All Packages Class Hierarchy
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
S
- sampleSizeTipText().
Method in class weka.attributeSelection.ReliefFAttributeEval
- Returns the tip text for this property
- save(StringBuffer).
Method in class weka.gui.SaveBuffer
- Save a buffer
- SaveBuffer class weka.gui.SaveBuffer.
- This class handles the saving of StringBuffers to files.
- SaveBuffer(Logger, Component).
Constructor for class weka.gui.SaveBuffer
- Constructor
- saveInstanceDataTipText().
Method in class weka.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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