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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.classifiers.adtree.ADTree
saveInstanceDataTipText(). Method in class weka.clusterers.Cobweb
Returns the tip text for this property
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.
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.
SEARCHPATH_ALL. Static variable in class weka.classifiers.adtree.ADTree
The search modes
SEARCHPATH_HEAVIEST. Static variable in class weka.classifiers.adtree.ADTree
SEARCHPATH_RANDOM. Static variable in class weka.classifiers.adtree.ADTree
SEARCHPATH_ZPURE. Static variable in class weka.classifiers.adtree.ADTree
searchPathTipText(). Method in class weka.classifiers.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.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.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.ThresholdSelector
seedTipText(). Method in class weka.classifiers.CostSensitiveClassifier
seedTipText(). Method in class weka.clusterers.SimpleKMeans
Returns the tip text for this property
seedTipText(). Method in class weka.clusterers.EM
Returns the tip text for this property
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
selectionThresholdTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
selectModel(Instances). Method in class weka.classifiers.j48.ModelSelection
Selects a model for the given dataset.
selectModel(Instances). Method in class weka.classifiers.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances). Method in class weka.classifiers.j48.C45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances). Method in class weka.classifiers.j48.ModelSelection
Selects a model for the given train data using the given test data
selectModel(Instances, Instances). Method in class weka.classifiers.j48.BinC45ModelSelection
Selects C4.5-type split for the given dataset.
selectModel(Instances, Instances). Method in class weka.classifiers.j48.C45ModelSelection
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.
separatorToString(). Static method in class weka.classifiers.m5.M5Utils
Prints sepearating line
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.
This class stores an object serialized in memory.
SerializedObject(Object). Constructor for class weka.core.SerializedObject
Serializes the supplied object into a byte array without compression.
SerializedObject(Object, boolean). Constructor for class weka.core.SerializedObject
Serializes the supplied object into a byte array.
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.SpreadSubsampleFilter
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.
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.SelectedTagEditor
Sets the current property value as text.
setAsText(String). Method in class weka.gui.GenericObjectEditor
Returns null as we don't support getting/setting values as text.
setAsText(String). Method in class weka.gui.CostMatrixEditor
Returns null as we don't support getting/setting values as text.
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.InstanceFilter
Sets attribute to be used for selection
setAttributeIndex(int). Method in class weka.filters.SwapAttributeValuesFilter
Sets index of the attribute used.
setAttributeIndex(int). Method in class weka.filters.StringToNominalFilter
Sets index of the attribute used.
setAttributeIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the attribute used.
setAttributeIndex(int). Method in class weka.filters.AddFilter
Set the index where the attribute will be inserted
setAttributeIndex(int). Method in class weka.filters.MakeIndicatorFilter
Sets index of of the attribute used.
setAttributeIndices(String). Method in class weka.filters.AbstractTimeSeriesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.CopyAttributesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.NumericTransformFilter
Set which attributes are to be transformed (or kept if invert is true).
setAttributeIndices(String). Method in class weka.filters.FirstOrderFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.AttributeFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndices(String). Method in class weka.filters.DiscretizeFilter
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeIndicesArray(int[]). Method in class weka.filters.AbstractTimeSeriesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.CopyAttributesFilter
Set which attributes are to be copied (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.NumericTransformFilter
Set which attributes are to be transformed (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.FirstOrderFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.AttributeFilter
Set which attributes are to be deleted (or kept if invert is true)
setAttributeIndicesArray(int[]). Method in class weka.filters.DiscretizeFilter
Sets which attributes are to be Discretized (only numeric attributes among the selection will be Discretized).
setAttributeName(String). Method in class weka.filters.AddFilter
Set the new attribute's name
setAttributeSelectionMethod(SelectedTag). Method in class weka.classifiers.LinearRegression
Sets the method used to select attributes for use in the linear regression.
setAttributeType(SelectedTag). Method in class weka.filters.AttributeTypeFilter
Sets the type of attribute to delete.
setAutoBuild(boolean). Method in class weka.classifiers.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.MetaCost
Sets the size of each bag, as a percentage of the training set size.
setBagSizePercent(int). Method in class weka.classifiers.Bagging
Sets the size of each bag, as a percentage of the training set size.
setBaseClassifiers(Classifier[]). Method in class weka.classifiers.Stacking
Sets the list of possible classifers to choose from.
setBaseExperiment(Experiment). Method in class weka.experiment.RemoteExperiment
Set the base experiment.
setBaseInstances(Instances). Method in class weka.gui.explorer.PreprocessPanel
Tells the panel to use a new base set of instances.
setBaseInstancesFromDB(InstanceQuery). Method in class weka.gui.explorer.PreprocessPanel
Loads instances from a database
setBaseInstancesFromDBQ(). 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.
setBaseInstancesFromFile(File). Method in class weka.gui.explorer.PreprocessPanel
Loads results from a set of instances contained in the supplied file.
setBaseInstancesFromFileQ(). 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.
setBaseInstancesFromURL(URL). Method in class weka.gui.explorer.PreprocessPanel
Loads instances from a URL.
setBaseInstancesFromURLQ(). 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.
setBias(double). Method in class weka.classifiers.VFI
Set the value of the exponential bias towards more confident intervals
setBiasToUniformClass(double). Method in class weka.filters.ResampleFilter
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.NominalToBinaryFilter
Sets if binary attributes are to be treates as nominal ones.
setBinarySplits(boolean). Method in class weka.classifiers.j48.J48
Set the value of binarySplits.
setBinarySplits(boolean). Method in class weka.classifiers.j48.PART
Set the value of binarySplits.
setBins(int). Method in class weka.filters.DiscretizeFilter
Sets the number of bins to divide each selected numeric attribute into
setBlendFactor(int). Method in class weka.classifiers.kstar.KStarNumericAttribute
Set the blending factor
setBlendMethod(int). Method in class weka.classifiers.kstar.KStarNumericAttribute
Set the blending method
setC(double). Method in class weka.classifiers.SMO
Set the value of C.
setCacheKeyName(String). Method in class weka.experiment.DatabaseResultListener
Set the value of CacheKeyName.
setCacheSize(int). Method in class weka.classifiers.SMO
Set the value of the kernel cache.
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.
setCenter(double). Method in class weka.gui.treevisualizer.Node
Set the value of center.
setChildForBranch(int, PredictionNode). Method in class weka.classifiers.adtree.Splitter
Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode). Method in class weka.classifiers.adtree.TwoWayNominalSplit
Sets the child for a branch of the split.
setChildForBranch(int, PredictionNode). Method in class weka.classifiers.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.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.
setClassForIRStatistics(int). Method in class weka.experiment.ClassifierSplitEvaluator
Set the value of ClassForIRStatistics.
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.MetaCost
Sets the distribution classifier
setClassifier(Classifier). Method in class weka.classifiers.AdaBoostM1
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.ClassificationViaRegression
Set the base classifier.
setClassifier(Classifier). Method in class weka.classifiers.AttributeSelectedClassifier
Sets the classifier
setClassifier(Classifier). Method in class weka.classifiers.CheckClassifier
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.CVParameterSelection
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.Bagging
Set the classifier for bagging.
setClassifier(Classifier). Method in class weka.classifiers.RegressionByDiscretization
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.LogitBoost
Set the classifier for boosting.
setClassifier(Classifier). Method in class weka.classifiers.AdditiveRegression
Sets the classifier
setClassifier(Classifier). Method in class weka.classifiers.BVDecompose
Set the classifiers being analysed
setClassifier(Classifier). Method in class weka.classifiers.CostSensitiveClassifier
Sets the distribution classifier
setClassifier(Classifier). Method in class weka.classifiers.DistributionMetaClassifier
Set the base classifier.
setClassifier(Classifier). Method in class weka.classifiers.FilteredClassifier
Sets the classifier
setClassifier(Classifier). Method in class weka.experiment.ClassifierSplitEvaluator
Sets the classifier.
setClassifier(Classifier). Method in class weka.experiment.RegressionSplitEvaluator
Sets the classifier.
setClassifierName(String). Method in class weka.experiment.ClassifierSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifierName(String). Method in class weka.experiment.RegressionSplitEvaluator
Set the Classifier to use, given it's class name.
setClassifiers(Classifier[]). Method in class weka.classifiers.MultiScheme
Sets the list of possible classifers to choose from.
setClassIndex(int). Method in class weka.classifiers.BVDecompose
Sets index of attribute to discretize on
setClassIndex(int). Method in class weka.core.Instances
Sets the class index of the set.
setClassMissing(). Method in class weka.core.Instance
Sets the class value of an instance to be "missing".
setClassName(String). Method in class weka.filters.NumericTransformFilter
Sets the class containing the transformation method.
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.DistributionMetaClusterer
Set the base clusterer.
setClusterer(Clusterer). Method in class weka.clusterers.ClusterEvaluation
set the clusterer
setColor(Color). Method in class weka.gui.treevisualizer.Node
Set the value of color.
setColourIndex(int). Method in class weka.gui.visualize.VisualizePanel
Sets the index used for colouring.
setColours(FastVector). Method in class weka.gui.visualize.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.
setConfidenceFactor(float). Method in class weka.classifiers.j48.J48
Set the value of CF.
setConfidenceFactor(float). Method in class weka.classifiers.j48.PART
Set the value of CF.
setConnectPoints(boolean[]). Method in class weka.gui.visualize.PlotData2D
Set whether consecutive points should be connected by lines
setConnectPoints(FastVector). Method in class weka.gui.visualize.PlotData2D
Set whether consecutive points should be connected by lines
setCostMatrix(CostMatrix). Method in class weka.classifiers.MetaCost
Sets the misclassification cost matrix.
setCostMatrix(CostMatrix). Method in class weka.classifiers.CostSensitiveClassifier
Sets the misclassification cost matrix.
setCostMatrixSource(SelectedTag). Method in class weka.classifiers.MetaCost
Sets the source location of the cost matrix.
setCostMatrixSource(SelectedTag). Method in class weka.classifiers.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.DecisionTable
Sets the number of folds for cross validation (1 = leave one out)
setCrossValidate(boolean). Method in class weka.classifiers.IBk
Sets whether hold-one-out cross-validation will be used to select the best k value
setCustomColour(Color). Method in class weka.gui.visualize.PlotData2D
Set a custom colour to use for this plot.
setCutoff(double). Method in class weka.clusterers.Cobweb
set the cutoff
setCVisible(boolean). Method in class weka.gui.treevisualizer.Node
Sets all the children of this node either to visible or invisible
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
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.attributeSelection.RaceSearch
Set whether verbose output should be generated.
setDebug(boolean). Method in class weka.classifiers.AdaBoostM1
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.CheckClassifier
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.CVParameterSelection
Sets debugging mode
setDebug(boolean). Method in class weka.classifiers.IBk
Set the value of Debug.
setDebug(boolean). Method in class weka.classifiers.RegressionByDiscretization
Sets whether debugging output will be printed
setDebug(boolean). Method in class weka.classifiers.LogitBoost
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.MultiScheme
Set debugging mode
setDebug(boolean). Method in class weka.classifiers.AdditiveRegression
Set whether debugging output is produced.
setDebug(boolean). Method in class weka.classifiers.BVDecompose
Sets debugging mode
setDebug(boolean). Method in class weka.classifiers.Logistic
Sets whether debugging output will be printed.
setDebug(boolean). Method in class weka.classifiers.LWR
Sets whether debugging output should be produced
setDebug(boolean). Method in class weka.classifiers.LinearRegression
Controls whether debugging output will be printed
setDebug(boolean). Method in class weka.clusterers.EM
Set debug mode - verbose output
setDebug(boolean). Method in class weka.filters.AttributeExpressionFilter
Set debug mode.
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.InstanceViewer
setDebug(boolean). Method in class weka.gui.streams.InstanceJoiner
setDebug(boolean). Method in class weka.gui.streams.InstanceCounter
setDebug(boolean). Method in class weka.gui.streams.InstanceTable
setDecay(boolean). Method in class weka.classifiers.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
setDelta(double). Method in class weka.associations.Apriori
Set the value of delta.
setDesignatedClass(SelectedTag). Method in class weka.classifiers.ThresholdSelector
Sets the method to determine which class value to optimize.
setDirection(SelectedTag). Method in class weka.attributeSelection.BestFirst
Set the search direction
setDisplayRules(boolean). Method in class weka.classifiers.DecisionTable
Sets whether rules are to be printed
setDistanceWeighting(SelectedTag). Method in class weka.classifiers.IBk
Sets the distance weighting method used.
setDistributionClassifier(DistributionClassifier). Method in class weka.classifiers.ThresholdSelector
Set the DistributionClassifier for which threshold is set.
setDistributionClassifier(DistributionClassifier). Method in class weka.classifiers.MultiClassClassifier
Set the base classifier.
setDistributionSpread(double). Method in class weka.filters.SpreadSubsampleFilter
Sets the value for the distribution spread
setDontStratifyData(boolean). Method in class weka.filters.SplitDatasetFilter
Sets whether stratification is not performed.
setDoXval(boolean). Method in class weka.clusterers.ClusterEvaluation
set whether or not to do cross validation
setElement(int, int, double). Method in class weka.core.Matrix
Sets an element of the matrix to the given value.
setElementAt(Object, int). Method in class weka.core.FastVector
Sets the element at the given index.
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.kstar.KStar
Set whether entropic blending is to be used.
setEpsilon(double). Method in class weka.classifiers.SMO
Set the value of epsilon.
setErrorCorrectionMode(SelectedTag). Method in class weka.classifiers.MultiClassClassifier
Sets the error correction mode used.
setEvaluationMode(SelectedTag). Method in class weka.classifiers.ThresholdSelector
Sets the evaluation mode used.
setEvaluator(ASEvaluation). Method in class weka.attributeSelection.AttributeSelection
set the attribute/subset evaluator
setEvaluator(ASEvaluation). Method in class weka.classifiers.AttributeSelectedClassifier
Sets the attribute evaluator
setEvaluator(ASEvaluation). Method in class weka.filters.AttributeSelectionFilter
set a string holding the name of a attribute/subset evaluator
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.experiment.RemoteExperimentSubTask
Set the experiment for this sub task
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.RunPanel
Sets the experiment the panel operates on.
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.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.ResultsPanel
Tells the panel to use a new experiment.
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.SMO
Set the value of exponent.
setExponent(double). Method in class weka.classifiers.VotedPerceptron
Set the value of exponent.
setExpression(String). Method in class weka.filters.AttributeExpressionFilter
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
setFillWithMissing(boolean). Method in class weka.filters.AbstractTimeSeriesFilter
Sets whether missing values should be used rather than removing instances where the translated value is not known (due to border effects).
setFilter(Filter). Method in class weka.classifiers.FilteredClassifier
Sets the filter
setFindNumBins(boolean). Method in class weka.filters.DiscretizeFilter
Set the value of FindNumBins.
setFirstValueIndex(int). Method in class weka.filters.SwapAttributeValuesFilter
Sets index of the first value used.
setFirstValueIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the first value used.
setFold(int). Method in class weka.filters.SplitDatasetFilter
Selects a fold.
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.clusterers.ClusterEvaluation
set the number of folds to use for cross validation
setFoldsType(SelectedTag). Method in class weka.attributeSelection.RaceSearch
Set the xfold type
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.
setGlobalBlend(int). Method in class weka.classifiers.kstar.KStar
Set the global blend parameter
setGUI(boolean). Method in class weka.classifiers.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.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
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.SpreadSubsampleFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.InstanceFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AbstractTimeSeriesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.TimeSeriesTranslateFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.TimeSeriesDeltaFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.SwapAttributeValuesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.StringToNominalFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.EmptyAttributeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NormalizationFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.SparseToNonSparseFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.MergeTwoValuesFilter
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.SplitDatasetFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.CopyAttributesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.ResampleFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NumericTransformFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NonSparseToSparseFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AttributeTypeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.ObfuscateFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NominalToBinaryFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.ReplaceMissingValuesFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AddFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.FirstOrderFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.NumericToBinaryFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.RandomizeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.MakeIndicatorFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AttributeExpressionFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.AttributeFilter
Sets the format of the input instances.
setInputFormat(Instances). Method in class weka.filters.DiscretizeFilter
Sets the format of the input instances.
setInstanceRange(int). Method in class weka.filters.AbstractTimeSeriesFilter
Sets the number of instances forward to translate values between.
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.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.PairedTTester
Set the value of Instances.
setInstances(Instances). Method in class weka.experiment.DatabaseResultProducer
Sets the dataset that results will be obtained for.
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.experiment.ResultsPanel
Sets up the panel with a new set of instances, attempting to guess the correct settings for various columns.
setInstances(Instances). Method in class weka.gui.explorer.ClustererPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.gui.explorer.AttributeSelectionPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.gui.explorer.ClassifierPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.gui.explorer.AssociationsPanel
Tells the panel to use a new set of instances.
setInstances(Instances). Method in class weka.gui.visualize.Plot2D
Sets the master plot from a set of instances
setInstances(Instances). Method in class weka.gui.visualize.AttributePanel
This sets the instances to be drawn into the attribute panel
setInstances(Instances). Method in class weka.gui.visualize.VisualizePanel
Tells the panel to use a new set of instances.
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.
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.
setInstancesIndices(String). Method in class weka.filters.SplitDatasetFilter
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.
setInvert(boolean). Method in class weka.core.Range
Sets whether the range sense is inverted, i.e.
setInvertSelection(boolean). Method in class weka.filters.InstanceFilter
Set whether selected values should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.AbstractTimeSeriesFilter
Set whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.SplitDatasetFilter
Sets if selection is to be inverted.
setInvertSelection(boolean). Method in class weka.filters.CopyAttributesFilter
Set whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.NumericTransformFilter
Set whether selected columns should be transformed or not.
setInvertSelection(boolean). Method in class weka.filters.AttributeFilter
Set whether selected columns should be removed or kept.
setInvertSelection(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether selected columns should be removed or kept.
setJitter(int). Method in class weka.gui.visualize.Plot2D
Set level of jitter and repaint the plot using the new jitter value
setKeyFieldName(String). Method in class weka.experiment.AveragingResultProducer
Set the value of KeyFieldName.
setKNN(int). Method in class weka.classifiers.IBk
Set the number of neighbours the learner is to use.
setKNN(int). Method in class weka.classifiers.LWR
Sets the number of neighbours used for kernel bandwidth setting.
setLearningRate(double). Method in class weka.classifiers.neural.NeuralNetwork
The learning rate can be set using this command.
setLocallyPredictive(boolean). Method in class weka.attributeSelection.CfsSubsetEval
Include locally predictive attributes
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.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.SMO
Set whether lower-order terms are to be used.
setLowerSize(int). Method in class weka.experiment.LearningRateResultProducer
Set the value of LowerSize.
setMakeBinary(boolean). Method in class weka.filters.DiscretizeFilter
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.InstanceFilter
Sets whether missing values are counted as a match.
setMaxCount(double). Method in class weka.filters.SpreadSubsampleFilter
Sets the value for the max count
setMaxGenerations(int). Method in class weka.attributeSelection.GeneticSearch
set the number of generations to evaluate
setMaxIterations(int). Method in class weka.classifiers.AdaBoostM1
Set the maximum number of boost iterations
setMaxIterations(int). Method in class weka.classifiers.LogitBoost
Set the maximum number of boost iterations
setMaxIterations(int). Method in class weka.clusterers.EM
Set the maximum number of iterations to perform
setMaxK(int). Method in class weka.classifiers.VotedPerceptron
Set the value of maxK.
setMaxModels(int). Method in class weka.classifiers.AdditiveRegression
Set the maximum number of models to generate
setMaxStale(int). Method in class weka.classifiers.DecisionTable
Sets the number of non improving decision tables to consider before abandoning the search.
setMeanSquared(boolean). Method in class weka.classifiers.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.Stacking
Adds meta classifier
setMethod(NeuralMethod). Method in class weka.classifiers.neural.NeuralNode
Set how this node should operate (note that the neural method has no internal state, so the same object can be used by any number of nodes.
setMethodName(String). Method in class weka.filters.NumericTransformFilter
Set the transformation method.
setMetricType(SelectedTag). Method in class weka.associations.Apriori
Set the metric type for ranking rules
setMinBucketSize(int). Method in class weka.classifiers.OneR
Set the value of minBucketSize.
setMinimizeExpectedCost(boolean). Method in class weka.classifiers.CostSensitiveClassifier
Set the value of MinimizeExpectedCost.
setMinMetric(double). Method in class weka.associations.Apriori
Set the value of minConfidence.
setMinNumObj(int). Method in class weka.classifiers.j48.J48
Set the value of minNumObj.
setMinNumObj(int). Method in class weka.classifiers.j48.PART
Set the value of minNumObj.
setMinStdDev(double). Method in class weka.clusterers.EM
Set the minimum value for standard deviation when calculating normal density.
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.kstar.KStarNumericAttribute
Set the missing value mode.
setMissingMode(SelectedTag). Method in class weka.classifiers.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
setModelType(SelectedTag). Method in class weka.classifiers.m5.M5Prime
Set the value of Model.
setModifyHeader(boolean). Method in class weka.filters.InstanceFilter
Sets whether the header will be modified when selecting on nominal attributes.
setMomentum(double). Method in class weka.classifiers.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.filters.AttributeExpressionFilter
Set the name for the new attribute.
setName(String). Method in class weka.gui.visualize.VisualizePanel
Set a name for this plot
setNominalIndices(String). Method in class weka.filters.InstanceFilter
Set which nominal labels are to be included in the selection.
setNominalIndicesArr(int[]). Method in class weka.filters.InstanceFilter
Set which values of a nominal attribute are to be used for selection.
setNominalLabels(String). Method in class weka.filters.AddFilter
Set the labels for nominal attribute creation.
setNominalToBinaryFilter(boolean). Method in class weka.classifiers.neural.NeuralNetwork
setNoNormalization(boolean). Method in class weka.classifiers.IBk
Set whether normalization is turned off.
setNormalize(boolean). Method in class weka.attributeSelection.PrincipalComponents
Set whether input data will be normalized.
setNormalizeAttributes(boolean). Method in class weka.classifiers.neural.NeuralNetwork
setNormalizeData(boolean). Method in class weka.classifiers.SMO
Set whether data is to be normalized.
setNormalizeNumericClass(boolean). Method in class weka.classifiers.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.
setNumBins(int). Method in class weka.classifiers.RegressionByDiscretization
Sets the number of bins the class attribute will be discretized into.
setNumClusters(int). Method in class weka.clusterers.SimpleKMeans
set the number of clusters to generate
setNumClusters(int). Method in class weka.clusterers.EM
Set the number of clusters (-1 to select by CV).
setNumeric(boolean). Method in class weka.filters.MakeIndicatorFilter
Sets if the new Attribute is to be numeric.
setNumFolds(int). Method in class weka.classifiers.Stacking
Sets the number of folds for the cross-validation.
setNumFolds(int). Method in class weka.classifiers.CVParameterSelection
Set the number of folds used for cross-validation.
setNumFolds(int). Method in class weka.classifiers.MultiScheme
Sets the number of folds for cross-validation.
setNumFolds(int). Method in class weka.classifiers.j48.J48
Set the value of numFolds.
setNumFolds(int). Method in class weka.classifiers.j48.PART
Set the value of numFolds.
setNumFolds(int). Method in class weka.experiment.CrossValidationResultProducer
Set the value of NumFolds.
setNumFolds(int). Method in class weka.filters.SplitDatasetFilter
Sets the number of folds the dataset is split into.
setNumIterations(int). Method in class weka.classifiers.MetaCost
Sets the number of bagging iterations
setNumIterations(int). Method in class weka.classifiers.Bagging
Sets the number of bagging iterations
setNumIterations(int). Method in class weka.classifiers.VotedPerceptron
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.adtree.ADTree
Sets the number of boosting iterations.
setNumRules(int). Method in class weka.associations.Apriori
Set the value of numRules.
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.
setNumXValFolds(int). Method in class weka.classifiers.ThresholdSelector
Set the number of folds used for cross-validation.
setOnDemandDirectory(File). Method in class weka.classifiers.MetaCost
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File). Method in class weka.classifiers.CostSensitiveClassifier
Sets the directory that will be searched for cost files when loading on demand.
setOnDemandDirectory(File). Method in class weka.experiment.CostSensitiveClassifierSplitEvaluator
Sets the directory that will be searched for cost files when loading on demand.
setOptimizeBins(boolean). Method in class weka.classifiers.RegressionByDiscretization
Sets whether the discretizer optimizes the number of bins
setOptions(int, int, int). Method in class weka.classifiers.kstar.KStarNumericAttribute
Set options.
setOptions(int, int, int). Method in class weka.classifiers.kstar.KStarNominalAttribute
Sets the options.
setOptions(String[]). Method in class weka.associations.Apriori
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.SymmetricalUncertAttributeEval
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.RankSearch
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.RaceSearch
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.RandomSearch
Parses a given list of options.
setOptions(String[]). Method in class weka.attributeSelection.GainRatioAttributeEval
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.classifiers.MetaCost
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.DecisionTable
Parses the options for this object.
setOptions(String[]). Method in class weka.classifiers.AdaBoostM1
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.ClassificationViaRegression
Sets a given list of options.
setOptions(String[]). Method in class weka.classifiers.AttributeSelectedClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.Stacking
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.CVParameterSelection
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.OneR
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.Bagging
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.ThresholdSelector
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.IBk
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.RegressionByDiscretization
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LogitBoost
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.MultiClassClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.MultiScheme
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.AdditiveRegression
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.CostSensitiveClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.NaiveBayes
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.SMO
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.Logistic
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LWR
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.VotedPerceptron
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.DistributionMetaClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.VFI
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.LinearRegression
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.FilteredClassifier
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.adtree.ADTree
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.j48.J48
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.j48.PART
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.kstar.KStar
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.m5.M5Prime
Parses a given list of options.
setOptions(String[]). Method in class weka.classifiers.neural.NeuralNetwork
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.SimpleKMeans
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.DistributionMetaClusterer
Parses a given list of options.
setOptions(String[]). Method in interface weka.core.OptionHandler
Sets the OptionHandler's options using the given list.
setOptions(String[]). Method in class weka.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.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.PairedTTester
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.filters.SpreadSubsampleFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.filters.InstanceFilter
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.AbstractTimeSeriesFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.filters.SwapAttributeValuesFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.StringToNominalFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.MergeTwoValuesFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.SplitDatasetFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.CopyAttributesFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.filters.ResampleFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.filters.NumericTransformFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.AttributeTypeFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.filters.NominalToBinaryFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.AddFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.filters.AttributeSelectionFilter
Parses a given list of options.
setOptions(String[]). Method in class weka.filters.FirstOrderFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.filters.RandomizeFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.filters.MakeIndicatorFilter
Parses the options for this object.
setOptions(String[]). Method in class weka.filters.AttributeExpressionFilter
Parses a list of options for this object.
setOptions(String[]). Method in class weka.filters.AttributeFilter
Parses a given list of options controlling the behaviour of this object.
setOptions(String[]). Method in class weka.filters.DiscretizeFilter
Parses the options for this object.
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.
setParent(Edge). Method in class weka.gui.treevisualizer.Node
Set the value of parent.
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
setPopulationSize(int). Method in class weka.attributeSelection.GeneticSearch
set the population size
setPreprocess(PreprocessPanel). Method in class weka.gui.explorer.ClustererPanel
Sets the preprocess panel through which user selected filters can be applied to any supplied test data
setPreprocess(PreprocessPanel). Method in class weka.gui.explorer.ClassifierPanel
Sets the preprocess panel through which user selected filters can be applied to any supplied test data
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
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.
setPruningFactor(double). Method in class weka.classifiers.m5.M5Prime
Set the value of PruningFactor.
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
setRandomizeData(boolean). Method in class weka.experiment.RandomSplitResultProducer
Set to true if dataset is to be randomized
setRandomSeed(int). Method in class weka.classifiers.adtree.ADTree
Sets random seed for a random walk.
setRandomSeed(int). Method in class weka.filters.SpreadSubsampleFilter
Sets the random number seed.
setRandomSeed(int). Method in class weka.filters.ResampleFilter
Sets the random number seed.
setRandomSeed(int). Method in class weka.filters.RandomizeFilter
Set the random number generator seed value.
setRandomSeed(long). Method in class weka.classifiers.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.MultiClassClassifier
Sets the multiplier when generating random codes.
setRangeCorrection(SelectedTag). Method in class weka.classifiers.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.j48.J48
Set the value of reducedErrorPruning.
setReducedErrorPruning(boolean). Method in class weka.classifiers.j48.PART
Set the value of reducedErrorPruning.
setRefer(String). Method in class weka.gui.treevisualizer.Node
Set the value of refer.
setRelationName(String). Method in class weka.core.Instances
Sets the relation's name.
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
setRescaleKernel(boolean). Method in class weka.classifiers.SMO
Set whether kernel is to be rescaled.
setReset(boolean). Method in class weka.classifiers.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.
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.
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.
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
setSampleSizePercent(double). Method in class weka.filters.ResampleFilter
Sets the size of the subsample, as a percentage of the original set.
setSaveInstanceData(boolean). Method in class weka.classifiers.adtree.ADTree
Sets whether the tree is to save instance data.
setSaveInstanceData(boolean). Method in class weka.classifiers.j48.J48
Set whether instance data is to be saved.
setSaveInstanceData(boolean). Method in class weka.clusterers.Cobweb
Set the value of saveInstances.
setSearch(ASSearch). Method in class weka.attributeSelection.AttributeSelection
set the search method
setSearch(ASSearch). Method in class weka.classifiers.AttributeSelectedClassifier
Sets the search method
setSearch(ASSearch). Method in class weka.filters.AttributeSelectionFilter
Set as string holding the name of a search class
setSearchPath(SelectedTag). Method in class weka.classifiers.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.SwapAttributeValuesFilter
Sets index of the second value used.
setSecondValueIndex(int). Method in class weka.filters.MergeTwoValuesFilter
Sets index of the second value used.
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.MetaCost
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.AdaBoostM1
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.Stacking
Sets the seed for random number generation.
setSeed(int). Method in class weka.classifiers.CVParameterSelection
Sets the seed for random number generation.
setSeed(int). Method in class weka.classifiers.Bagging
Set the seed for random number generation.
setSeed(int). Method in class weka.classifiers.ThresholdSelector
Sets the seed for random number generation.
setSeed(int). Method in class weka.classifiers.LogitBoost
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.MultiScheme
Sets the seed for random number generation.
setSeed(int). Method in class weka.classifiers.BVDecompose
Sets the random number seed
setSeed(int). Method in class weka.classifiers.CostSensitiveClassifier
Set seed for resampling.
setSeed(int). Method in class weka.classifiers.VotedPerceptron
Set the value of Seed.
setSeed(int). Method in class weka.classifiers.evaluation.EvaluationUtils
Sets the seed for randomization during cross-validation
setSeed(int). Method in class weka.clusterers.SimpleKMeans
Set the random number seed
setSeed(int). Method in class weka.clusterers.EM
Set the random number seed
setSeed(int). Method in class weka.clusterers.ClusterEvaluation
set the seed to use for cross validation
setSeed(long). Method in class weka.filters.SplitDatasetFilter
Sets the random number seed for shuffling the dataset.
setSelectionThreshold(double). Method in class weka.attributeSelection.RaceSearch
Set the threshold by which the AttributeSelection module can discard attributes.
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.AdditiveRegression
Set the shrinkage parameter
setSigma(int). Method in class weka.attributeSelection.ReliefFAttributeEval
Sets the sigma value.
setSignificanceLevel(double). Method in class weka.associations.Apriori
Set the value of significanceLevel.
setSignificanceLevel(double). Method in class weka.attributeSelection.RaceSearch
Sets the significance level to use
setSignificanceLevel(double). Method in class weka.experiment.PairedTTester
Set the value of SignificanceLevel.
setSIndex(int). Method in class weka.gui.visualize.VisualizePanel
Set the shape for creating splits.
setSingle(String). Method in class weka.gui.ResultHistoryPanel
Sets the single-click display to view the named result.
setSource(File). Method in class weka.core.converters.AbstractLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in class weka.core.converters.CSVLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in 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 interface weka.core.converters.Loader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in class weka.core.converters.C45Loader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(File). Method in class weka.core.converters.SerializedInstancesLoader
Resets the Loader object and sets the source of the data set to be the supplied File object.
setSource(InputStream). Method in class weka.core.converters.AbstractLoader
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 interface weka.core.converters.Loader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(InputStream). Method in class weka.core.converters.SerializedInstancesLoader
Resets the Loader object and sets the source of the data set to be the supplied InputStream.
setSource(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.InstanceFilter
Split point to be used for selection on numeric attribute.
setSplitPoint(Instances). Method in class weka.classifiers.j48.BinC45Split
Sets split point to greatest value in given data smaller or equal to old split point.
setSplitPoint(Instances). Method in class weka.classifiers.j48.C45Split
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.
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.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
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.Ranker
Set the threshold by which the AttributeSelection module can discard attributes.
setThreshold(double). Method in class weka.attributeSelection.AttributeSelection
set the threshold by which to select features from a ranked list
setThreshold(double). Method in class weka.attributeSelection.WrapperSubsetEval
Set the value of the threshold for repeating cross validation
setToleranceParameter(double). Method in class weka.classifiers.SMO
Set the value of tolerance parameter.
setTop(double). Method in class weka.gui.treevisualizer.Node
Set the value of top.
setTrainingTime(int). Method in class weka.classifiers.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.
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
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.neural.NeuralConnection
setUnpruned(boolean). Method in class weka.classifiers.j48.J48
Set the value of unpruned.
setUpComboBoxes(Instances). Method in class weka.gui.visualize.VisualizePanel
SetupPanel class weka.gui.experiment.SetupPanel.
This panel controls the configuration of an experiment.
SetupPanel(). Constructor for class weka.gui.experiment.SetupPanel
Creates the setup panel with no initial experiment.
SetupPanel(Experiment). Constructor for class weka.gui.experiment.SetupPanel
Creates the setup panel with the supplied initial experiment.
setUpper(int). Method in class weka.core.Range
Sets the value of "last".
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.
setUseBetterEncoding(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether better encoding is to be used for MDL.
setUseIBk(boolean). Method in class weka.classifiers.DecisionTable
Sets whether IBk should be used instead of the majority class
setUseKernelEstimator(boolean). Method in class weka.classifiers.NaiveBayes
Sets if kernel estimator is to be used.
setUseKononenko(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether Kononenko's MDL criterion is to be used.
setUseLaplace(boolean). Method in class weka.classifiers.j48.J48
Set the value of useLaplace.
setUseMDL(boolean). Method in class weka.filters.DiscretizeFilter
Sets whether MDL will be used as the discretisation method.
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.
setUseResampling(boolean). Method in class weka.classifiers.AdaBoostM1
Set resampling mode
setUseResampling(boolean). Method in class weka.classifiers.LogitBoost
Set resampling mode
setUseTraining(boolean). Method in class weka.attributeSelection.ClassifierSubsetEval
Set if training data is to be used instead of hold out/test data
setUseUnsmoothed(boolean). Method in class weka.classifiers.m5.M5Prime
Set the value of UseUnsmoothed.
setValidationSetSize(int). Method in class weka.classifiers.neural.NeuralNetwork
This will set the size of the validation set.
setValidationThreshold(int). Method in class weka.classifiers.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.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 current object array.
setValueIndex(int). Method in class weka.filters.MakeIndicatorFilter
Sets index of the indicator value.
setValueIndices(String). Method in class weka.filters.MakeIndicatorFilter
Sets indices of the indicator values.
setValueIndicesArray(int[]). Method in class weka.filters.MakeIndicatorFilter
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
setVerbosity(int). Method in class weka.classifiers.m5.M5Prime
Set the value of Verbosity.
setWeight(double). Method in class weka.core.Instance
Sets the weight of an instance.
setWeightByConfidence(boolean). Method in class weka.classifiers.VFI
Set weighting by confidence
setWeightByDistance(boolean). Method in class weka.attributeSelection.ReliefFAttributeEval
Set the nearest neighbour weighting method
setWeightingKernel(int). Method in class weka.classifiers.LWR
Sets the kernel weighting method to use.
setWeightThreshold(int). Method in class weka.classifiers.AdaBoostM1
Set weight threshold
setWeightThreshold(int). Method in class weka.classifiers.LogitBoost
Set weight thresholding
setWindowSize(int). Method in class weka.classifiers.IBk
Sets the maximum number of instances allowed in the training pool.
setWorkingInstances(Instances). Method in class weka.gui.explorer.PreprocessPanel
Tells the panel to use a new working set of instances.
setWorkingInstancesFromFilters(). Method in class weka.gui.explorer.PreprocessPanel
Applies the current filters and attribute selection settings and sets the result as the working dataset.
setX(double). Method in class weka.classifiers.neural.NeuralConnection
setX(int). Method in class weka.gui.visualize.AttributePanel
shows which bar is the current x attribute.
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
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.neural.NeuralConnection
setY(int). Method in class weka.gui.visualize.AttributePanel
shows which bar is the current y attribute.
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, Instance). Method in class weka.classifiers.j48.Distribution
Shifts given instance from one bag to another one.
shiftRange(int, int, Instances, int, int). Method in class weka.classifiers.j48.Distribution
Shifts all instances in given range from one bag to another one.
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.
shrinkageTipText(). Method in class weka.classifiers.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.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.neural.SigmoidUnit
significanceLevelTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
significanceLevelTipText(). Method in class weka.attributeSelection.RaceSearch
Returns the tip text for this property
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
singleNodeToString(). Method in class weka.classifiers.m5.Node
Converts the information stored at this node to a string
singletons(Instances). Static method in class weka.associations.ItemSet
Converts the header info of the given set of instances into a set of item sets (singletons).
size(). Method in class weka.classifiers.CostMatrix
Gets the number of classes.
size(). Method in class weka.classifiers.evaluation.ConfusionMatrix
Gets the number of classes.
size(). Method in class weka.classifiers.kstar.KStarCache.CacheTable
Returns the number of keys in this hashtable.
size(). Method in class weka.classifiers.kstar.LightHashTable
Returns the number of keys in this hashtable.
size(). Method in class weka.core.FastVector
Returns the vector's current size.
size(). Method in class weka.core.Queue
Gets queue's size.
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.SMO.
Implements John C.
SMO(). Constructor for class weka.classifiers.SMO
smoothen(). Method in class weka.classifiers.m5.Node
Smoothens all unsmoothed formulae at the tree leaves under this node.
smoothenFormula(Node). Method in class weka.classifiers.m5.Node
Recursively smoothens the unsmoothed linear model at this node with the unsmoothed linear models at the nodes above this
smoothenValue(double, double, int, int). Static method in class weka.classifiers.m5.M5Utils
Returns the smoothed values according to the smoothing formula (np+kq)/(n+k)
smOrEq(double, double). Static method in class weka.core.Utils
Tests if a is smaller or equal to b.
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.
Sourcable interface weka.classifiers.Sourcable.
Interface for classifiers that can be converted to Java source.
sourceClass(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
sourceExpression(int, Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
sourceExpression(int, Instances). Method in class weka.classifiers.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.j48.BinC45Split
Returns a string containing java source code equivalent to the test made at this node.
sourceExpression(int, Instances). Method in class weka.classifiers.j48.C45Split
Returns a string containing java source code equivalent to the test made at this node.
sparseDataTipText(). Method in class weka.experiment.InstanceQuery
Returns the tip text for this property
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.
SparseToNonSparseFilter class weka.filters.SparseToNonSparseFilter.
A filter that converts all incoming sparse instances into non-sparse format.
SparseToNonSparseFilter(). Constructor for class weka.filters.SparseToNonSparseFilter
SpecialFunctions class weka.core.SpecialFunctions.
Class implementing some mathematical functions.
SpecialFunctions(). Constructor for class weka.core.SpecialFunctions
sphere. Variable in class weka.classifiers.kstar.KStarWrapper
used/reused to hold the sphere size
split(Instances). Method in class weka.classifiers.j48.ClassifierSplitModel
Splits the given set of instances into subsets.
split(Instances). Method in class weka.classifiers.m5.Node
Splits the node recursively, unless there are few instances or instances have similar values of the class attribute
SplitCriterion class weka.classifiers.j48.SplitCriterion.
Abstract class for computing splitting criteria with respect to distributions of class values.
SplitCriterion(). Constructor for class weka.classifiers.j48.SplitCriterion
splitCritValue(Distribution). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.GainRatioSplitCrit
This method is a straightforward implementation of the gain ratio criterion for the given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.EntropySplitCrit
Computes entropy for given distribution.
splitCritValue(Distribution). Method in class weka.classifiers.j48.InfoGainSplitCrit
This method is a straightforward implementation of the information gain criterion for the given distribution.
splitCritValue(Distribution, Distribution). Method in class weka.classifiers.j48.SplitCriterion
Computes result of splitting criterion for given training and test distributions.
splitCritValue(Distribution, Distribution). Method in class weka.classifiers.j48.EntropySplitCrit
Computes entropy of test distribution with respect to training distribution.
splitCritValue(Distribution, Distribution, Distribution). Method in class weka.classifiers.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.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.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
splitCritValue(Distribution, double, double). Method in class weka.classifiers.j48.GainRatioSplitCrit
This method computes the gain ratio in the same way C4.5 does.
splitCritValue(Distribution, double, double). Method in class weka.classifiers.j48.InfoGainSplitCrit
This method computes the information gain in the same way C4.5 does.
SplitDatasetFilter class weka.filters.SplitDatasetFilter.
This filter takes a dataset and outputs a subset of it.
SplitDatasetFilter(). Constructor for class weka.filters.SplitDatasetFilter
splitEnt(Distribution). Method in class weka.classifiers.j48.EntropyBasedSplitCrit
Computes entropy after splitting without considering the class values.
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
SplitInfo class weka.classifiers.m5.SplitInfo.
Stores split information.
SplitInfo(int, int, int). Constructor for class weka.classifiers.m5.SplitInfo
Constructs an object which contains the split information
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.adtree.Splitter.
Abstract class representing a splitter node in an alternating tree.
Splitter(). Constructor for class weka.classifiers.adtree.Splitter
SpreadSubsampleFilter class weka.filters.SpreadSubsampleFilter.
Produces a random subsample of a dataset.
SpreadSubsampleFilter(). Constructor for class weka.filters.SpreadSubsampleFilter
sqrSum(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the squared sum of the instances values of an attribute
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.Stacking.
Implements stacking.
Stacking(). Constructor for class weka.classifiers.Stacking
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.classifiers.j48.Stats.
Class implementing a statistical routine needed by J48.
Stats class weka.experiment.Stats.
A class to store simple statistics
Stats(). Constructor for class weka.classifiers.j48.Stats
Stats(). Constructor for class weka.experiment.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
stdDev(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the standard deviation value of the instances values of an attribute
STEP_FIELD_NAME. Static variable in class weka.experiment.LearningRateResultProducer
stepSizeTipText(). Method in class weka.experiment.LearningRateResultProducer
Returns the tip text for this property
store(double, double, double). Method in class weka.classifiers.kstar.KStarCache
Stores the specified values in the cahce table for easy retrieval.
stratify(int). Method in class weka.core.Instances
Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).
STRING. 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.
StringToNominalFilter class weka.filters.StringToNominalFilter.
Converts a string attribute (i.e.
StringToNominalFilter(). Constructor for class weka.filters.StringToNominalFilter
stringValue(Attribute). Method in class weka.core.Instance
Returns the value of a nominal (or string) attribute for the instance.
stringValue(int). Method in class weka.core.Instance
Returns the value of a nominal (or string) attribute for the instance.
studentTConfidenceInterval(int, double, double). Static method in class weka.core.Statistics
Computes absolute size of half of a student-t confidence interval for given degrees of freedom, probability, and observed value.
sub(int, Instance). Method in class weka.classifiers.j48.Distribution
Subtracts given instance from given bag.
SubsetEvaluator class weka.attributeSelection.SubsetEvaluator.
Abstract attribute subset evaluator.
SubsetEvaluator(). Constructor for class weka.attributeSelection.SubsetEvaluator
subtract(Distribution). Method in class weka.classifiers.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.
sum. Variable in class weka.experiment.Stats
The sum of values seen
sum(double[]). Static method in class weka.core.Utils
Computes the sum of the elements of an array of doubles.
sum(int[]). Static method in class weka.core.Utils
Computes the sum of the elements of an array of integers.
sum(int, Instances). Static method in class weka.classifiers.m5.M5Utils
Returns the sum of the instances values of an attribute
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
support(). Method in class weka.associations.ItemSet
Outputs the support for an item set.
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
Returns true because we do support a custom editor.
swap(int, int). Method in class weka.core.FastVector
Swaps two elements in the vector.
SwapAttributeValuesFilter class weka.filters.SwapAttributeValuesFilter.
Swaps two values of a nominal attribute.

Valid filter-specific options are:

-C col
Index of the attribute to be changed.

SwapAttributeValuesFilter(). Constructor for class weka.filters.SwapAttributeValuesFilter
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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