize

Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).

setAttributeIndicesArray(int[]). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries

Set which attributes are to be copied (or kept if invert is true)

setAttributeIndicesArray(int[]). Method in class weka.filters.unsupervised.attribute.Remove

Set which attributes are to be deleted (or kept if invert is true)

setAttributeIndicesArray(int[]). Method in class weka.filters.unsupervised.attribute.Copy

Set which attributes are to be copied (or kept if invert is true)

setAttributeIndicesArray(int[]). Method in class weka.filters.unsupervised.attribute.Discretize

Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).

setAttributeIndicesArray(int[]). Method in class weka.filters.unsupervised.attribute.FirstOrder

Set which attributes are to be deleted (or kept if invert is true)

setAttributeIndicesArray(int[]). Method in class weka.filters.unsupervised.attribute.NumericTransform

Set which attributes are to be transformed (or kept if invert is true)

setAttributeName(String). Method in class weka.filters.unsupervised.attribute.Add

Set the new attribute's name

setAttributeSelectionMethod(SelectedTag). Method in class weka.classifiers.functions.LinearRegression

Sets the method used to select attributes for use in the linear regression.

setAttributeType(SelectedTag). Method in class weka.filters.unsupervised.attribute.RemoveType

Sets the attribute type to be deleted by the filter.

setAtts(int[], boolean). Method in class weka.classifiers.lazy.LBR.Indexes

Changes the boolean value at the specified index in the InstIndexes array

setAttsToEliminatePerIteration(int). Method in class weka.attributeSelection.SVMAttributeEval

Set the constant rate of attribute elimination per iteration

setAutoBuild(boolean). Method in class weka.classifiers.functions.neural.NeuralNetwork

This will set whether the network is automatically built or if it is left up to the user.

setBagSizePercent(int). Method in class weka.classifiers.meta.Bagging

Sets the size of each bag, as a percentage of the training set size.

setBagSizePercent(int). Method in class weka.classifiers.meta.MetaCost

Sets the size of each bag, as a percentage of the training set size.

setBalanced(boolean). Method in class weka.classifiers.functions.Winnow

Set the value of Balanced.

setBaseClassifiers(Classifier[]). Method in class weka.classifiers.meta.Stacking

Sets the list of possible classifers to choose from.

setBaseClassifiers(Classifier[]). Method in class weka.classifiers.meta.Grading

Sets the list of possible classifers to choose from.

setBaseClassifiers(Classifier[]). Method in class weka.classifiers.meta.StackingC

Sets the list of possible classifers to choose from.

setBaseExperiment(Experiment). Method in class weka.experiment.RemoteExperiment

Set the base experiment.

setBeanContext(BeanContext). Method in class weka.gui.beans.DataVisualizer

Set a bean context for this bean

setBeanContext(BeanContext). Method in class weka.gui.beans.AttributeSummarizer

Set a bean context for this bean

setBeanInstances(Vector, JComponent). Static method in class weka.gui.beans.BeanInstance

Describe setBeanInstances method here.

setBeta(double). Method in class weka.classifiers.functions.Winnow

Set the value of Beta.

setBias(double). Method in class weka.classifiers.misc.VFI

Set the value of the exponential bias towards more confident intervals

setBiasToUniformClass(double). Method in class weka.filters.supervised.instance.Resample

Sets the bias towards a uniform class.

setBinarizeNumericAttributes(boolean). Method in class weka.attributeSelection.ChiSquaredAttributeEval

Binarize numeric attributes.

setBinarizeNumericAttributes(boolean). Method in class weka.attributeSelection.InfoGainAttributeEval

Binarize numeric attributes.

setBinaryAttributesNominal(boolean). Method in class weka.filters.supervised.attribute.NominalToBinary

Sets if binary attributes are to be treates as nominal ones.

setBinaryAttributesNominal(boolean). Method in class weka.filters.unsupervised.attribute.NominalToBinary

Sets if binary attributes are to be treates as nominal ones.

setBinarySplits(boolean). Method in class weka.classifiers.rules.part.PART

Set the value of binarySplits.

setBinarySplits(boolean). Method in class weka.classifiers.trees.j48.J48

Set the value of binarySplits.

setBins(int). Method in class weka.filters.unsupervised.attribute.Discretize

Sets the number of bins to divide each selected numeric attribute into

setBins(int). Method in class weka.filters.unsupervised.attribute.PKIDiscretize

Ignored

setBinValue(double). Method in class weka.clusterers.XMeans

Sets the distance e value between true and false of binary attributes and "same" and "different" of nominal attributes

setBlendFactor(int). Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute

Set the blending factor

setBlendMethod(int). Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute

Set the blending method

setBuildLogisticModels(boolean). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of buildLogisticModels.

setBuildRegressionTree(boolean). Method in class weka.classifiers.trees.m5.M5Base

Set the value of regressionTree.

setC(double). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of C.

setC(double). Method in class weka.classifiers.functions.supportVector.SMOreg

Set the value of C.

setCacheKeyName(String). Method in class weka.experiment.DatabaseResultListener

Set the value of CacheKeyName.

setCacheSize(int). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of the kernel cache.

setCacheSize(int). Method in class weka.classifiers.functions.supportVector.SMOreg

Set the value of the kernel cache.

setCalcOutOfBag(boolean). Method in class weka.classifiers.meta.Bagging

Set whether the out of bag error is calculated.

setCalculateStdDevs(boolean). Method in class weka.experiment.AveragingResultProducer

Set the value of CalculateStdDevs.

setCapacity(int). Method in class weka.core.FastVector

Sets the vector's capacity to the given value.

setCapacity(int). Method in class weka.classifiers.functions.pace.DoubleVector

Sets the capacity of the vector

setCapacity(int). Method in class weka.classifiers.functions.pace.IntVector

Sets the capacity of the vector

setCenter(double). Method in class weka.gui.treevisualizer.Node

Set the value of center.

setCheckErrorRate(boolean). Method in class weka.classifiers.rules.JRip

setChildForBranch(int, PredictionNode). Method in class weka.classifiers.trees.adtree.Splitter

Sets the child for a branch of the split.

setChildForBranch(int, PredictionNode). Method in class weka.classifiers.trees.adtree.TwoWayNominalSplit

Sets the child for a branch of the split.

setChildForBranch(int, PredictionNode). Method in class weka.classifiers.trees.adtree.TwoWayNumericSplit

Sets the child for a branch of the split.

setCindex(int). Method in class weka.gui.visualize.Plot2D

Set the index of the attribute to use for colouring

setCindex(int). Method in class weka.gui.visualize.ClassPanel

Set the index of the attribute to display coloured labels for

setCindex(int). Method in class weka.gui.visualize.AttributePanel

Set the index of the attribute by which to colour the data.

setCindex(int). Method in class weka.gui.visualize.PlotData2D

Set the colouring index of the data

setCindex(int, double, double). Method in class weka.gui.visualize.AttributePanel

Set the index of the attribute by which to colour the data.

setClass(Attribute). Method in class weka.core.Instances

Sets the class attribute.

setClassColumn(String). Method in class weka.gui.beans.ClassAssigner

setClassForIRStatistics(int). Method in class weka.experiment.ClassifierSplitEvaluator

Set the value of ClassForIRStatistics.

setClassifier(Classifier). Method in class weka.gui.beans.IncrementalClassifierEvent

setClassifier(Classifier). Method in class weka.gui.beans.Classifier

Set the classifier for this wrapper

setClassifier(Classifier). Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer

Set a classifier to use

setClassifier(Classifier). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Sets the classifier to classify instances with.

setClassifier(Classifier). Method in class weka.experiment.ClassifierSplitEvaluator

Sets the classifier.

setClassifier(Classifier). Method in class weka.experiment.RegressionSplitEvaluator

Sets the classifier.

setClassifier(Classifier). Method in class weka.attributeSelection.ClassifierSubsetEval

Set the classifier to use for accuracy estimation

setClassifier(Classifier). Method in class weka.attributeSelection.WrapperSubsetEval

Set the classifier to use for accuracy estimation

setClassifier(Classifier). Method in class weka.classifiers.BVDecompose

Set the classifiers being analysed

setClassifier(Classifier). Method in class weka.classifiers.CheckClassifier

Set the classifier for boosting.

setClassifier(Classifier). Method in class weka.classifiers.lazy.LWL

Set the classifier for locally weighted learning.

setClassifier(Classifier). Method in class weka.classifiers.meta.AdditiveRegression

Sets the classifier

setClassifier(Classifier). Method in class weka.classifiers.meta.ClassificationViaRegression

Set the base classifier.

setClassifier(Classifier). Method in class weka.classifiers.meta.DistributionMetaClassifier

Sets the classifier to wrap.

setClassifier(Classifier). Method in class weka.classifiers.meta.MultiBoostAB

Set the classifier for boosting.

setClassifier(Classifier). Method in class weka.classifiers.meta.Bagging

Set the classifier for bagging.

setClassifier(Classifier). Method in class weka.classifiers.meta.MetaCost

Sets the distribution classifier

setClassifier(Classifier). Method in class weka.classifiers.meta.AdaBoostM1

Set the classifier for boosting.

setClassifier(Classifier). Method in class weka.classifiers.meta.AttributeSelectedClassifier

Sets the classifier

setClassifier(Classifier). Method in class weka.classifiers.meta.CVParameterSelection

Set the classifier for boosting.

setClassifier(Classifier). Method in class weka.classifiers.meta.LogitBoost

Set the classifier for boosting.

setClassifier(Classifier). Method in class weka.classifiers.meta.CostSensitiveClassifier

Sets the distribution classifier

setClassifier(Classifier). Method in class weka.classifiers.meta.RegressionByDiscretization

Set the classifier for boosting.

setClassifier(Classifier). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Set the classifier for boosting.

setClassifier(Classifier). Method in class weka.classifiers.meta.FilteredClassifier

Sets the classifier

setClassifier(DistributionClassifier). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set the classifier to use.

setClassifier(DistributionClassifier). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the classifier to use

setClassifierName(String). Method in class weka.experiment.ClassifierSplitEvaluator

Set the Classifier to use, given it's class name.

setClassifierName(String). Method in class weka.experiment.RegressionSplitEvaluator

Set the Classifier to use, given it's class name.

setClassifiers(Classifier[]). Method in class weka.classifiers.meta.Vote

Sets the list of possible classifers to choose from.

setClassifiers(Classifier[]). Method in class weka.classifiers.meta.MultiScheme

Sets the list of possible classifers to choose from.

setClassIndex(int). Method in class weka.core.Instances

Sets the class index of the set.

setClassIndex(int). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Sets the attribute on which misclassifications are based.

setClassIndex(int). Method in class weka.classifiers.BVDecompose

Sets index of attribute to discretize on

setClassMissing(). Method in class weka.core.Instance

Sets the class value of an instance to be "missing".

setClassName(String). Method in class weka.filters.unsupervised.attribute.NumericTransform

Sets the class containing the transformation method.

setClassOrder(int). Method in class weka.filters.supervised.attribute.ClassOrder

Set the wanted class order

setClassType(Class). Method in class weka.gui.GenericObjectEditor

Sets the class of values that can be edited.

setClassValue(double). Method in class weka.core.Instance

Sets the class value of an instance to the given value (internal floating-point format).

setClassValue(String). Method in class weka.core.Instance

Sets the class value of an instance to the given value.

setClearEachDataset(boolean). Method in class weka.gui.streams.InstanceViewer

setClusterer(Clusterer). Method in class weka.clusterers.ClusterEvaluation

set the clusterer

setClusterer(Clusterer). Method in class weka.clusterers.DistributionMetaClusterer

Sets the clusterer to wrap.

setClusterer(Clusterer). Method in class weka.filters.unsupervised.attribute.AddCluster

Sets the clusterer to assign clusters with.

setColor(Color). Method in class weka.gui.treevisualizer.Node

Set the value of color.

setColoringIndex(int). Method in class weka.gui.AttributeVisualizationPanel

Set the coloring index for the plot

setColoringIndex(int). Method in class weka.gui.beans.AttributeSummarizer

Set the coloring index for the attribute summary plots

setColors(FastVector). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set a vector of Color objects for the classes

setColourIndex(int). Method in class weka.gui.visualize.VisualizePanel

Sets the index used for colouring.

setColours(FastVector). Method in class weka.gui.visualize.Plot2D

Set a list of colours to use when colouring points according to class values or cluster numbers

setColours(FastVector). Method in class weka.gui.visualize.ClassPanel

Set a list of colours to use for colouring labels

setColours(FastVector). Method in class weka.gui.visualize.AttributePanel

Sets a list of colours to use for colouring data points

setColumn(int, double[]). Method in class weka.core.Matrix

Sets a column of the matrix to the given column.

setColumnDimension(int). Method in class weka.classifiers.functions.pace.PaceMatrix

Set the column dimenion of the matrix

setComplexityParameter(double). Method in class weka.attributeSelection.SVMAttributeEval

Set the value of C for SMO

setConfidenceFactor(float). Method in class weka.classifiers.rules.part.PART

Set the value of CF.

setConfidenceFactor(float). Method in class weka.classifiers.trees.j48.J48

Set the value of CF.

setConnections(Vector). Static method in class weka.gui.beans.BeanConnection

Describe setConnections method here.

setConnectPoints(boolean[]). Method in class weka.gui.visualize.PlotData2D

Set whether consecutive points should be connected by lines

setConnectPoints(FastVector). Method in class weka.gui.visualize.PlotData2D

Set whether consecutive points should be connected by lines

setCostMatrix(CostMatrix). Method in class weka.classifiers.meta.MetaCost

Sets the misclassification cost matrix.

setCostMatrix(CostMatrix). Method in class weka.classifiers.meta.CostSensitiveClassifier

Sets the misclassification cost matrix.

setCostMatrixSource(SelectedTag). Method in class weka.classifiers.meta.MetaCost

Sets the source location of the cost matrix.

setCostMatrixSource(SelectedTag). Method in class weka.classifiers.meta.CostSensitiveClassifier

Sets the source location of the cost matrix.

setCrossoverProb(double). Method in class weka.attributeSelection.GeneticSearch

set the probability of crossover

setCrossVal(int). Method in class weka.classifiers.rules.DecisionTable

Sets the number of folds for cross validation (1 = leave one out)

setCrossValidate(boolean). Method in class weka.classifiers.lazy.IBk

Sets whether hold-one-out cross-validation will be used to select the best k value

setCurrentInstance(Instance). Method in class weka.gui.beans.IncrementalClassifierEvent

Set the current instance for this event

setCustomColour(Color). Method in class weka.gui.visualize.PlotData2D

Set a custom colour to use for this plot.

setCutoff(double). Method in class weka.clusterers.Cobweb

set the cutoff

setCutOffFactor(double). Method in class weka.clusterers.XMeans

Sets a new cutoff factor.

setCVisible(boolean). Method in class weka.gui.treevisualizer.Node

Sets all the children of this node either to visible or invisible

setData(Instances). Method in class weka.classifiers.rules.RuleStats

Set the data of the stats, overwriting the old one if any

setDatabaseURL(String). Method in class weka.experiment.DatabaseUtils

Set the value of DatabaseURL.

setDataFileName(String). Method in class weka.classifiers.BVDecompose

Sets the maximum number of boost iterations

setDataGenerator(DataGenerator). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set the data generator to use for generating new instances

setDataGenerator(DataGenerator). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the density estimator to use

setDataPoint(double[]). Method in class weka.gui.beans.ChartEvent

Set the data point

setDataset(Instances). Method in class weka.core.Instance

Sets the reference to the dataset.

setDatasetKeyColumns(Range). Method in class weka.experiment.PairedTTester

Set the value of DatasetKeyColumns.

setDatasetKeyFromDialog(). Method in class weka.gui.experiment.ResultsPanel

setDatasets(DefaultListModel). Method in class weka.experiment.Experiment

Set the datasets to use in the experiment

setDatasets(DefaultListModel). Method in class weka.experiment.RemoteExperiment

Set the datasets to use in the experiment

setDebug(boolean). Method in class weka.gui.streams.InstanceSavePanel

setDebug(boolean). Method in class weka.gui.streams.InstanceLoader

setDebug(boolean). Method in class weka.gui.streams.InstanceJoiner

setDebug(boolean). Method in class weka.gui.streams.InstanceViewer

setDebug(boolean). Method in class weka.gui.streams.InstanceCounter

setDebug(boolean). Method in class weka.gui.streams.InstanceTable

setDebug(boolean). Method in class weka.core.Optimization

Set whether in debug mode

setDebug(boolean). Method in class weka.clusterers.EM

Set debug mode - verbose output

setDebug(boolean). Method in class weka.filters.unsupervised.attribute.AddExpression

Set debug mode.

setDebug(boolean). Method in class weka.attributeSelection.RaceSearch

Set whether verbose output should be generated.

setDebug(boolean). Method in class weka.classifiers.BVDecompose

Sets debugging mode

setDebug(boolean). Method in class weka.classifiers.CheckClassifier

Set debugging mode

setDebug(boolean). Method in class weka.classifiers.lazy.IBk

Set the value of Debug.

setDebug(boolean). Method in class weka.classifiers.lazy.LWL

Sets whether debugging output should be produced

setDebug(boolean). Method in class weka.classifiers.meta.AdditiveRegression

Set whether debugging output is produced.

setDebug(boolean). Method in class weka.classifiers.meta.MultiBoostAB

Set debugging mode

setDebug(boolean). Method in class weka.classifiers.meta.Vote

Set debugging mode

setDebug(boolean). Method in class weka.classifiers.meta.MultiScheme

Set debugging mode

setDebug(boolean). Method in class weka.classifiers.meta.AdaBoostM1

Set debugging mode

setDebug(boolean). Method in class weka.classifiers.meta.CVParameterSelection

Sets debugging mode

setDebug(boolean). Method in class weka.classifiers.meta.LogitBoost

Set debugging mode

setDebug(boolean). Method in class weka.classifiers.meta.RegressionByDiscretization

Sets whether debugging output will be printed

setDebug(boolean). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Set debugging mode

setDebug(boolean). Method in class weka.classifiers.rules.JRip

setDebug(boolean). Method in class weka.classifiers.trees.RandomTree

Set the value of Debug.

setDebug(boolean). Method in class weka.classifiers.functions.LeastMedSq

sets whether or not debugging output shouild be printed

setDebug(boolean). Method in class weka.classifiers.functions.LinearRegression

Controls whether debugging output will be printed

setDebug(boolean). Method in class weka.classifiers.functions.Logistic

Sets whether debugging output will be printed.

setDebug(boolean). Method in class weka.classifiers.functions.pace.PaceRegression

Controls whether debugging output will be printed

setDebugLevel(int). Method in class weka.core.KDTree

Sets the debug level.

setDebugLevel(int). Method in class weka.clusterers.XMeans

Sets the debug level.

setDebugVektorsFile(String). Method in class weka.clusterers.XMeans

Sets a file name for a file that has the random vektors stored.

setDecay(boolean). Method in class weka.classifiers.functions.neural.NeuralNetwork

setDefaultValue(). Method in class weka.gui.GenericObjectEditor

Sets the current object to be the default, taken as the first item in the chooser

setDefaultWeight(double). Method in class weka.classifiers.functions.Winnow

Set the value of defaultWeight.

setDelimiters(String). Method in class weka.filters.unsupervised.attribute.StringToWordVector

Set the value of delimiters.

setDelta(double). Method in class weka.associations.Apriori

Set the value of delta.

setDesign(boolean). Method in class weka.gui.beans.AttributeSummarizer

Set whether the appearance of this bean should be design or application

setDesignatedClass(SelectedTag). Method in class weka.classifiers.meta.ThresholdSelector

Sets the method to determine which class value to optimize.

setDirection(SelectedTag). Method in class weka.attributeSelection.BestFirst

Set the search direction

setDisplayConnectors(boolean). Method in class weka.gui.beans.BeanVisual

Turn on/off the connector points

setDisplayRules(boolean). Method in class weka.classifiers.rules.DecisionTable

Sets whether rules are to be printed

setDistanceF(DistanceFunction). Method in class weka.clusterers.XMeans

gets the "binary" distance value

setDistanceFunction(DistanceFunction). Method in class weka.core.KDTree

Sets the distance function.

setDistanceWeighting(SelectedTag). Method in class weka.classifiers.lazy.IBk

Sets the distance weighting method used.

setDistributionClassifier(DistributionClassifier). Method in class weka.classifiers.meta.ThresholdSelector

Set the DistributionClassifier for which threshold is set.

setDistributionClassifier(DistributionClassifier). Method in class weka.classifiers.meta.MultiClassClassifier

Set the base classifier.

setDistributionClassifier(DistributionClassifier). Method in class weka.classifiers.meta.OrdinalClassClassifier

Set the base classifier.

setDistributionSpread(double). Method in class weka.filters.supervised.instance.SpreadSubsample

Sets the value for the distribution spread

setDoXval(boolean). Method in class weka.clusterers.ClusterEvaluation

set whether or not to do cross validation

setElement(int, double). Method in class weka.core.AlgVector

Sets an element of the matrix to the given value.

setElement(int, double). Method in class weka.clusterers.AlgVector

Sets an element of the matrix to the given value.

setElement(int, int, double). Method in class weka.core.Matrix

Sets an element of the matrix to the given value.

setElementAt(Object, int). Method in class weka.core.FastVector

Sets the element at the given index.

setElements(double[]). Method in class weka.core.AlgVector

Sets the elements of the vector to values of the given array.

setElements(double[]). Method in class weka.clusterers.AlgVector

Sets the elements of the vector to values of the given array.

setEliminateColinearAttributes(boolean). Method in class weka.classifiers.functions.LinearRegression

Set the value of EliminateColinearAttributes.

setEnabled(boolean). Method in class weka.gui.GenericObjectEditor

Sets whether the editor is "enabled", meaning that the current values will be painted.

setEntropicAutoBlend(boolean). Method in class weka.classifiers.lazy.kstar.KStar

Set whether entropic blending is to be used.

setEps(double). Method in class weka.classifiers.functions.supportVector.SMOreg

Set the value of eps.

setEpsilon(double). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of epsilon.

setEpsilon(double). Method in class weka.classifiers.functions.supportVector.SMOreg

Set the value of epsilon.

setEpsilonParameter(double). Method in class weka.attributeSelection.SVMAttributeEval

Set the value of P for SMO

setEstimator(SelectedTag). Method in class weka.classifiers.functions.pace.PaceRegression

Sets the estimator.

setEvaluationMode(SelectedTag). Method in class weka.classifiers.meta.ThresholdSelector

Sets the evaluation mode used.

setEvaluator(ASEvaluation). Method in class weka.filters.supervised.attribute.AttributeSelection

set a string holding the name of a attribute/subset evaluator

setEvaluator(ASEvaluation). Method in class weka.attributeSelection.AttributeSelection

set the attribute/subset evaluator

setEvaluator(ASEvaluation). Method in class weka.classifiers.meta.AttributeSelectedClassifier

Sets the attribute evaluator

setExclusive(boolean). Method in class weka.classifiers.rules.ConjunctiveRule

setExecutionStatus(int). Method in class weka.experiment.TaskStatusInfo

Set the execution status of this Task.

setExpectedResultsPerAverage(int). Method in class weka.experiment.AveragingResultProducer

Set the value of ExpectedResultsPerAverage.

setExperiment(Experiment). Method in class weka.gui.experiment.SetupPanel

Sets the experiment to configure.

setExperiment(Experiment). Method in class weka.gui.experiment.DistributeExperimentPanel

Sets the experiment to be configured.

setExperiment(Experiment). Method in class weka.gui.experiment.DatasetListPanel

Tells the panel to act on a new experiment.

setExperiment(Experiment). Method in class weka.gui.experiment.RunPanel

Sets the experiment the panel operates on.

setExperiment(Experiment). Method in class weka.gui.experiment.AlgorithmListPanel

Tells the panel to act on a new experiment.

setExperiment(Experiment). Method in class weka.gui.experiment.RunNumberPanel

Sets the experiment to be configured.

setExperiment(Experiment). Method in class weka.gui.experiment.GeneratorPropertyIteratorPanel

Sets the experiment which will have the custom properties edited.

setExperiment(Experiment). Method in class weka.gui.experiment.SimpleSetupPanel

Sets the experiment to configure.

setExperiment(Experiment). Method in class weka.gui.experiment.ResultsPanel

Tells the panel to use a new experiment.

setExperiment(Experiment). Method in class weka.experiment.RemoteExperimentSubTask

Set the experiment for this sub task

setExperiment(RemoteExperiment). Method in class weka.gui.experiment.HostListPanel

Tells the panel to act on a new experiment.

setExponent(double). Method in class weka.classifiers.functions.VotedPerceptron

Set the value of exponent.

setExponent(double). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of exponent.

setExponent(double). Method in class weka.classifiers.functions.supportVector.SMOreg

Set the value of exponent.

setExpression(String). Method in class weka.filters.unsupervised.attribute.AddExpression

Set the expression to apply

setFalseNegative(double). Method in class weka.classifiers.evaluation.TwoClassStats

Sets the number of positive instances predicted as negative

setFalsePositive(double). Method in class weka.classifiers.evaluation.TwoClassStats

Sets the number of negative instances predicted as positive

setFeatureSpaceNormalization(boolean). Method in class weka.classifiers.functions.supportVector.SMO

Set whether feature space is normalized.

setFeatureSpaceNormalization(boolean). Method in class weka.classifiers.functions.supportVector.SMOreg

Set whether feature space is normalized.

setFile(File). Method in class weka.core.converters.ArffLoader

sets the source File

setFillWithMissing(boolean). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries

Sets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).

setFilter(Filter). Method in class weka.gui.beans.Filter

Set the filter to be wrapped by this bean

setFilter(Filter). Method in class weka.classifiers.meta.FilteredClassifier

Sets the filter

setFilterType(SelectedTag). Method in class weka.attributeSelection.SVMAttributeEval

The filtering mode to pass to SMO

setFilterType(SelectedTag). Method in class weka.classifiers.functions.supportVector.SMO

Sets how the training data will be transformed.

setFilterType(SelectedTag). Method in class weka.classifiers.functions.supportVector.SMOreg

Sets how the training data will be transformed.

setFindNumBins(boolean). Method in class weka.filters.unsupervised.attribute.Discretize

Set the value of FindNumBins.

setFindNumBins(boolean). Method in class weka.filters.unsupervised.attribute.PKIDiscretize

Set the value of FindNumBins.

setFirstValueIndex(int). Method in class weka.filters.unsupervised.attribute.MergeTwoValues

Sets index of the first value used.

setFirstValueIndex(int). Method in class weka.filters.unsupervised.attribute.SwapValues

Sets index of the first value used.

setFold(int). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds

Selects a fold.

setFold(int). Method in class weka.filters.unsupervised.instance.RemoveFolds

Selects a fold.

setFoldColumn(int). Method in class weka.experiment.PairedTTester

Set the value of FoldColumn.

setFolds(int). Method in class weka.gui.beans.CrossValidationFoldMaker

Set the number of folds for the cross validation

setFolds(int). Method in class weka.clusterers.ClusterEvaluation

set the number of folds to use for cross validation

setFolds(int). Method in class weka.attributeSelection.AttributeSelection

set the number of folds for cross validation

setFolds(int). Method in class weka.attributeSelection.WrapperSubsetEval

Set the number of folds to use for accuracy estimation

setFolds(int). Method in class weka.classifiers.rules.Ridor

Set and get members for parameters

setFolds(int). Method in class weka.classifiers.rules.JRip

setFolds(int). Method in class weka.classifiers.rules.ConjunctiveRule

The access functions for parameters

setFoldsType(SelectedTag). Method in class weka.attributeSelection.RaceSearch

Set the xfold type

setFrequencyLimitForParentAttributes(int). Method in class weka.classifiers.bayes.AODE

Set the frequency limit for parent attributes

setFunctionValue(int, double). Method in class weka.classifiers.functions.pace.DiscreteFunction

Sets a particular function value

setGamma(double). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of gamma.

setGamma(double). Method in class weka.classifiers.functions.supportVector.SMOreg

Set the value of gamma.

setGenerateRanking(boolean). Method in class weka.attributeSelection.RaceSearch

Records whether the user has requested a ranked list of attributes.

setGenerateRanking(boolean). Method in interface weka.attributeSelection.RankedOutputSearch

Sets whether or not ranking is to be performed.

setGenerateRanking(boolean). Method in class weka.attributeSelection.ForwardSelection

Records whether the user has requested a ranked list of attributes.

setGenerateRanking(boolean). Method in class weka.attributeSelection.Ranker

This is a dummy set method---Ranker is ONLY capable of producing a ranked list of attributes for attribute evaluators.

setGeneratorSamplesBase(double). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set the base for computing the number of samples to obtain from each generator.

setGeneratorSamplesBase(double). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the base for computing the number of samples to obtain from each generator.

setGlobalBlend(int). Method in class weka.classifiers.lazy.kstar.KStar

Set the global blend parameter

setGridWidth(int). Method in class weka.gui.beans.AttributeSummarizer

Set the width of the grid of plots

setGUI(boolean). Method in class weka.classifiers.functions.neural.NeuralNetwork

This will set whether A GUI is brought up to allow interaction by the user with the neural network during training.

setHandleRightClicks(boolean). Method in class weka.gui.ResultHistoryPanel

Set whether the result history list should handle right clicks or whether the parent object will handle them.

setHiddenLayers(String). Method in class weka.classifiers.functions.neural.NeuralNetwork

This will set what the hidden layers are made up of when auto build is enabled.

setHighlight(String). Method in class weka.gui.treevisualizer.TreeVisualizer

Set the highlight for the node with the given id

setHoldOutFile(File). Method in class weka.attributeSelection.ClassifierSubsetEval

Set the file that contains hold out/test instances

setIgnoredAttributeIndices(String). Method in class weka.filters.unsupervised.attribute.AddCluster

Sets the ranges of attributes to be ignored.

setInitAsNaiveBayes(boolean). Method in class weka.classifiers.bayes.BayesNet

Method declaration

setInputCenterFile(String). Method in class weka.clusterers.XMeans

Sets the name of the file to read the list of centers from.

setInputFormat(Instances). Method in class weka.filters.Filter

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.NullFilter

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.AllFilter

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.supervised.attribute.ClassOrder

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.supervised.attribute.NominalToBinary

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.supervised.attribute.Discretize

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.supervised.instance.Resample

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.supervised.instance.SpreadSubsample

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.TimeSeriesTranslate

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.Remove

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.AddExpression

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.ReplaceMissingValues

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.Add

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.MergeTwoValues

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.Copy

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.AddNoise

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.SwapValues

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.NumericToBinary

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.Discretize

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.FirstOrder

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.RemoveUseless

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.Normalize

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.Standardize

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.RemoveType

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.Obfuscate

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.AddCluster

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.StringToWordVector

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.NominalToBinary

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.TimeSeriesDelta

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.StringToNominal

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.PKIDiscretize

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.MakeIndicator

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.attribute.NumericTransform

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.NonSparseToSparse

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.RemoveFolds

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.RemoveRange

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.Randomize

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.SparseToNonSparse

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.RemovePercentage

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.Resample

Sets the format of the input instances.

setInputFormat(Instances). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Sets the format of the input instances.

setInstance(Instance). Method in class weka.gui.beans.InstanceEvent

Set the instance

setInstanceIndex(int, boolean). Method in class weka.classifiers.lazy.LBR.Indexes

Changes the boolean value at the specified index in the InstIndexes array

setInstanceRange(int). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries

Sets the number of instances forward to translate values between.

setInstances(Instances). Method in class weka.gui.AttributeVisualizationPanel

Sets the instances for use

setInstances(Instances). Method in class weka.gui.AttributeSelectionPanel

Sets the instances who's attribute names will be displayed.

setInstances(Instances). Method in class weka.gui.InstancesSummaryPanel

Tells the panel to use a new set of instances.

setInstances(Instances). Method in class weka.gui.SetInstancesPanel

Updates the set of instances that is currently held by the panel

setInstances(Instances). Method in class weka.gui.AttributeSummaryPanel

Tells the panel to use a new set of instances.

setInstances(Instances). Method in class weka.gui.AttributeListPanel

Sets the instances who's attribute names

Tells the panel to use a new set of instances.

setInstances(Instances). Method in class weka.gui.explorer.AssociationsPanel

Tells the panel to use a new set of instances.

setInstances(Instances). Method in class weka.gui.beans.DataVisualizer

Set instances for this bean.

setInstances(Instances). Method in class weka.gui.beans.ScatterPlotMatrix

Set instances for this bean.

setInstances(Instances). Method in class weka.gui.beans.AttributeSummarizer

Set instances for this bean.

setInstances(Instances). Method in class weka.gui.experiment.ResultsPanel

Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.

setInstances(Instances). Method in class weka.gui.visualize.Plot2D

Sets the master plot from a set of instances

setInstances(Instances). Method in class weka.gui.visualize.ClassPanel

Set the instances.

setInstances(Instances). Method in class weka.gui.visualize.AttributePanel

This sets the instances to be drawn into the attribute panel

setInstances(Instances). Method in class weka.gui.visualize.MatrixPanel

This method changes the Instances object of this class to a new one.

setInstances(Instances). Method in class weka.gui.visualize.VisualizePanel

Tells the panel to use a new set of instances.

setInstances(Instances). Method in class weka.gui.boundaryvisualizer.BoundaryVisualizer

Set the training instances

setInstances(Instances). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the training data

setInstances(Instances). Method in interface weka.experiment.ResultProducer

Sets the dataset that results will be obtained for.

setInstances(Instances). Method in class weka.experiment.LearningRateResultProducer

Sets the dataset that results will be obtained for.

setInstances(Instances). Method in class weka.experiment.PairedTTester

Set the value of Instances.

setInstances(Instances). Method in class weka.experiment.CrossValidationResultProducer

Sets the dataset that results will be obtained for.

setInstances(Instances). Method in class weka.experiment.RandomSplitResultProducer

Sets the dataset that results will be obtained for.

setInstances(Instances). Method in class weka.experiment.AveragingResultProducer

Sets the dataset that results will be obtained for.

setInstances(Instances). Method in class weka.experiment.DatabaseResultProducer

Sets the dataset that results will be obtained for.

setInstancesFromDB(InstanceQuery). Method in class weka.gui.explorer.PreprocessPanel

Loads instances from a database

setInstancesFromDBQ(). Method in class weka.gui.explorer.PreprocessPanel

Queries the user for a URL to a database to load instances from, then loads the instances in a background process.

setInstancesFromFile(File). Method in class weka.gui.explorer.PreprocessPanel

Loads results from a set of instances contained in the supplied file.

setInstancesFromFileQ(). Method in class weka.gui.SetInstancesPanel

Queries the user for a file to load instances from, then loads the instances in a background process.

setInstancesFromFileQ(). Method in class weka.gui.explorer.PreprocessPanel

Queries the user for a file to load instances from, then loads the instances in a background process.

setInstancesFromURL(URL). Method in class weka.gui.explorer.PreprocessPanel

Loads instances from a URL.

setInstancesFromURLQ(). Method in class weka.gui.SetInstancesPanel

Queries the user for a URL to load instances from, then loads the instances in a background process.

setInstancesFromURLQ(). Method in class weka.gui.explorer.PreprocessPanel

Queries the user for a URL to load instances from, then loads the instances in a background process.

setInstancesIndices(String). Method in class weka.filters.unsupervised.instance.RemoveRange

Sets the ranges of instances to be selected.

SetInstancesPanel class weka.gui.SetInstancesPanel.

A panel that displays an instance summary for a set of instances and lets the user open a set of instances from either a file or URL.

SetInstancesPanel(). Constructor for class weka.gui.SetInstancesPanel

Create the panel.

setInsts(int[], boolean). Method in class weka.classifiers.lazy.LBR.Indexes

Changes the boolean value at the specified index in the InstIndexes array

setInvert(boolean). Method in class weka.core.Range

Sets whether the range sense is inverted, i.e.

setInvert(boolean). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Set whether selection is inverted.

setInvertSelection(boolean). Method in class weka.filters.supervised.attribute.Discretize

Sets whether selected columns should be removed or kept.

setInvertSelection(boolean). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds

Sets if selection is to be inverted.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries

Set whether selected columns should be removed or kept.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.attribute.Remove

Set whether selected columns should be removed or kept.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.attribute.Copy

Set whether selected columns should be removed or kept.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.attribute.Discretize

Sets whether selected columns should be removed or kept.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.attribute.RemoveType

Set whether selected columns should be removed or kept.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.attribute.NumericTransform

Set whether selected columns should be transformed or not.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.instance.RemoveFolds

Sets if selection is to be inverted.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.instance.RemoveRange

Sets if selection is to be inverted.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.instance.RemovePercentage

Sets if selection is to be inverted.

setInvertSelection(boolean). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Set whether selected values should be removed or kept.

setIsRandom(boolean). Method in class weka.classifiers.functions.LeastMedSq

Sets whether sample selection is random

setJitter(int). Method in class weka.gui.visualize.Plot2D

Set level of jitter and repaint the plot using the new jitter value

setKDTree(KDTree). Method in class weka.clusterers.XMeans

Sets the KDTree class.

setKDTree(KDTree). Method in class weka.classifiers.lazy.IBk

Sets the KDTree class.

setKernelBandwidth(int). Method in class weka.gui.boundaryvisualizer.KDDataGenerator

Set the kernel bandwidth (number of nearest neighbours to cover)

setKeyFieldName(String). Method in class weka.experiment.AveragingResultProducer

Set the value of KeyFieldName.

setKNN(int). Method in class weka.classifiers.lazy.IBk

Set the number of neighbours the learner is to use.

setKNN(int). Method in class weka.classifiers.lazy.LWL

Sets the number of neighbours used for kernel bandwidth setting.

setKValue(int). Method in class weka.classifiers.trees.RandomTree

Set the value of K.

setLearningRate(double). Method in class weka.classifiers.functions.neural.NeuralNetwork

The learning rate can be set using this command.

setLegendText(Vector). Method in class weka.gui.beans.ChartEvent

Set the legend text vector

setLikelihoodThreshold(double). Method in class weka.classifiers.meta.LogitBoost

Set the value of Precision.

setLoader(Loader). Method in class weka.gui.beans.Loader

Set the loader to use

setLocallyPredictive(boolean). Method in class weka.attributeSelection.CfsSubsetEval

Include locally predictive attributes

setLocationProbs(int, double[]). Method in class weka.gui.boundaryvisualizer.RemoteResult

Store the classifier's distribution for a particular pixel in the visualization

setLog(Logger). Method in class weka.gui.explorer.PreprocessPanel

Sets the Logger to receive informational messages

setLog(Logger). Method in class weka.gui.explorer.ClustererPanel

Sets the Logger to receive informational messages

setLog(Logger). Method in class weka.gui.explorer.AttributeSelectionPanel

Sets the Logger to receive informational messages

setLog(Logger). Method in class weka.gui.explorer.ClassifierPanel

Sets the Logger to receive informational messages

setLog(Logger). Method in class weka.gui.explorer.AssociationsPanel

Sets the Logger to receive informational messages

setLog(Logger). Method in class weka.gui.beans.AbstractTestSetProducer

Set a logger

setLog(Logger). Method in class weka.gui.beans.AbstractEvaluator

Set a logger

setLog(Logger). Method in class weka.gui.beans.Filter

Set a logger

setLog(Logger). Method in class weka.gui.beans.AbstractTrainingSetProducer

Set a logger

setLog(Logger). Method in class weka.gui.beans.Classifier

Set a logger

setLog(Logger). Method in class weka.gui.beans.AbstractTrainAndTestSetProducer

Set a log for this bean

setLog(Logger). Method in class weka.gui.beans.StripChart

Set a logger

setLog(Logger). Method in class weka.gui.beans.ClassAssigner

setLog(Logger). Method in interface weka.gui.beans.BeanCommon

Set a logger

setLog(Logger). Method in class weka.gui.visualize.VisualizePanel

Sets the Logger to receive informational messages

setLowerBoundMinSupport(double). Method in class weka.associations.Apriori

Set the value of lowerBoundMinSupport.

setLowerOrderTerms(boolean). Method in class weka.classifiers.functions.supportVector.SMO

Set whether lower-order terms are to be used.

setLowerOrderTerms(boolean). Method in class weka.classifiers.functions.supportVector.SMOreg

Set whether lower-order terms are to be used.

setLowerSize(int). Method in class weka.experiment.LearningRateResultProducer

Set the value of LowerSize.

setMajorityClass(boolean). Method in class weka.classifiers.rules.Ridor

setMakeBinary(boolean). Method in class weka.filters.supervised.attribute.Discretize

Sets whether binary attributes should be made for discretized ones.

setMakeBinary(boolean). Method in class weka.filters.unsupervised.attribute.Discretize

Sets whether binary attributes should be made for discretized ones.

setMasterPlot(PlotData2D). Method in class weka.gui.visualize.Plot2D

Set the master plot.

setMasterPlot(PlotData2D). Method in class weka.gui.visualize.VisualizePanel

Set the master plot for the visualize panel

setMatchMissingValues(boolean). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Sets whether missing values are counted as a match.

setMatrix(double[], boolean). Method in class weka.classifiers.functions.pace.PaceMatrix

Set the whole matrix from a 1-D array

setMatrix(int[], int[], Matrix). Method in class weka.classifiers.functions.pace.Matrix

Set a submatrix.

setMatrix(int[], int, int, Matrix). Method in class weka.classifiers.functions.pace.Matrix

Set a submatrix.

setMatrix(int, int, int[], Matrix). Method in class weka.classifiers.functions.pace.Matrix

Set a submatrix.

setMatrix(int, int, int, DoubleVector). Method in class weka.classifiers.functions.pace.PaceMatrix

Set the submatrix A[i0:i1][j] with the values stored in a DoubleVector

setMatrix(int, int, int, int, double). Method in class weka.classifiers.functions.pace.PaceMatrix

Set the submatrix A[i0:i1][j0:j1] with a same value

setMatrix(int, int, int, int, Matrix). Method in class weka.classifiers.functions.pace.Matrix

Set a submatrix.

setMax(double). Method in class weka.gui.beans.ChartEvent

Set the max y value

setMaxChunkSize(int). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Set the maximum chunk size

setMaxCount(double). Method in class weka.filters.supervised.instance.SpreadSubsample

Sets the value for the max count

setMaxDepth(int). Method in class weka.classifiers.trees.REPTree

Set the value of MaxDepth.

setMaxGenerations(int). Method in class weka.attributeSelection.GeneticSearch

set the number of generations to evaluate

setMaximumVariancePercentageAllowed(double). Method in class weka.filters.unsupervised.attribute.RemoveUseless

Sets the maximum variance attributes are allowed to have before they are deleted by the filter.

setMaxInstInLeaf(int). Method in class weka.core.KDTree

Sets the maximum number of instances in a leaf.

setMaxIteration(int). Method in class weka.core.Optimization

Set the maximal number of iterations in searching (Default 200)

setMaxIterations(int). Method in class weka.clusterers.EM

Set the maximum number of iterations to perform

setMaxIterations(int). Method in class weka.clusterers.XMeans

Sets the maximum number of iterations to perform.

setMaxIterations(int). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Sets the maximum number of cleansing iterations to perform - < 1 means go until fully cleansed

setMaxIterations(int). Method in class weka.classifiers.meta.MultiBoostAB

Set the maximum number of boost iterations

setMaxIterations(int). Method in class weka.classifiers.meta.AdaBoostM1

Set the maximum number of boost iterations

setMaxIterations(int). Method in class weka.classifiers.meta.LogitBoost

Set the maximum number of boost iterations

setMaxIts(int). Method in class weka.classifiers.functions.Logistic

Set the value of MaxIts.

setMaxK(int). Method in class weka.classifiers.functions.VotedPerceptron

Set the value of maxK.

setMaxKMeans(int). Method in class weka.clusterers.XMeans

Set the maximum number of iterations to perform in KMeans

setMaxKMeansForChildren(int). Method in class weka.clusterers.XMeans

Sets the maximum number of iterations KMeans that is performed on the child centers.

setMaxModels(int). Method in class weka.classifiers.meta.AdditiveRegression

Set the maximum number of models to generate

setMaxNrOfParents(int). Method in class weka.classifiers.bayes.BayesNet

Method declaration

setMaxNumClusters(int). Method in class weka.clusterers.XMeans

Sets the maximum number of clusters to generate.

setMaxPlots(int). Method in class weka.gui.beans.AttributeSummarizer

Set the maximum number of plots to display

setMaxStale(int). Method in class weka.classifiers.rules.DecisionTable

Sets the number of non improving decision tables to consider before abandoning the search.

setMDLTheoryWeight(double). Method in class weka.classifiers.rules.RuleStats

Set the weight of theory in MDL calcualtion

setMeanSquared(boolean). Method in class weka.classifiers.lazy.IBk

Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation.

setMetaClassifier(Classifier). Method in class weka.classifiers.meta.Stacking

Adds meta classifier

setMetaClassifier(Classifier). Method in class weka.classifiers.meta.Grading

Adds meta classifier

setMetaClassifier(Classifier). Method in class weka.classifiers.meta.setMethod(SelectedTag). Method in class weka.classifiers.meta.MultiClassClassifier

Sets the method used.

setMethodName(String). Method in class weka.filters.unsupervised.attribute.NumericTransform

Set the transformation method.

setMetricType(SelectedTag). Method in class weka.associations.Apriori

Set the metric type for ranking rules

setMin(double). Method in class weka.gui.beans.ChartEvent

Set the min y value

setMinBoxRelWidth(double). Method in class weka.core.KDTree

Sets the minimum relative box width.

setMinBucketSize(int). Method in class weka.classifiers.rules.OneR

Set the value of minBucketSize.

setMinChunkSize(int). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Set the minimum chunk size

setMinimizeExpectedCost(boolean). Method in class weka.classifiers.meta.CostSensitiveClassifier

Set the value of MinimizeExpectedCost.

setMinMaxX(double, double). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the minimum and maximum values of the x axis fixed dimension

setMinMaxY(double, double). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the minimum and maximum values of the y axis fixed dimension

setMinMetric(double). Method in class weka.associations.Apriori

Set the value of minConfidence.

setMinNo(double). Method in class weka.classifiers.rules.Ridor

setMinNo(double). Method in class weka.classifiers.rules.JRip

setMinNo(double). Method in class weka.classifiers.rules.ConjunctiveRule

setMinNum(double). Method in class weka.classifiers.trees.REPTree

Set the value of MinNum.

setMinNum(double). Method in class weka.classifiers.trees.RandomTree

Set the value of MinNum.

setMinNumClusters(int). Method in class weka.clusterers.XMeans

Sets the minimum number of clusters to generate.

setMinNumInstances(double). Method in class weka.classifiers.trees.m5.M5Base

Set the minumum number of instances to allow at a leaf node

setMinNumInstances(double). Method in class weka.classifiers.trees.m5.RuleNode

Set the minumum number of instances to allow at a leaf node

setMinNumInstances(double). Method in class weka.classifiers.trees.m5.Rule

Set the minumum number of instances to allow at a leaf node

setMinNumObj(int). Method in class weka.classifiers.rules.part.PART

Set the value of minNumObj.

setMinNumObj(int). Method in class weka.classifiers.trees.j48.J48

Set the value of minNumObj.

setMinStdDev(double). Method in class weka.clusterers.EM

Set the minimum value for standard deviation when calculating normal density.

setMinVarianceProp(double). Method in class weka.classifiers.trees.REPTree

Set the value of MinVarianceProp.

setMissing(Attribute). Method in class weka.core.Instance

Sets a specific value to be "missing".

setMissing(int). Method in class weka.core.Instance

Sets a specific value to be "missing".

setMissingMerge(boolean). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval

distribute the counts for missing values across observed values

setMissingMerge(boolean). Method in class weka.attributeSelection.GainRatioAttributeEval

distribute the counts for missing values across observed values

setMissingMerge(boolean). Method in class weka.attributeSelection.ChiSquaredAttributeEval

distribute the counts for missing values across observed values

setMissingMerge(boolean). Method in class weka.attributeSelection.InfoGainAttributeEval

distribute the counts for missing values across observed values

setMissingMode(int). Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute

Set the missing value mode.

setMissingMode(SelectedTag). Method in class weka.classifiers.lazy.kstar.KStar

Sets the method to use for handling missing values.

setMissingSeperate(boolean). Method in class weka.attributeSelection.CfsSubsetEval

Treat missing as a seperate value

setMixingDistribution(DiscreteFunction). Method in class weka.classifiers.functions.pace.MixtureDistribution

Sets the mixing distribution

setModePanel(SetupModePanel). Method in class weka.gui.experiment.SimpleSetupPanel

Sets the panel used to switch between simple and advanced modes.

setModifyHeader(boolean). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Sets whether the header will be modified when selecting on nominal attributes.

setMomentum(double). Method in class weka.classifiers.functions.neural.NeuralNetwork

The momentum can be set using this command.

setMutationProb(double). Method in class weka.attributeSelection.GeneticSearch

set the probability of mutation

setName(String). Method in class weka.gui.visualize.VisualizePanel

Set a name for this plot

setName(String). Method in class weka.filters.unsupervised.attribute.AddExpression

Set the name for the new attribute.

setNominalIndices(String). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Set which nominal labels are to be included in the selection.

setNominalIndicesArr(int[]). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Set which values of a nominal attribute are to be used for selection.

setNominalLabels(String). Method in class weka.filters.unsupervised.attribute.Add

Set the labels for nominal attribute creation.

setNominalToBinaryFilter(boolean). Method in class weka.classifiers.functions.neural.NeuralNetwork

setNoNormalization(boolean). Method in class weka.classifiers.lazy.IBk

Set whether normalization is turned off.

setNoPruning(boolean). Method in class weka.classifiers.trees.REPTree

Set the value of NoPruning.

setNormalize(boolean). Method in class weka.core.KDTree

Sets the flag for normalizing the widths of a KDTree Node by the width of the dimension in the universe.

setNormalize(boolean). Method in class weka.attributeSelection.PrincipalComponents

Set whether input data will be normalized.

setNormalizeAttributes(boolean). Method in class weka.classifiers.functions.neural.NeuralNetwork

setNormalizeNumericClass(boolean). Method in class weka.classifiers.functions.neural.NeuralNetwork

setNotes(String). Method in class weka.experiment.Experiment

Set the user notes.

setNotes(String). Method in class weka.experiment.RemoteExperiment

Set the user notes.

setNumAllConds(double). Method in class weka.classifiers.rules.RuleStats

Set the number of all conditions that could appear in a rule in this RuleStats object, if the number set is smaller than 0 (typically -1), then it calcualtes based on the data store

setNumAntds(int). Method in class weka.classifiers.rules.ConjunctiveRule

setNumAttemptsOfGeneOption(int). Method in class weka.classifiers.rules.Nnge

Sets the number of attempts for generalisation.

setNumAttributesUsed(). Method in class weka.core.EuclideanDistance

Computes and sets the number of attributes used.

setNumBins(int). Method in class weka.classifiers.meta.RegressionByDiscretization

Sets the number of bins the class attribute will be discretized into.

setNumClusters(int). Method in class weka.clusterers.EM

Set the number of clusters (-1 to select by CV).

setNumClusters(int). Method in class weka.clusterers.FarthestFirst

set the number of clusters to generate

setNumClusters(int). Method in class weka.clusterers.SimpleKMeans

set the number of clusters to generate

setNumeric(boolean). Method in class weka.filters.unsupervised.attribute.MakeIndicator

Sets if the new Attribute is to be numeric.

setNumFeatures(int). Method in class weka.classifiers.trees.RandomForest

Set the number of features to use in random selection.

setNumFoldersMIOption(int). Method in class weka.classifiers.rules.Nnge

Sets the number of folder for mutual information.

setNumFolds(int). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds

Sets the number of folds the dataset is split into.

setNumFolds(int). Method in class weka.filters.unsupervised.instance.RemoveFolds

Sets the number of folds the dataset is split into.

setNumFolds(int). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Sets the number of cross-validation folds to use - < 2 means no cross-validation.

setNumFolds(int). Method in class weka.experiment.CrossValidationResultProducer

Set the value of NumFolds.

setNumFolds(int). Method in class weka.classifiers.meta.Stacking

Sets the number of folds for the cross-validation.

setNumFolds(int). Method in class weka.classifiers.meta.MultiScheme

Sets the number of folds for cross-validation.

setNumFolds(int). Method in class weka.classifiers.meta.CVParameterSelection

Set the number of folds used for cross-validation.

setNumFolds(int). Method in class weka.classifiers.meta.LogitBoost

Set the value of NumFolds.

setNumFolds(int). Method in class weka.classifiers.meta.Grading

Sets the number of folds for the cross-validation.

setNumFolds(int). Method in class weka.classifiers.meta.StackingC

Sets the number of folds for the cross-validation.

setNumFolds(int). Method in class weka.classifiers.rules.part.PART

Set the value of numFolds.

setNumFolds(int). Method in class weka.classifiers.trees.REPTree

Set the value of NumFolds.

setNumFolds(int). Method in class weka.classifiers.trees.j48.J48

Set the value of numFolds.

setNumFolds(int). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of numFolds.

setNumIterations(int). Method in class weka.classifiers.meta.Bagging

Sets the number of bagging iterations

setNumIterations(int). Method in class weka.classifiers.meta.MetaCost

Sets the number of bagging iterations

setNumIterations(int). Method in class weka.classifiers.functions.VotedPerceptron

Set the value of NumIterations.

setNumIterations(int). Method in class weka.classifiers.functions.Winnow

Set the value of numIterations.

setNumNeighbours(int). Method in class weka.attributeSelection.ReliefFAttributeEval

Set the number of nearest neighbours

setNumOfBoostingIterations(int). Method in class weka.classifiers.trees.adtree.ADTree

Sets the number of boosting iterations.

setNumRules(int). Method in class weka.associations.Apriori

Set the value of numRules.

setNumRuns(int). Method in class weka.classifiers.meta.LogitBoost

Set the value of NumRuns.

setNumSamplesPerRegion(int). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set the number of points to uniformly sample from a region (fixed dimensions).

setNumSamplesPerRegion(int). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the number of points to uniformly sample from a region (fixed dimensions).

setNumSubCmtys(int). Method in class weka.classifiers.meta.MultiBoostAB

Set the number of sub committees to use

setNumToSelect(int). Method in class weka.attributeSelection.RaceSearch

Specify the number of attributes to select from the ranked list (if generating a ranking).

setNumToSelect(int). Method in interface weka.attributeSelection.RankedOutputSearch

Specify the number of attributes to select from the ranked list.

setNumToSelect(int). Method in class weka.attributeSelection.ForwardSelection

Specify the number of attributes to select from the ranked list (if generating a ranking).

setNumToSelect(int). Method in class weka.attributeSelection.Ranker

Specify the number of attributes to select from the ranked list.

setNumTrees(int). Method in class weka.classifiers.trees.RandomForest

Set the value of numTrees.

setNumXValFolds(int). Method in class weka.classifiers.meta.ThresholdSelector

Set the number of folds used for cross-validation.

setObject(Object). Method in class weka.gui.beans.LoaderCustomizer

Set the loader to be customized

setObject(Object). Method in class weka.gui.beans.CrossValidationFoldMakerCustomizer

Set the object to be edited

setObject(Object). Method in class weka.gui.beans.ClassifierCustomizer

Set the classifier object to be edited

setObject(Object). Method in class weka.gui.beans.StripChartCustomizer

Set the StripChart object to be customized

setObject(Object). Method in class weka.gui.beans.FilterCustomizer

Set the filter bean to be edited

setObject(Object). Method in class weka.gui.beans.ClassAssignerCustomizer

Set the bean to be edited

setObject(Object). Method in class weka.gui.beans.TrainTestSplitMakerCustomizer

Set the TrainTestSplitMaker to be customized

setOkButtonText(String). Method in class weka.gui.GenericObjectEditor.GOEPanel

Allows customization of the action label on the dialog.

setOn(boolean). Method in class weka.gui.visualize.ClassPanel

Enables the panel

setOnDemandDirectory(File). Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator

Sets the directory that will be searched for cost files when loading on demand.

setOnDemandDirectory(File). Method in class weka.classifiers.meta.MetaCost

Sets the directory that will be searched for cost files when loading on demand.

setOnDemandDirectory(File). Method in class weka.classifiers.meta.CostSensitiveClassifier

Sets the directory that will be searched for cost files when loading on demand.

setOptimizations(int). Method in class weka.classifiers.rules.JRip

setOptimizeBins(boolean). Method in class weka.classifiers.meta.RegressionByDiscretization

Sets whether the discretizer optimizes the number of bins

setOptions(int, int, int). Method in class weka.classifiers.lazy.kstar.KStarNumericAttribute

Set options.

setOptions(int, int, int). Method in class weka.classifiers.lazy.kstar.KStarNominalAttribute

Sets the options.

setOptions(String[]). Method in interface weka.core.OptionHandler

Sets the OptionHandler's options using the given list.

setOptions(String[]). Method in class weka.core.KDTree

Parses a given list of options.

setOptions(String[]). Method in class weka.clusterers.EM

Parses a given list of options.

setOptions(String[]). Method in class weka.clusterers.FarthestFirst

Parses a given list of options.

setOptions(String[]). Method in class weka.clusterers.SimpleKMeans

Parses a given list of options.

setOptions(String[]). Method in class weka.clusterers.XMeans

Parses a given list of options.

setOptions(String[]). Method in class weka.clusterers.Cobweb

Parses a given list of options.

setOptions(String[]). Method in class weka.clusterers.DistributionMetaClusterer

Parses a given list of options.

setOptions(String[]). Method in class weka.filters.supervised.attribute.ClassOrder

Parses a given list of options controlling the behaviour of this object.

setOptions(String[]). Method in class weka.filters.supervised.attribute.NominalToBinary

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.supervised.attribute.Discretize

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.supervised.attribute.setOptions(String[]). Method in class weka.filters.supervised.instance.SpreadSubsample

Parses a list of options for this object.

setOptions(String[]). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.AbstractTimeSeries

Parses a given list of options controlling the behaviour of this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.Remove

Parses a given list of options controlling the behaviour of this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.AddExpression

Parses a list of options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.Add

Parses a list of options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.MergeTwoValues

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.Copy

Parses a given list of options controlling the behaviour of this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.AddNoise

Parses a list of options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.SwapValues

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.Discretize

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.FirstOrder

Parses a given list of options controlling the behaviour of this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.RemoveUseless

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.RemoveType

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.AddCluster

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.StringToWordVector

Parses a given list of options controlling the behaviour of this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.NominalToBinary

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.StringToNominal

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.PKIDiscretize

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.MakeIndicator

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.attribute.NumericTransform

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.instance.RemoveFolds

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.instance.RemoveRange

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.instance.Randomize

Parses a list of options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.instance.RemovePercentage

Parses the options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.instance.Resample

Parses a list of options for this object.

setOptions(String[]). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.ClassifierSplitEvaluator

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.LearningRateResultProducer

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.PairedTTester

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.CSVResultListener

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.CrossValidationResultProducer

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.RegressionSplitEvaluator

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.RandomSplitResultProducer

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.Experiment

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.AveragingResultProducer

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.DatabaseResultProducer

Parses a given list of options.

setOptions(String[]). Method in class weka.experiment.InstanceQuery

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.ExhaustiveSearch

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.SVMAttributeEval

Parses a given list of options Valid options are:

-X
Specify constant rate at which attributes are eliminated per invocation of the support vector machine.

setOptions(String[]). Method in class weka.attributeSelection.RankSearch

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.RaceSearch

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.GainRatioAttributeEval

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.RandomSearch

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.ForwardSelection

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.GeneticSearch

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.CfsSubsetEval

Parses and sets a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.BestFirst

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.ReliefFAttributeEval

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.Ranker

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.ChiSquaredAttributeEval

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.InfoGainAttributeEval

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.ClassifierSubsetEval

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.PrincipalComponents

Parses a given list of options.

setOptions(String[]). Method in class weka.attributeSelection.WrapperSubsetEval

Parses a given list of options.

setOptions(String[]). Method in class weka.associations.Apriori

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.BVDecompose

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.CheckClassifier

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.lazy.IBk

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.lazy.LWL

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.lazy.kstar.KStar

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.AdditiveRegression

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.ClassificationViaRegression

Sets a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.DistributionMetaClassifier

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.Stacking

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.MultiBoostAB

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.Vote

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.Bagging

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.ThresholdSelector

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.MetaCost

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.MultiScheme

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.MultiClassClassifier

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.AdaBoostM1

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.OrdinalClassClassifier

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.AttributeSelectedClassifier

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.CVParameterSelection

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.LogitBoost

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.Grading

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.StackingC

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.CostSensitiveClassifier

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.RegressionByDiscretization

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.meta.FilteredClassifier

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.misc.VFI

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.bayes.BayesNet

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.bayes.AODE

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.bayes.BayesNetK2

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.bayes.NaiveBayes

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.rules.DecisionTable

Parses the options for this object.

setOptions(String[]). Method in class weka.classifiers.rules.Ridor

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.rules.JRip

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.rules.ConjunctiveRule

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.rules.Nnge

Sets the OptionHandler's options using the given list.

setOptions(String[]). Method in class weka.classifiers.rules.OneR

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.rules.part.PART

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.trees.REPTree

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.trees.RandomForest

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.trees.RandomTree

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.trees.m5.M5Base

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.trees.m5.M5P

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.trees.j48.J48

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.trees.adtree.ADTree

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.functions.LeastMedSq

Sets the OptionHandler's options using the given list.

setOptions(String[]). Method in class weka.classifiers.functions.VotedPerceptron

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.functions.LinearRegression

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.functions.Winnow

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.functions.Logistic

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.functions.pace.PaceRegression

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.functions.neural.NeuralNetwork

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.functions.supportVector.SMO

Parses a given list of options.

setOptions(String[]). Method in class weka.classifiers.functions.supportVector.SMOreg

Parses a given list of options.

setOutputCenterFile(String). Method in class weka.clusterers.XMeans

Sets the name of the file to write the list of centers to.

setOutputFile(File). Method in class weka.experiment.CSVResultListener

Set the value of OutputFile.

setOutputFile(File). Method in class weka.experiment.CrossValidationResultProducer

Set the value of OutputFile.

setOutputFile(File). Method in class weka.experiment.RandomSplitResultProducer

Set the value of OutputFile.

setOutputWordCounts(boolean). Method in class weka.filters.unsupervised.attribute.RemoteBoundaryVisualizerSubTask

Set the height of the visualization

setPanelWidth(int). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the width of the visualization

setParent(Edge). Method in class weka.gui.treevisualizer.Node

Set the value of parent.

setPercent(). Method in class weka.gui.visualize.MatrixPanel

Calculates the percentage to resample

setPercent(int). Method in class weka.filters.unsupervised.attribute.AddNoise

Sets the size of noise data, as a percentage of the original set.

setPercentage(int). Method in class weka.filters.unsupervised.instance.RemovePercentage

Sets the percentage of intances to select.

setPercentCompleted(int). Method in class weka.gui.boundaryvisualizer.RemoteResult

Set the progress for this row so far

setPercentThreshold(int). Method in class weka.attributeSelection.SVMAttributeEval

Set the threshold below which percentage elimination reverts to constant elimination.

setPercentToEliminatePerIteration(int). Method in class weka.attributeSelection.SVMAttributeEval

Set the percentage of attributes to eliminate per iteration

setPixHeight(double). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the height of a pixel

setPixWidth(double). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the width of a pixel

setPlotCompanion(Plot2DCompanion). Method in class weka.gui.visualize.Plot2D

Set a companion class.

setPlotList(FastVector). Method in class weka.gui.visualize.LegendPanel

Set the list of plots to generate legend entries for

setPlotName(String). Method in class weka.gui.visualize.PlotData2D

Set the name of this plot

setPlotTrainingData(boolean). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set whether to superimpose the training data plot

setPlus(int, double). Method in class weka.classifiers.functions.pace.DoubleVector

Adds a value to an element

setPlus(int, int, double). Method in class weka.classifiers.functions.pace.PaceMatrix

Add a value to an element and reset the element

setPointValue(int, double). Method in class weka.classifiers.functions.pace.DiscreteFunction

Sets a particular point value

setPopulationSize(int). Method in class weka.attributeSelection.GeneticSearch

set the population size

setPriors(Instances). Method in class weka.classifiers.Evaluation

Sets the class prior probabilities

setProduceLatex(boolean). Method in class weka.experiment.PairedTTester

Set whether latex is output

setProperty(String, String). Method in class weka.core.ProtectedProperties

Overrides a method to prevent the properties from being modified.

setPropertyArray(Object). Method in class weka.experiment.Experiment

Sets the array of values to set the custom property to.

setPropertyArray(Object). Method in class weka.experiment.RemoteExperiment

Sets the array of values to set the custom property to.

setPropertyPath(PropertyNode[]). Method in class weka.experiment.Experiment

Sets the path of properties taken to get to the custom property to iterate over.

setPropertyPath(PropertyNode[]). Method in class weka.experiment.RemoteExperiment

Sets the path of properties taken to get to the custom property to iterate over.

setPrune(boolean). Method in class weka.core.KDTree

Sets the flag for pruning of the blacklisting algorithm.

setPruningType(SelectedTag). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Set the pruning type

setQuery(String). Method in class weka.experiment.InstanceQuery

Set the query to execute against the database

setRaceType(SelectedTag). Method in class weka.attributeSelection.RaceSearch

Set the race type

setRandomized(boolean). Method in class weka.classifiers.rules.ConjunctiveRule

setRandomizeData(boolean). Method in class weka.experiment.RandomSplitResultProducer

Set to true if dataset is to be randomized

setRandomOrder(boolean). Method in class weka.classifiers.bayes.BayesNetK2

Set random order flag

setRandomSeed(int). Method in class weka.filters.supervised.instance.Resample

Sets the random number seed.

setRandomSeed(int). Method in class weka.filters.supervised.instance.SpreadSubsample

Sets the random number seed.

setRandomSeed(int). Method in class weka.filters.unsupervised.attribute.AddNoise

Sets the random number seed.

setRandomSeed(int). Method in class weka.filters.unsupervised.instance.Randomize

Set the random number generator seed value.

setRandomSeed(int). Method in class weka.filters.unsupervised.instance.Resample

Sets the random number seed.

setRandomSeed(int). Method in class weka.classifiers.trees.adtree.ADTree

Sets random seed for a random walk.

setRandomSeed(int). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of randomSeed.

setRandomSeed(long). Method in class weka.classifiers.functions.LeastMedSq

Set the seed for the random number generator

setRandomSeed(long). Method in class weka.classifiers.functions.neural.NeuralNetwork

This seeds the random number generator, that is used when a random number is needed for the network.

setRandomWidthFactor(double). Method in class weka.classifiers.meta.MultiClassClassifier

Sets the multiplier when generating random codes.

setRangeCorrection(SelectedTag). Method in class weka.classifiers.meta.ThresholdSelector

Sets the confidence range correction mode used.

setRanges(String). Method in class weka.core.Range

Sets the ranges from a string representation.

setRanking(boolean). Method in class weka.attributeSelection.AttributeSelection

produce a ranking (if possible with the set search and evaluator)

setRawOutput(boolean). Method in class weka.experiment.CrossValidationResultProducer

Set to true if raw split evaluator output is to be saved

setRawOutput(boolean). Method in class weka.experiment.RandomSplitResultProducer

Set to true if raw split evaluator output is to be saved

setReducedErrorPruning(boolean). Method in class weka.classifiers.rules.part.PART

Set the value of reducedErrorPruning.

setReducedErrorPruning(boolean). Method in class weka.classifiers.trees.j48.J48

Set the value of reducedErrorPruning.

setRefer(String). Method in class weka.gui.treevisualizer.Node

Set the value of refer.

setRefreshFreq(int). Method in class weka.gui.beans.StripChart

Set how often (in x axis points) to refresh the display

setRegressionTree(boolean). Method in class weka.classifiers.trees.m5.RuleNode

Set the value of regressionTree.

setRegressionTree(boolean). Method in class weka.classifiers.trees.m5.Rule

Set the value of regressionTree.

setRelationName(String). Method in class weka.core.Instances

Sets the relation's name.

setRemoteHosts(Vector). Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed

Set a list of host names of machines to distribute processing to

setRemoveAllMissingCols(boolean). Method in class weka.associations.Apriori

Remove columns containing all missing values.

setReportFrequency(int). Method in class weka.attributeSelection.GeneticSearch

set how often reports are generated

setReset(boolean). Method in class weka.gui.beans.ChartEvent

Set the reset flag

setReset(boolean). Method in class weka.classifiers.functions.neural.NeuralNetwork

This sets the network up to be able to reset itself with the current settings and the learning rate at half of what it is currently.

setResultKeyFromDialog(). Method in class weka.gui.experiment.ResultsPanel

setResultListener(ResultListener). Method in interface weka.experiment.ResultProducer

Sets the object to send results of each run to.

setResultListener(ResultListener). Method in class weka.experiment.LearningRateResultProducer

Sets the object to send results of each run to.

setResultListener(ResultListener). Method in class weka.experiment.CrossValidationResultProducer

Sets the object to send results of each run to.

setResultListener(ResultListener). Method in class weka.experiment.RandomSplitResultProducer

Sets the object to send results of each run to.

setResultListener(ResultListener). Method in class weka.experiment.Experiment

Sets the result listener where results will be sent.

setResultListener(ResultListener). Method in class weka.experiment.AveragingResultProducer

Sets the object to send results of each run to.

setResultListener(ResultListener). Method in class weka.experiment.RemoteExperiment

Sets the result listener where results will be sent.

setResultListener(ResultListener). Method in class weka.experiment.DatabaseResultProducer

Sets the object to send results of each run to.

setResultProducer(ResultProducer). Method in class weka.experiment.LearningRateResultProducer

Set the ResultProducer.

setResultProducer(ResultProducer). Method in class weka.experiment.Experiment

Set the result producer used for the current experiment.

setResultProducer(ResultProducer). Method in class weka.experiment.AveragingResultProducer

Set the ResultProducer.

setResultProducer(ResultProducer). Method in class weka.experiment.RemoteExperiment

Set the result producer used for the current experiment.

setResultProducer(ResultProducer). Method in class weka.experiment.DatabaseResultProducer

Set the ResultProducer.

setResultsetKeyColumns(Range). Method in class weka.experiment.PairedTTester

Set the value of ResultsetKeyColumns.

setRidge(double). Method in class weka.classifiers.functions.LinearRegression

Set the value of Ridge.

setRidge(double). Method in class weka.classifiers.functions.Logistic

Sets the ridge in the log-likelihood.

setRoot(boolean). Method in class weka.gui.treevisualizer.Node

Set the value of root.

setRow(int, double[]). Method in class weka.core.Matrix

Sets a row of the matrix to the given row.

setRowDimension(int). Method in class weka.classifiers.functions.pace.PaceMatrix

Set the row dimenion of the matrix

setRowNumber(int). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the row number for this sub task

setRsource(String). Method in class weka.gui.treevisualizer.Edge

Set the value of rsource.

setRtarget(String). Method in class weka.gui.treevisualizer.Edge

Set the value of rtarget.

setRuleset(FastVector). Method in class weka.classifiers.rules.RuleStats

Set the ruleset of the stats, overwriting the old one if any

setRunColumn(int). Method in class weka.experiment.PairedTTester

Set the value of RunColumn.

setRunLower(int). Method in class weka.experiment.Experiment

Set the lower run number for the experiment.

setRunLower(int). Method in class weka.experiment.RemoteExperiment

Set the lower run number for the experiment.

setRunUpper(int). Method in class weka.experiment.Experiment

Set the upper run number for the experiment.

setRunUpper(int). Method in class weka.experiment.RemoteExperiment

Set the upper run number for the experiment.

setSampleSize(int). Method in class weka.attributeSelection.ReliefFAttributeEval

Set the number of instances to sample for attribute estimation

setSampleSize(int). Method in class weka.classifiers.functions.LeastMedSq

sets number of samples

setSampleSizePercent(double). Method in class weka.filters.supervised.instance.Resample

Sets the size of the subsample, as a percentage of the original set.

setSampleSizePercent(double). Method in class weka.filters.unsupervised.instance.Resample

Sets the size of the subsample, as a percentage of the original set.

setSaveInstanceData(boolean). Method in class weka.clusterers.Cobweb

Set the value of saveInstances.

setSaveInstanceData(boolean). Method in class weka.classifiers.trees.j48.J48

Set whether instance data is to be saved.

setSaveInstanceData(boolean). Method in class weka.classifiers.trees.adtree.ADTree

Sets whether the tree is to save instance data.

setSaveInstances(boolean). Method in class weka.classifiers.trees.m5.M5P

Set whether to save instance data at each node in the tree for visualization purposes

setScoreType(SelectedTag). Method in class weka.classifiers.bayes.BayesNet

Method declaration

setSearch(ASSearch). Method in class weka.filters.supervised.attribute.AttributeSelection

Set as string holding the name of a search class

setSearch(ASSearch). Method in class weka.attributeSelection.AttributeSelection

set the search method

setSearch(ASSearch). Method in class weka.classifiers.meta.AttributeSelectedClassifier

Sets the search method

setSearchPath(SelectedTag). Method in class weka.classifiers.trees.adtree.ADTree

Sets the method of searching the tree for a new insertion.

setSearchPercent(double). Method in class weka.attributeSelection.RandomSearch

set the percentage of the search space to consider

setSearchTermination(int). Method in class weka.attributeSelection.BestFirst

Set the numnber of non-improving nodes to consider before terminating search.

setSecondValueIndex(int). Method in class weka.filters.unsupervised.attribute.MergeTwoValues

Sets index of the second value used.

setSecondValueIndex(int). Method in class weka.filters.unsupervised.attribute.SwapValues

Sets index of the second value used.

setSeed(int). Method in class weka.gui.beans.TrainTestSplitMaker

Set the random seed

setSeed(int). Method in class weka.gui.beans.CrossValidationFoldMaker

Set the seed

setSeed(int). Method in class weka.gui.boundaryvisualizer.KDDataGenerator

Initializes a new random number generator using the supplied seed.

setSeed(int). Method in interface weka.gui.boundaryvisualizer.DataGenerator

Set a seed for random number generation (if needed).

setSeed(int). Method in interface weka.core.Randomizable

Set the seed for random number generation.

setSeed(int). Method in class weka.clusterers.EM

Set the random number seed

setSeed(int). Method in class weka.clusterers.ClusterEvaluation

set the seed to use for cross validation

setSeed(int). Method in class weka.clusterers.FarthestFirst

Set the random number seed

setSeed(int). Method in class weka.clusterers.SimpleKMeans

Set the random number seed

setSeed(int). Method in class weka.clusterers.XMeans

Sets the random number seed.

setSeed(int). Method in class weka.attributeSelection.GeneticSearch

set the seed for random number generation

setSeed(int). Method in class weka.attributeSelection.ReliefFAttributeEval

Set the random number seed for randomly sampling instances.

setSeed(int). Method in class weka.attributeSelection.AttributeSelection

set the seed for use in cross validation

setSeed(int). Method in class weka.attributeSelection.WrapperSubsetEval

Set the seed to use for cross validation

setSeed(int). Method in class weka.classifiers.BVDecompose

Sets the random number seed

setSeed(int). Method in class weka.classifiers.meta.Stacking

Sets the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.MultiBoostAB

Set seed for resampling.

setSeed(int). Method in class weka.classifiers.meta.Vote

Sets the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.Bagging

Set the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.ThresholdSelector

Sets the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.MetaCost

Set seed for resampling.

setSeed(int). Method in class weka.classifiers.meta.MultiScheme

Sets the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.MultiClassClassifier

Sets the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.AdaBoostM1

Set seed for resampling.

setSeed(int). Method in class weka.classifiers.meta.CVParameterSelection

Sets the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.LogitBoost

Set seed for resampling.

setSeed(int). Method in class weka.classifiers.meta.Grading

Sets the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.StackingC

Sets the seed for random number generation.

setSeed(int). Method in class weka.classifiers.meta.CostSensitiveClassifier

Set seed for resampling.

setSeed(int). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Set seed for resampling.

setSeed(int). Method in class weka.classifiers.rules.Ridor

setSeed(int). Method in class weka.classifiers.trees.REPTree

Set the value of Seed.

setSeed(int). Method in class weka.classifiers.trees.RandomForest

Set the seed for random number generation.

setSeed(int). Method in class weka.classifiers.trees.RandomTree

Set the seed for random number generation.

setSeed(int). Method in class weka.classifiers.evaluation.EvaluationUtils

Sets the seed for randomization during cross-validation

setSeed(int). Method in class weka.classifiers.functions.VotedPerceptron

Set the value of Seed.

setSeed(int). Method in class weka.classifiers.functions.Winnow

Set the value of Seed.

setSeed(long). Method in class weka.filters.supervised.attribute.ClassOrder

Set randomization seed

setSeed(long). Method in class weka.filters.supervised.instance.StratifiedRemoveFolds

Sets the random number seed for shuffling the dataset.

setSeed(long). Method in class weka.filters.unsupervised.instance.RemoveFolds

Sets the random number seed for shuffling the dataset.

setSeed(long). Method in class weka.classifiers.rules.JRip

setSeed(long). Method in class weka.classifiers.rules.ConjunctiveRule

setSelectedRange(String). Method in class weka.filters.unsupervised.attribute.StringToWordVector

Set the value of m_SelectedRange.

setSelectionThreshold(double). Method in class weka.attributeSelection.RaceSearch

Set the threshold by which the AttributeSelection module can discard attributes.

setSeparatingThreshold(double). Method in class weka.classifiers.functions.pace.ChisqMixture

Sets the separating threshold value

setSeparatingThreshold(double). Method in class weka.classifiers.functions.pace.NormalMixture

Sets the separating threshold value

setSeperator(String). Method in class weka.gui.HierarchyPropertyParser

Set the seperator between levels.

setSequentialAttIndex(boolean). Method in class weka.classifiers.lazy.LBR.Indexes

A Sequential Attribute index is all those Attributes that are set to the specified value placed in a sequential array.

setSequentialDataset(boolean). Method in class weka.classifiers.lazy.LBR.Indexes

Sets both the Instance and Attribute indexes to a specified value

setSequentialInstanceIndex(boolean). Method in class weka.classifiers.lazy.LBR.Indexes

A Sequential Instance index is all those Instances that are set to the specified value placed in a sequential array.

setShape(int). Method in class weka.gui.treevisualizer.Node

Set the value of shape.

setShapes(FastVector). Method in class weka.gui.visualize.VisualizePanel

This will set the shapes for the instances.

setShapeSize(FastVector). Method in class weka.gui.visualize.PlotData2D

Set the shape sizes for the plot data

setShapeSize(int[]). Method in class weka.gui.visualize.PlotData2D

Set the shape sizes for the plot data

setShapeType(FastVector). Method in class weka.gui.visualize.PlotData2D

Set the shape type for the plot data

setShapeType(int[]). Method in class weka.gui.visualize.PlotData2D

Set the shape type for the plot data

setShowStdDevs(boolean). Method in class weka.experiment.PairedTTester

Set whether standard deviations are displayed or not.

setShrinkage(double). Method in class weka.classifiers.meta.AdditiveRegression

Set the shrinkage parameter

setShrinkage(double). Method in class weka.classifiers.meta.LogitBoost

Set the value of Shrinkage.

setShuffle(int). Method in class weka.classifiers.rules.Ridor

setSigma(int). Method in class weka.attributeSelection.ReliefFAttributeEval

Sets the sigma value.

setSignificanceLevel(double). Method in class weka.experiment.PairedTTester

Set the value of SignificanceLevel.

setSignificanceLevel(double). Method in class weka.attributeSelection.RaceSearch

Sets the significance level to use

setSignificanceLevel(double). Method in class weka.associations.Apriori

Set the value of significanceLevel.

setSIndex(int). Method in class weka.gui.visualize.VisualizePanel

Set the shape for creating splits.

setSingle(String). Method in class weka.gui.ResultHistoryPanel

Sets the single-click display to view the named result.

setSize(int). Method in class weka.classifiers.functions.pace.DoubleVector

Sets the size of the vector

setSize(int). Method in class weka.classifiers.functions.pace.IntVector

Sets the size of the vector.

setSmoothing(boolean). Method in class weka.classifiers.trees.m5.RuleNode

Get if smoothing is being used

setSmoothing(boolean). Method in class weka.classifiers.trees.m5.Rule

Smooth predictions

setSource(File). Method in class weka.core.converters.AbstractLoader

Default implementation throws an IOException.

setSource(File). Method in class weka.core.converters.CSVLoader

Resets the Loader object and sets the source of the data set to be the supplied File object.

setSource(File). Method in class weka.core.converters.C45Loader

Resets the Loader object and sets the source of the data set to be the supplied File object.

setSource(File). Method in interface weka.core.converters.Loader

Resets the Loader object and sets the source of the data set to be the supplied File object.

setSource(File). Method in class weka.core.converters.ArffLoader

Resets the Loader object and sets the source of the data set to be the supplied File object.

setSource(File). Method in class weka.core.converters.SerializedInstancesLoader

Resets the Loader object and sets the source of the data set to be the supplied File object.

setSource(InputStream). Method in class weka.core.converters.AbstractLoader

Default implementation throws an IOException.

setSource(InputStream). Method in interface weka.core.converters.Loader

Resets the Loader object and sets the source of the data set to be the supplied InputStream.

setSource(InputStream). Method in class weka.core.converters.ArffLoader

Resets the Loader object and sets the source of the data set to be the supplied InputStream.

setSource(InputStream). Method in class weka.core.converters.SerializedInstancesLoader

Resets the Loader object and sets the source of the data set to be the supplied InputStream.

setSource(Node). Method in class weka.gui.treevisualizer.Edge

Set the value of source.

setSparseData(boolean). Method in class weka.experiment.InstanceQuery

Sets whether data should be encoded as sparse instances

setSplitByDataSet(boolean). Method in class weka.experiment.RemoteExperiment

Set whether sub experiments are to be created on the basis of data set.

setSplitEvaluator(SplitEvaluator). Method in class weka.experiment.CrossValidationResultProducer

Set the SplitEvaluator.

setSplitEvaluator(SplitEvaluator). Method in class weka.experiment.RandomSplitResultProducer

Set the SplitEvaluator.

setSplitPoint(double). Method in class weka.filters.unsupervised.instance.RemoveWithValues

Split point to be used for selection on numeric attribute.

setSplitPoint(Instances). Method in class weka.classifiers.trees.j48.C45Split

Sets split point to greatest value in given data smaller or equal to old split point.

setSplitPoint(Instances). Method in class weka.classifiers.trees.j48.BinC45Split

Sets split point to greatest value in given data smaller or equal to old split point.

setStartSet(String). Method in class weka.attributeSelection.ExhaustiveSearch

Sets a starting set of attributes for the search.

setStartSet(String). Method in class weka.attributeSelection.RandomSearch

Sets a starting set of attributes for the search.

setStartSet(String). Method in class weka.attributeSelection.ForwardSelection

Sets a starting set of attributes for the search.

setStartSet(String). Method in class weka.attributeSelection.GeneticSearch

Sets a starting set of attributes for the search.

setStartSet(String). Method in class weka.attributeSelection.BestFirst

Sets a starting set of attributes for the search.

setStartSet(String). Method in class weka.attributeSelection.Ranker

Sets a starting set of attributes for the search.

setStartSet(String). Method in interface weka.attributeSelection.StartSetHandler

Sets a starting set of attributes for the search.

setStatic(). Method in class weka.gui.beans.BeanVisual

Set the static version of the icon

setStatus(int). Method in class weka.gui.beans.IncrementalClassifierEvent

Set the status

setStatus(int). Method in class weka.gui.beans.InstanceEvent

Set the status

setStatusMessage(String). Method in class weka.experiment.TaskStatusInfo

Set the status message.

setStepSize(int). Method in class weka.experiment.LearningRateResultProducer

Set the value of StepSize.

setSubtreeRaising(boolean). Method in class weka.classifiers.trees.j48.J48

Set the value of subtreeRaising.

setTarget(Node). Method in class weka.gui.treevisualizer.Edge

Set the value of target.

setTarget(Object). Method in class weka.gui.PropertySheetPanel

Sets a new target object for customisation.

setTaskResult(Object). Method in class weka.experiment.TaskStatusInfo

Set the returnable result for this task..

setTestBaseFromDialog(). Method in class weka.gui.experiment.ResultsPanel

setText(String). Method in class weka.gui.beans.BeanVisual

Set the label for the visual.

setThreshold(double). Method in class weka.filters.unsupervised.instance.RemoveMisclassified

Sets the threshold for the max error when predicting a numeric class.

setThreshold(double). Method in class weka.attributeSelection.RaceSearch

Sets the threshold for comparisons

setThreshold(double). Method in interface weka.attributeSelection.RankedOutputSearch

Sets a threshold by which attributes can be discarded from the ranking.

setThreshold(double). Method in class weka.attributeSelection.ForwardSelection

Set the threshold by which the AttributeSelection module can discard attributes.

setThreshold(double). Method in class weka.attributeSelection.AttributeSelection

set the threshold by which to select features from a ranked list

setThreshold(double). Method in class weka.attributeSelection.Ranker

Set the threshold by which the AttributeSelection module can discard attributes.

setThreshold(double). Method in class weka.attributeSelection.WrapperSubsetEval

Set the value of the threshold for repeating cross validation

setThreshold(double). Method in class weka.classifiers.functions.Winnow

Set the value of Threshold.

setThreshold(double). Method in class weka.classifiers.functions.pace.PaceRegression

Set threshold for the olsc estimator

setTimes(int, double). Method in class weka.classifiers.functions.pace.DoubleVector

Multiplies a value to an element

setTimes(int, int, double). Method in class weka.classifiers.functions.pace.PaceMatrix

Multiply a value with an element and reset the element

setToleranceParameter(double). Method in class weka.attributeSelection.SVMAttributeEval

Set the value of T for SMO

setToleranceParameter(double). Method in class weka.classifiers.functions.supportVector.SMO

Set the value of tolerance parameter.

setToleranceParameter(double). Method in class weka.classifiers.functions.supportVector.SMOreg

Set the value of tolerance parameter.

setTop(double). Method in class weka.gui.treevisualizer.Node

Set the value of top.

setTrainingData(Instances). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set the training data to use

setTrainingTime(int). Method in class weka.classifiers.functions.neural.NeuralNetwork

Set the number of training epochs to perform.

setTrainIterations(int). Method in class weka.classifiers.BVDecompose

Sets the maximum number of boost iterations

setTrainPercent(double). Method in class weka.experiment.RandomSplitResultProducer

Set the value of TrainPercent.

setTrainPercent(int). Method in class weka.gui.beans.TrainTestSplitMaker

Set the percentage of data to be in the training portion of the split

setTrainPoolSize(int). Method in class weka.classifiers.BVDecompose

Set the number of instances in the training pool.

setTransformBackToOriginal(boolean). Method in class weka.attributeSelection.PrincipalComponents

Sets whether the data should be transformed back to the original space

setTrimingThreshold(double). Method in class weka.classifiers.functions.pace.ChisqMixture

Sets the triming thresholding value.

setTrimingThreshold(double). Method in class weka.classifiers.functions.pace.NormalMixture

Sets the triming thresholding value.

setTrueNegative(double). Method in class weka.classifiers.evaluation.TwoClassStats

Sets the number of negative instances predicted as negative

setTruePositive(double). Method in class weka.classifiers.evaluation.TwoClassStats

Sets the number of positive instances predicted as positive

setType(int). Method in class weka.classifiers.functions.neural.NeuralConnection

setUnpruned(boolean). Method in class weka.classifiers.rules.part.PART

Set the value of unpruned.

setUnpruned(boolean). Method in class weka.classifiers.trees.m5.M5Base

Use unpruned tree/rules

setUnpruned(boolean). Method in class weka.classifiers.trees.m5.Rule

Use unpruned tree/rules

setUnpruned(boolean). Method in class weka.classifiers.trees.j48.J48

Set the value of unpruned.

setupAttribLists(). Method in class weka.gui.visualize.MatrixPanel

Sets up the UI's attributes lists

setUpComboBoxes(Instances). Method in class weka.gui.visualize.VisualizePanel

setUpdateIncrementalClassifier(boolean). Method in class weka.gui.beans.Classifier

SetupModePanel class wekF="weka.gui.experiment.SetupPanel.html">SetupPanel

Creates the setup panel with no initial experiment.

SetupPanel(Experiment). Constructor for class weka.gui.experiment.SetupPanel

Creates the setup panel with the supplied initial experiment.

setUpper(int). Method in class weka.core.Range

Sets the value of "last".

setUpperBoundMinSupport(double). Method in class weka.associations.Apriori

Set the value of upperBoundMinSupport.

setUpperSize(int). Method in class weka.experiment.LearningRateResultProducer

Set the value of UpperSize.

setUseADTree(boolean). Method in class weka.classifiers.bayes.BayesNet

Method declaration

setUseBetterEncoding(boolean). Method in class weka.filters.supervised.attribute.Discretize

Sets whether better encoding is to be used for MDL.

setUseEqualFrequency(boolean). Method in class weka.filters.unsupervised.attribute.Discretize

Set the value of UseEqualFrequency.

setUseEqualFrequency(boolean). Method in class weka.filters.unsupervised.attribute.PKIDiscretize

Set the value of UseEqualFrequency.

setUseIBk(boolean). Method in class weka.classifiers.rules.DecisionTable

Sets whether IBk should be used instead of the majority class

setUseKernelEstimator(boolean). Method in class weka.classifiers.bayes.NaiveBayes

Sets if kernel estimator is to be used.

setUseKononenko(boolean). Method in class weka.filters.supervised.attribute.Discretize

Sets whether Kononenko's MDL criterion is to be used.

setUseLaplace(boolean). Method in class weka.classifiers.trees.j48.J48

Set the value of useLaplace.

setUseMissing(boolean). Method in class weka.filters.unsupervised.attribute.AddNoise

Sets the flag if missing values are treated as extra values.

setUsePropertyIterator(boolean). Method in class weka.experiment.Experiment

Sets whether the custom property iterator should be used.

setUsePropertyIterator(boolean). Method in class weka.experiment.RemoteExperiment

Sets whether the custom property iterator should be used.

setUsePruning(boolean). Method in class weka.classifiers.rules.JRip

setUseRBF(boolean). Method in class weka.classifiers.functions.supportVector.SMO

Set if the RBF kernel is to be used.

setUseRBF(boolean). Method in class weka.classifiers.functions.supportVector.SMOreg

Set if the RBF kernel is to be used.

setUseResampling(boolean). Method in class weka.classifiers.meta.MultiBoostAB

Set resampling mode

setUseResampling(boolean). Method in class weka.classifiers.meta.AdaBoostM1

Set resampling mode

setUseResampling(boolean). Method in class weka.classifiers.meta.LogitBoost

Set resampling mode

setUseResampling(boolean). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Set resampling mode

setUseSupervisedDiscretization(boolean). Method in class weka.classifiers.bayes.NaiveBayes

Set whether supervised discretization is to be used.

setUseSupervisedDiscretization(boolean). Method in class weka.classifiers.bayes.NaiveBayesUpdateable

Set whether supervised discretization is to be used.

setUseTraining(boolean). Method in class weka.attributeSelection.ClassifierSubsetEval

Set if training data is to be used instead of hold out/test data

setUseTree(boolean). Method in class weka.classifiers.trees.m5.Rule

Use an m5 tree rather than generate rules

setUseUnsmoothed(boolean). Method in class weka.classifiers.trees.m5.M5Base

Use unsmoothed predictions

setValid(boolean). Method in class weka.core.KDTree

Sets KDTree to be valid for dataset in m_Instances.

setValidationChunkSize(int). Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Set the validation chunk size

setValidationSetSize(int). Method in class weka.classifiers.functions.neural.NeuralNetwork

This will set the size of the validation set.

setValidationThreshold(int). Method in class weka.classifiers.functions.neural.NeuralNetwork

This sets the threshold to use for when validation testing is being done.

setValue(Attribute, double). Method in class weka.core.Instance

Sets a specific value in the instance to the given value (internal floating-point format).

setValue(Attribute, String). Method in class weka.core.Instance

Sets a value of an nominal or string attribute to the given value.

setValue(double). Method in class weka.classifiers.trees.adtree.PredictionNode

Sets the prediction value of the node.

setValue(int, double). Method in class weka.core.Instance

Sets a specific value in the instance to the given value (internal floating-point format).

setValue(int, double). Method in class weka.core.SparseInstance

Sets a specific value in the instance to the given value (internal floating-point format).

setValue(int, double). Method in class weka.core.BinarySparseInstance

Sets a specific value in the instance to the given value (internal floating-point format).

setValue(int, String). Method in class weka.core.Instance

Sets a value of a nominal or string attribute to the given value.

setValue(Object). Method in class weka.gui.GenericArrayEditor

Sets the current object array.

setValue(Object). Method in class weka.gui.GenericObjectEditor

Sets the current Object.

setValue(Object). Method in class weka.gui.CostMatrixEditor

Sets the value of the CostMatrix to be edited.

setValueIndex(int). Method in class weka.filters.unsupervised.attribute.MakeIndicator

Sets index of the indicator value.

setValueIndices(String). Method in class weka.filters.unsupervised.attribute.MakeIndicator

Sets indices of the indicator values.

setValueIndicesArray(int[]). Method in class weka.filters.unsupervised.attribute.MakeIndicator

Set which attributes are to be deleted (or kept if invert is true)

setValueSparse(int, double). Method in class weka.core.Instance

Sets a specific value in the instance to the given value (internal floating-point format).

setValueSparse(int, double). Method in class weka.core.SparseInstance

Sets a specific value in the instance to the given value (internal floating-point format).

setValueSparse(int, double). Method in class weka.core.BinarySparseInstance

Sets a specific value in the instance to the given value (internal floating-point format).

setVarianceCovered(double). Method in class weka.attributeSelection.PrincipalComponents

Sets the amount of variance to account for when retaining principal components

setVerbose(boolean). Method in class weka.attributeSelection.ExhaustiveSearch

set whether or not to output new best subsets as the search proceeds

setVerbose(boolean). Method in class weka.attributeSelection.RandomSearch

set whether or not to output new best subsets as the search proceeds

setVisual(BeanVisual). Method in class weka.gui.beans.AbstractDataSource

Set the visual for this data source

setVisual(BeanVisual). Method in interface weka.gui.beans.Visible

Set a new visual representation

setVisual(BeanVisual). Method in class weka.gui.beans.DataVisualizer

Set the visual appearance of this bean

setVisual(BeanVisual). Method in class weka.gui.beans.AbstractTestSetProducer

Set the visual for this bean

setVisual(BeanVisual). Method in class weka.gui.beans.AbstractEvaluator

Set the visual

setVisual(BeanVisual). Method in class weka.gui.beans.Filter

Set the visual appearance of this bean

setVisual(BeanVisual). Method in class weka.gui.beans.AbstractTrainingSetProducer

Set the visual for this bean

setVisual(BeanVisual). Method in class weka.gui.beans.Classifier

Sets the visual appearance of this wrapper bean

setVisual(BeanVisual). Method in class weka.gui.beans.AbstractTrainAndTestSetProducer

Set the visual for this bean

setVisual(BeanVisual). Method in class weka.gui.beans.TextViewer

Describe setVisual method here.

setVisual(BeanVisual). Method in class weka.gui.beans.StripChart

Set the visual appearance of this bean

setVisual(BeanVisual). Method in class weka.gui.beans.GraphViewer

Set the visual appearance of this bean

setVisual(BeanVisual). Method in class weka.gui.beans.ClassAssigner

setWeight(double). Method in class weka.core.Instance

Sets the weight of an instance.

setWeightByConfidence(boolean). Method in class weka.classifiers.misc.VFI

Set weighting by confidence

setWeightByDistance(boolean). Method in class weka.attributeSelection.ReliefFAttributeEval

Set the nearest neighbour weighting method

setWeightingDimensions(boolean[]). Method in class weka.gui.boundaryvisualizer.KDDataGenerator

Set which dimensions to use when computing a weight for the next instance to generate

setWeightingDimensions(boolean[]). Method in interface weka.gui.boundaryvisualizer.DataGenerator

Set the dimensions to be used in computing a weight for each instance generated

setWeightingKernel(int). Method in class weka.classifiers.lazy.LWL

Sets the kernel weighting method to use.

setWeightingValues(double[]). Method in class weka.gui.boundaryvisualizer.KDDataGenerator

Set the values for the weighting dimensions to be used when computing the weight for the next instance to be generated

setWeightingValues(double[]). Method in interface weka.gui.boundaryvisualizer.DataGenerator

Set the values of the dimensions (chosen via setWeightingDimensions) to be used when computing instance weights

setWeightThreshold(int). Method in class weka.classifiers.meta.MultiBoostAB

Set weight threshold

setWeightThreshold(int). Method in class weka.classifiers.meta.AdaBoostM1

Set weight threshold

setWeightThreshold(int). Method in class weka.classifiers.meta.LogitBoost

Set weight thresholding

setWholeDataErr(boolean). Method in class weka.classifiers.rules.Ridor

setWindowSize(int). Method in class weka.classifiers.lazy.IBk

Sets the maximum number of instances allowed in the training pool.

setWordsToKeep(int). Method in class weka.filters.unsupervised.attribute.StringToWordVector

Sets the number of words (per class if there is a class attribute assigned) to attempt to keep.

setWrappedAlgorithm(Object). Method in interface weka.gui.beans.WekaWrapper

Set the algorithm.

setWrappedAlgorithm(Object). Method in class weka.gui.beans.Loader

Set the loader

setWrappedAlgorithm(Object). Method in class weka.gui.beans.Filter

Set the filter to be wrapped by this bean

setWrappedAlgorithm(Object). Method in class weka.gui.beans.Classifier

Sets the algorithm (classifier) for this bean

setX(double). Method in class weka.classifiers.functions.neural.NeuralConnection

setX(int). Method in class weka.gui.beans.BeanInstance

Sets the x coordinate of this bean

setX(int). Method in class weka.gui.visualize.AttributePanel

shows which bar is the current x attribute.

setXAttribute(int). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set the x attribute index

setXAttribute(int). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the x axis fixed dimension

setXindex(int). Method in class weka.gui.visualize.Plot2D

Set the index of the attribute to go on the x axis

setXindex(int). Method in class weka.gui.visualize.PlotData2D

Set the x index of the data.

setXIndex(int). Method in class weka.gui.visualize.VisualizePanel

Set the index of the attribute for the x axis

setXLabelFreq(int). Method in class weka.gui.beans.StripChart

Set the frequency for printing x label values

setXval(boolean). Method in class weka.attributeSelection.AttributeSelection

do a cross validation

setXY_VisualizeIndexes(int, int). Method in class weka.gui.explorer.ClustererPanel

Set the default attributes to use on the x and y axis of a new visualization object.

setXY_VisualizeIndexes(int, int). Method in class weka.gui.explorer.ClassifierPanel

Set the default attributes to use on the x and y axis of a new visualization object.

setY(double). Method in class weka.classifiers.functions.neural.NeuralConnection

setY(int). Method in class weka.gui.beans.BeanInstance

Sets the y coordinate of this bean

setY(int). Method in class weka.gui.visualize.AttributePanel

shows which bar is the current y attribute.

setYAttribute(int). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Set the y attribute index

setYAttribute(int). Method in class weka.gui.boundaryvisualizer.RemoteBoundaryVisualizerSubTask

Set the y axis fixed dimension

setYindex(int). Method in class weka.gui.visualize.Plot2D

Set the index of the attribute to go on the y axis

setYindex(int). Method in class weka.gui.visualize.PlotData2D

Set the y index of the data

setYIndex(int). Method in class weka.gui.visualize.VisualizePanel

Set the index of the attribute for the y axis

SFEntropyGain(). Method in class weka.classifiers.Evaluation

Returns the total SF, which is the null model entropy minus the scheme entropy.

SFMeanEntropyGain(). Method in class weka.classifiers.Evaluation

Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.

SFMeanPriorEntropy(). Method in class weka.classifiers.Evaluation

Returns the entropy per instance for the null model

SFMeanSchemeEntropy(). Method in class weka.classifiers.Evaluation

Returns the entropy per instance for the scheme

SFPriorEntropy(). Method in class weka.classifiers.Evaluation

Returns the total entropy for the null model

SFSchemeEntropy(). Method in class weka.classifiers.Evaluation

Returns the total entropy for the scheme

shift(int, int). Method in class weka.classifiers.functions.pace.IntVector

Shifts an element to another position.

shift(int, int, Instance). Method in class weka.classifiers.trees.j48.Distribution

Shifts given instance from one bag to another one.

shiftRange(int, int, Instances, int, int). Method in class weka.classifiers.trees.j48.Distribution

Shifts all instances in given range from one bag to another one.

shiftToEnd(int). Method in class weka.classifiers.functions.pace.IntVector

Shifts an element to the end of the vector.

show(Component, int, int). Method in class weka.gui.GenericObjectEditor.JTreePopupMenu

Displays the menu, making sure it will fit on the screen.

showChart(). Method in class weka.gui.beans.StripChart

Popup the chart panel

showDialog(). Method in class weka.gui.PropertySelectorDialog

Pops up the modal dialog and waits for cancel or a selection.

showDialog(). Method in class weka.gui.ListSelectorDialog

Pops up the modal dialog and waits for cancel or a selection.

showPropertyDialog(). Method in class weka.gui.PropertyPanel

Displays the property edit dialog for the panel.

showResults(). Method in class weka.gui.beans.TextViewer

Popup a component to display the selected text

showResults(). Method in class weka.gui.beans.GraphViewer

Popup a result list from which the user can select a graph to view

showTree(). Method in class weka.gui.HierarchyPropertyParser

Show the whole tree in text format

shrinkageTipText(). Method in class weka.classifiers.meta.AdditiveRegression

Returns the tip text for this property

sigLevel. Variable in class weka.experiment.PairedStats

The significance level for comparisons

sigmaTipText(). Method in class weka.attributeSelection.ReliefFAttributeEval

Returns the tip text for this property

SigmoidUnit class weka.classifiers.functions.neural.SigmoidUnit.

This can be used by the neuralnode to perform all it's computations (as a sigmoid unit).

SigmoidUnit(). Constructor for class weka.classifiers.functions.neural.significanceLevelTipText(). Method in class weka.attributeSelection.RaceSearch

Returns the tip text for this property

significanceLevelTipText(). Method in class weka.associations.Apriori

Returns the tip text for this property

SimpleCLI class weka.gui.SimpleCLI.

Creates a very simple command line for invoking the main method of classes.

SimpleCLI(). Constructor for class weka.gui.SimpleCLI

Constructor

SimpleKMeans class weka.clusterers.SimpleKMeans.

Simple k means clustering class.

SimpleKMeans(). Constructor for class weka.clusterers.SimpleKMeans

SimpleLinearRegression class weka.classifiers.functions.SimpleLinearRegression.

Class for learning a simple linear regression model.

SimpleLinearRegression(). Constructor for class weka.classifiers.functions.SimpleLinearRegression

SimpleSetupPanel class weka.gui.experiment.SimpleSetupPanel.

This panel controls the configuration of an experiment.

SimpleSetupPanel(). Constructor for class weka.gui.experiment.SimpleSetupPanel

Creates the setup panel with no initial experiment.

SimpleSetupPanel(Experiment). Constructor for class weka.gui.experiment.SimpleSetupPanel

Creates the setup panel with the supplied initial experiment.

singletons(Instances). Static method in class weka.associations.ItemSet

Converts the header info of the given set of instances into a set of item sets (singletons).

size(). Method in class weka.core.Queue

Gets queue's size.

size(). Method in class weka.core.FastVector

Returns the vector's current size.

size(). Method in class weka.classifiers.CostMatrix

Gets the size of the matrix.

size(). Method in class weka.classifiers.lazy.kstar.KStarCache.CacheTable

Returns the number of keys in this hashtable.

size(). Method in class weka.classifiers.rules.Rule

The size of the rule.

size(). Method in class weka.classifiers.evaluation.ConfusionMatrix

Gets the number of classes.

size(). Method in class weka.classifiers.functions.pace.DoubleVector

Gets the size of the vector.

size(). Method in class weka.classifiers.functions.pace.IntVector

Gets the size of the vector.

size(). Method in class weka.classifiers.functions.pace.DiscreteFunction

Returns the size of the point set.

sm(double, double). Static method in class weka.core.Utils

Tests if a is smaller than b.

SMALL. Static variable in class weka.core.Utils

The small deviation allowed in double comparisons

SMO class weka.classifiers.functions.supportVector.SMO.

Implements John C.

SMO(). Constructor for class weka.classifiers.functions.supportVector.SMO

SMOreg class weka.classifiers.functions.supportVector.SMOreg.

Implements Alex J.Smola and Bernhard Scholkopf sequential minimal optimization algorithm for training a support vector regression using polynomial or RBF kernels.

SMOreg(). Constructor for class weka.classifiers.functions.supportVector.SMOreg

smOrEq(double, double). Static method in class weka.core.Utils

Tests if a is smaller or equal to b.

SMOset class weka.classifiers.functions.supportVector.SMOset.

Stores a set of integer of a given size.

SMOset(int). Constructor for class weka.classifiers.functions.supportVector.SMOset

Creates a new set of the given size.

solve(double[]). Method in class weka.core.Matrix

Solve A*X = B using backward substitution.

solveTriangle(Matrix, double[], boolean, boolean[]). Static method in class weka.core.Optimization

Solve the linear equation of TX=B where T is a triangle matrix It can be solved using back/forward substitution, with O(N^2) complexity

sort(). Method in class weka.classifiers.functions.pace.DoubleVector

Sorts the array in place

sort(). Method in class weka.classifiers.functions.pace.IntVector

Sorts the elements in place

sort(). Method in class weka.classifiers.functions.pace.DiscreteFunction

Sorts the point values of the discrete function.

sort(Attribute). Method in class weka.core.Instances

Sorts the instances based on an attribute.

sort(double[]). Static method in class weka.core.Utils

Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.

sort(int). Method in class weka.core.Instances

Sorts the instances based on an attribute.

sort(int[]). Static method in class weka.core.Utils

Sorts a given array of integers in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.

sortWithIndex(). Method in class weka.classifiers.functions.pace.DoubleVector

Sorts the array in place with index returned

sortWithIndex(int, int, IntVector). Method in class weka.classifiers.functions.pace.DoubleVector

Sorts the array in place with index changed

Sourcable interface weka.classifiers.Sourcable.

Interface for classifiers that can be converted to Java source.

sourceClass(int, Instances). Method in class weka.classifiers.trees.j48.ClassifierSplitModel

sourceExpression(int, Instances). Method in class weka.classifiers.trees.j48.ClassifierSplitModel

sourceExpression(int, Instances). Method in class weka.classifiers.trees.j48.NoSplit

Returns a string containing java source code equivalent to the test made at this node.

sourceExpression(int, Instances). Method in class weka.classifiers.trees.j48.C45Split

Returns a string containing java source code equivalent to the test made at this node.

sourceExpression(int, Instances). Method in class weka.classifiers.trees.j48.BinC45Split

Returns a string containing java source code equivalent to the test made at this node.

SOUTH_CONNECTOR. Static variable in class weka.gui.beans.BeanVisual

sparseDataTipText(). Method in class weka.experiment.InstanceQuery

Returns the tip text for this property

sparseIndices(). Method in class weka.classifiers.functions.supportVector.SMO

Returns the indices in sparse format.

SparseInstance class weka.core.SparseInstance.

Class for storing an instance as a sparse vector.

SparseInstance(double, double[]). Constructor for class weka.core.SparseInstance

Constructor that generates a sparse instance from the given parameters.

SparseInstance(double, double[], int[], int). Constructor for class weka.core.SparseInstance

Constructor that inititalizes instance variable with given values.

SparseInstance(Instance). Constructor for class weka.core.SparseInstance

Constructor that generates a sparse instance from the given instance.

SparseInstance(int). Constructor for class weka.core.SparseInstance

Constructor of an instance that sets weight to one, all values to be missing, and the reference to the dataset to null.

SparseInstance(SparseInstance). Constructor for class weka.core.SparseInstance

Constructor that copies the info from the given instance.

SparseToNonSparse class weka.filters.unsupervised.instance.SparseToNonSparse.

A filter that converts all incoming sparse instances into non-sparse format.

SparseToNonSparse(). Constructor for class weka.filters.unsupervised.instance.SparseToNonSparse

sparseWeights(). Method in class weka.classifiers.functions.supportVector.SMO

Returns the weights in sparse format.

SpecialFunctions class weka.core.SpecialFunctions.

Class implementing some mathematical functions.

SpecialFunctions(). Constructor for class weka.core.SpecialFunctions

sphere. Variable in class weka.classifiers.lazy.kstar.KStarWrapper

used/reused to hold the sphere size

split(). Method in class weka.classifiers.trees.m5.RuleNode

Finds an attribute and split point for this node

split(Instances). Method in class weka.classifiers.trees.j48.ClassifierSplitModel

Splits the given set of instances into subsets.

splitAtt(). Method in class weka.classifiers.trees.m5.RuleNode

Get the index of the splitting attribute for this node

splitAttr(). Method in interface weka.classifiers.trees.m5.SplitEvaluate

Returns the attribute used in this split

splitAttr(). Method in class weka.classifiers.trees.m5.YongSplitInfo

Returns the attribute used in this split

splitAttr(). Method in class weka.classifiers.trees.m5.CorrelationSplitInfo

Returns the attribute used in this split

SplitCriterion class weka.classifiers.trees.j48.SplitCriterion.

Abstract class for computing splitting criteria with respect to distributions of class values.

SplitCriterion(). Constructor for class weka.classifiers.trees.j48.SplitCriterion

splitCritValue(Distribution). Method in class weka.classifiers.trees.j48.SplitCriterion

Computes result of splitting criterion for given distribution.

splitCritValue(Distribution). Method in class weka.classifiers.trees.j48.InfoGainSplitCrit

This method is a straightforward implementation of the information gain criterion for the given distribution.

splitCritValue(Distribution). Method in class weka.classifiers.trees.j48.GainRatioSplitCrit

This method is a straightforward implementation of the gain ratio criterion for the given distribution.

splitCritValue(Distribution). Method in class weka.classifiers.trees.j48.EntropySplitCrit

Computes entropy for given distribution.

splitCritValue(Distribution, Distribution). Method in class weka.classifiers.trees.j48.SplitCriterion

Computes result of splitting criterion for given training and test distributions.

splitCritValue(Distribution, Distribution). Method in class weka.classifiers.trees.j48.EntropySplitCrit

Computes entropy of test distribution with respect to training distribution.

splitCritValue(Distribution, Distribution, Distribution). Method in class weka.classifiers.trees.j48.SplitCriterion

Computes result of splitting criterion for given training and test distributions and given default distribution.

splitCritValue(Distribution, Distribution, int). Method in class weka.classifiers.trees.j48.SplitCriterion

Computes result of splitting criterion for given training and test distributions and given number of classes.

splitCritValue(Distribution, double). Method in class weka.classifiers.trees.j48.InfoGainSplitCrit

This method computes the information gain in the same way C4.5 does.

splitCritValue(Distribution, double, double). Method in class weka.classifiers.trees.j48.InfoGainSplitCrit

This method computes the information gain in the same way C4.5 does.

splitCritValue(Distribution, double, double). Method in class weka.classifiers.trees.j48.GainRatioSplitCrit

This method computes the gain ratio in the same way C4.5 does.

splitEnt(Distribution). Method in class weka.classifiers.trees.j48.EntropyBasedSplitCrit

Computes entropy after splitting without considering the class values.

SplitEvaluate interface weka.classifiers.trees.m5.SplitEvaluate.

Interface for objects that determine a split point on an attribute

SplitEvaluator interface weka.experiment.SplitEvaluator.

Interface to objects able to generate a fixed set of results for a particular split of a dataset.

splitEvaluatorTipText(). Method in class weka.experiment.CrossValidationResultProducer

Returns the tip text for this property

splitEvaluatorTipText(). Method in class weka.experiment.RandomSplitResultProducer

Returns the tip text for this property

splitOptions(String). Static method in class weka.core.Utils

Split up a string containing options into an array of strings, one for each option.

Splitter class weka.classifiers.trees.adtree.Splitter.

Abstract class representing a splitter node in an alternating tree.

Splitter(). Constructor for class weka.classifiers.trees.adtree.Splitter

splitVal(). Method in class weka.classifiers.trees.m5.RuleNode

Get the split point for this node

splitValue(). Method in interface weka.classifiers.trees.m5.SplitEvaluate

Returns the split value

splitValue(). Method in class weka.classifiers.trees.m5.YongSplitInfo

Returns the split value

splitValue(). Method in class weka.classifiers.trees.m5.CorrelationSplitInfo

Returns the split value

SpreadSubsample class weka.filters.supervised.instance.SpreadSubsample.

Produces a random subsample of a dataset.

SpreadSubsample(). Constructor for class weka.filters.supervised.instance.SpreadSubsample

sqrt(). Method in class weka.classifiers.functions.pace.DoubleVector

Returns the square-root of all the elements in the vector

square(). Method in class weka.classifiers.functions.pace.DoubleVector

Returns the squared vector

square(double). Static method in class weka.classifiers.functions.pace.Maths

Returns the square of a value

squeezeIn(int, int). Method in class weka.core.DynamicArrayOfPosInt

Sqeezes in the value in the specified position in the array.

stableSort(double[]). Static method in class weka.core.Utils

Sorts a given array of doubles in ascending order and returns an array of integers with the positions of the elements of the original array in the sorted array.

Stacking class weka.classifiers.meta.Stacking.

Implements stacking.

Stacking(). Constructor for class weka.classifiers.meta.Stacking

StackingC class weka.classifiers.meta.StackingC.

Implements StackingC.

StackingC(). Constructor for class weka.classifiers.meta.StackingC

Standardize class weka.filters.unsupervised.attribute.Standardize.

Standardizes all numeric attributes in the given dataset to have zero mean and unit variance.

Standardize(). Constructor for class weka.filters.unsupervised.attribute.Standardize

start(). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Start the plotting thread

start(). Method in class weka.gui.boundaryvisualizer.BoundaryPanelDistributed

Start processing

startLoading(). Method in class weka.gui.beans.Loader

Start loading data

StartSetHandler interface weka.attributeSelection.StartSetHandler.

Interface for search methods capable of doing something sensible given a starting set of attributes.

startSetTipText(). Method in class weka.attributeSelection.ExhaustiveSearch

Returns the tip text for this property

startSetTipText(). Method in class weka.attributeSelection.RandomSearch

Returns the tip text for this property

startSetTipText(). Method in class weka.attributeSelection.ForwardSelection

Returns the tip text for this property

startSetTipText(). Method in class weka.attributeSelection.GeneticSearch

Returns the tip text for this property

startSetTipText(). Method in class weka.attributeSelection.BestFirst

Returns the tip text for this property

startSetTipText(). Method in class weka.attributeSelection.Ranker

Returns the tip text for this property

Statistics class weka.core.Statistics.

Class implementing some distributions, tests, etc.

Statistics(). Constructor for class weka.core.Statistics

Stats class weka.experiment.Stats.

A class to store simple statistics

Stats class weka.classifiers.trees.j48.Stats.

Class implementing a statistical routine needed by J48 to compute its error estimate.

Stats(). Constructor for class weka.experiment.Stats

Stats(). Constructor for class weka.classifiers.trees.j48.Stats

statusMessage(String). Method in interface weka.gui.Logger

Sends the supplied message to the status line.

statusMessage(String). Method in class weka.gui.LogPanel

Sends the supplied message to the status line.

statusMessage(String). Method in class weka.gui.SysErrLog

Sends the supplied message to the status line.

stdDev. Variable in class weka.experiment.Stats

The std deviation of values at the last calculateDerived() call

steplsqr(PaceMatrix, IntVector, int, int, boolean). Method in class weka.classifiers.functions.pace.PaceMatrix

Stepwise least squares QR-decomposition of the problem A x = b

stepSizeTipText(). Method in class weka.experiment.LearningRateResultProducer

Returns the tip text for this property

stop(). Method in class weka.gui.beans.AbstractTestSetProducer

Stop any processing that the bean might be doing.

stop(). Method in class weka.gui.beans.TestSetMaker

stop(). Method in class weka.gui.beans.AbstractEvaluator

Stop any processing that the bean might be doing.

stop(). Method in class weka.gui.beans.ClassifierPerformanceEvaluator

Try and stop any action

stop(). Method in class weka.gui.beans.Filter

Stop all action if possible

stop(). Method in class weka.gui.beans.AbstractTrainingSetProducer

Stop any processing that the bean might be doing.

stop(). Method in class weka.gui.beans.TrainingSetMaker

Stop any action

stop(). Method in class weka.gui.beans.Classifier

Stop any classifier action

stop(). Method in class weka.gui.beans.AbstractTrainAndTestSetProducer

Stop any processing that the bean might be doing.

stop(). Method in class weka.gui.beans.IncrementalClassifierEvaluator

Stop all action

stop(). Method in class weka.gui.beans.TrainTestSplitMaker

Stop processing

stop(). Method in class weka.gui.beans.StripChart

Stop any processing that the bean might be doing.

stop(). Method in class weka.gui.beans.CrossValidationFoldMaker

Stop any action

stop(). Method in class weka.gui.beans.ClassAssigner

stop(). Method in interface weka.gui.beans.BeanCommon

Stop any processing that the bean might be doing.

stopPlotting(). Method in class weka.gui.boundaryvisualizer.BoundaryPanel

Stop the plotting thread

store(double, double, double). Method in class weka.classifiers.lazy.kstar.KStarCache

Stores the specified values in the cahce table for easy retrieval.

StratifiedRemoveFolds class weka.filters.supervised.instance.StratifiedRemoveFolds.

This filter takes a dataset and outputs folds suitable for cross validation.

StratifiedRemoveFolds(). Constructor for class weka.filters.supervised.instance.StratifiedRemoveFolds

stratify(Instances, int, Random). Static method in class weka.classifiers.rules.RuleStats

Stratify the given data into the given number of bags based on the class values.

stratify(int). Method in class weka.core.Instances

Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).

StreamableFilter interface weka.filters.StreamableFilter.

Interface for filters can work with a stream of instances.

STRING. Static variable in class weka.core.Attribute

Constant set for attributes with string values.

stringFreeStructure(). Method in class weka.core.Instances

Create a copy of the structure, but "cleanse" string types (i.e.

stringSize(FontMetrics). Method in class weka.gui.treevisualizer.Edge

This will calculate how large a rectangle using the FontMetrics passed that the lines of the label will take up

stringSize(FontMetrics). Method in class weka.gui.treevisualizer.Node

This will return the width and height of the rectangle that the text will fit into.

StringToNominal class weka.filters.unsupervised.attribute.StringToNominal.

Converts a string attribute (i.e.

StringToNominal(). Constructor for class weka.filters.unsupervised.attribute.StringToNominal

StringToWordVector class weka.filters.unsupervised.attribute.StringToWordVector.

Converts String attributes into a set of attributes representing word occurrence information from the text contained in the strings.

StringToWordVector(). Constructor for class weka.filters.unsupervised.attribute.StringToWordVector

Default constructor.

StringToWordVector(int). Constructor for class weka.filters.unsupervised.attribute.StringToWordVector

Constructor that allows specification of the target number of words in the output.

stringValue(Attribute). Method in class weka.core.Instance

Returns the string value of a nominal, string, or date attribute for the instance.

stringValue(int). Method in class weka.core.Instance

Returns the string value of a nominal, string, or date attribute for the instance.

StripChart class weka.gui.beans.StripChart.

Bean that can display a horizontally scrolling strip chart.

StripChart(). Constructor for class weka.gui.beans.StripChart

StripChartBeanInfo class weka.gui.beans.StripChartBeanInfo.

Bean info class for the strip chart bean

StripChartBeanInfo(). Constructor for class weka.gui.beans.StripChartBeanInfo

StripChartCustomizer class weka.gui.beans.StripChartCustomizer.

GUI Customizer for the strip chart bean

StripChartCustomizer(). Constructor for class weka.gui.beans.StripChartCustomizer

sub(int, Instance). Method in class weka.classifiers.trees.j48.Distribution

Subtracts given instance from given bag.

subsetDL(double, double, double). Static method in class weka.classifiers.rules.RuleStats

Subset description length:
S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95

subsetEstimate(DoubleVector). Method in class weka.classifiers.functions.pace.NormalMixture

Returns the estimate of optimal subset selection.

SubsetEvaluator class weka.attributeSelection.SubsetEvaluator.

Abstract attribute subset evaluator.

SubsetEvaluator(). Constructor for class weka.attributeSelection.SubsetEvaluator

substract(AlgVector). Method in class weka.core.AlgVector

Returns the difference of this vector minus another.

substract(AlgVector). Method in class weka.clusterers.AlgVector

Returns the difference of this vector minus another.

subtract(Distribution). Method in class weka.classifiers.trees.j48.Distribution

Subtracts the given distribution from this one.

subtract(double). Method in class weka.experiment.Stats

Removes a value to the observed values (no checking is done that the value being removed was actually added).

subtract(double, double). Method in class weka.experiment.PairedStats

Removes an observed pair of values.

subtract(double, double). Method in class weka.experiment.Stats

Subtracts a value that has been seen n times from the observed values

subtract(ItemSet). Method in class weka.associations.ItemSet

Subtracts an item set from another one.

subvector(int, int). Method in class weka.classifiers.functions.pace.DoubleVector

Returns a subvector.

subvector(int, int). Method in class weka.classifiers.functions.pace.IntVector

Returns a subvector.

subvector(IntVector). Method in class weka.classifiers.functions.pace.DoubleVector

Returns a subvector.

subvector(IntVector). Method in class weka.classifiers.functions.pace.IntVector

Returns a subvector as indexed by an IntVector.

sum. Variable in class weka.experiment.Stats

The sum of values seen

sum(). Method in class weka.classifiers.functions.pace.DoubleVector

Returns the sum of all elements in the vector.

sum(double[]). Static method in class weka.core.Utils

Computes the sum of the elements of an array of doubles.

sum(int[]). Static method in class weka.core.Utils

Computes the sum of the elements of an array of integers.

sum2(). Method in class weka.classifiers.functions.pace.DoubleVector

Returns the squared sum of all elements in the vector.

sum2(boolean). Method in class weka.classifiers.functions.pace.PaceMatrix

Squared sum of columns or rows of a matrix

sum2(DoubleVector). Method in class weka.classifiers.functions.pace.DoubleVector

Returns ||u-v||^2

sum2(int, int, int, boolean). Method in class weka.classifiers.functions.pace.PaceMatrix

Squared sum of a column or row in a matrix

Summarizable interface weka.core.Summarizable.

Interface to something that provides a short textual summary (as opposed to toString() which is usually a fairly complete description) of itself.

sumOfWeights(). Method in class weka.core.Instances

Computes the sum of all the instances' weights.

sumSq. Variable in class weka.experiment.Stats

The sum of values squared seen

SupervisedFilter interface weka.filters.SupervisedFilter.

Interface for filters that make use of a class attribute.

support(). Method in class weka.associations.ItemSet

Outputs the support for an item set.

supportPoints(DoubleVector, int). Method in class weka.classifiers.functions.pace.MixtureDistribution

Contructs the set of support points for mixture estimation.

supportPoints(DoubleVector, int). Method in class weka.classifiers.functions.pace.ChisqMixture

Contructs the set of support points for mixture estimation.

supportPoints(DoubleVector, int). Method in class weka.classifiers.functions.pace.NormalMixture

Contructs the set of support points for mixture estimation.

supportsCustomEditor(). Method in class weka.gui.GenericArrayEditor

Returns true because we do support a custom editor.

supportsCustomEditor(). Method in class weka.gui.GenericObjectEditor

Returns true because we do support a custom editor.

supportsCustomEditor(). Method in class weka.gui.FileEditor

Returns true because we do support a custom editor.

supportsCustomEditor(). Method in class weka.gui.CostMatrixEditor

Indicates whether the cost matrix can be edited in a GUI, which it can.

SVMAttributeEval class weka.attributeSelection.SVMAttributeEval.

Class for Evaluating attributes individually by using the SVM classifier.

SVMAttributeEval(). Constructor for class weka.attributeSelection.SVMAttributeEval

Constructor

swap(int, int). Method in class weka.core.FastVector

Swaps two elements in the vector.

swap(int, int). Method in class weka.classifiers.functions.pace.DoubleVector

Swaps the values stored at i and j

swap(int, int). Method in class weka.classifiers.functions.pace.IntVector

Swaps the values stored at i and j

SwapValues class weka.filters.unsupervised.attribute.SwapValues.

Swaps two values of a nominal attribute.

Valid filter-specific options are:

-C col
Index of the attribute to be changed.

SwapValues(). Constructor for class weka.filters.unsupervised.attribute.SwapValues

switchToAdvanced(Experiment). Method in class weka.gui.experiment.SetupModePanel

Switches to the advanced setup mode.

switchToSimple(Experiment). Method in class weka.gui.experiment.SetupModePanel

Switches to the simple setup mode only if allowed to.

symmetricalUncertainty(double[][]). Static method in class weka.core.ContingencyTables

Calculates the symmetrical uncertainty for base 2.

SymmetricalUncertAttributeEval class weka.attributeSelection.SymmetricalUncertAttributeEval.

Class for Evaluating attributes individually by measuring symmetrical uncertainty with respect to the class.

SymmetricalUncertAttributeEval(). Constructor for class weka.attributeSelection.SymmetricalUncertAttributeEval

Constructor

synopsis(). Method in class weka.core.Option

Returns the option's synopsis.

SysErrLog class weka.gui.SysErrLog.

This Logger just sends messages to System.err.

SysErrLog(). Constructor for class weka.gui.SysErrLog

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